4(0.5,1) To cite: Prerna Juneja and Tanushree Mitra. 2022. Human and
technological infrastructures of fact-checking. In Proceedings of the 2022
Conference On Computer Supported Cooperative Work And Social Computing (CSCW
’22). Proceedings of the ACM on Human-Computer Interaction.

# Human and technological infrastructures of fact-checking

Prerna Juneja  University of WashingtonSeattleWashingtonUSA<EMAIL_ADDRESS>and  Tanushree Mitra  University of WashingtonSeattleWashingtonUSA
<EMAIL_ADDRESS>
(2022)

###### Abstract.

Increasing demands for fact-checking have led to a growing interest in
developing systems and tools to automate the fact-checking process. However,
such systems are limited in practice because their system design often does
not take into account how fact-checking is done in the real world and ignores
the insights and needs of various stakeholder groups core to the fact-checking
process. This paper unpacks the fact-checking process by revealing the
infrastructures—both human and technological—that support and shape fact-
checking work. We interviewed 26 participants belonging to 16 fact-checking
teams and organizations with representation from 4 continents. Through these
interviews, we describe the human infrastructure of fact-checking by
identifying and presenting, in-depth, the roles of six primary stakeholder
groups, 1) Editors, 2) External fact-checkers, 3) In-house fact-checkers, 4)
Investigators and researchers, 5) Social media managers, and 6) Advocators.
Our findings highlight that the fact-checking process is a collaborative
effort among various stakeholder groups and associated technological and
informational infrastructures. By rendering visibility to the infrastructures,
we reveal how fact-checking has evolved to include both short-term claims
centric and long-term advocacy centric fact-checking. Our work also identifies
key social and technical needs and challenges faced by each stakeholder group.
Based on our findings, we suggest that improving the quality of fact-checking
requires systematic changes in the civic, informational, and technological
contexts.

fact-checking, collaboration, misinformation

††copyright: acmcopyright††journalyear: 2022††doi:
nn.nnnn/nnnnnnn.nnnnnnn††conference: ; 2022; Virtual††ccs: Human-centered
computing Collaborative and social computing theory, concepts and
paradigms††ccs: Human-centered computing Computer supported cooperative work

##  1\. Introduction

> ?[There is] potentially an issue with.. the academic community as well, at
> least around fact checking.. There is loads of research done but I’m not
> sure [how] it makes an impact on how we do fact checking and how we report
> fact checking.? \- _Fact-checking advocator from Full Fact_ (Fact, 2021)

> ?Duke reporters lab runs _Tech & Check alerts_ (COOPERATIVE, 2021) I’d say
> maybe 10 times in the last few years has it helped us identify something to
> fact check. Lately I haven’t found it quite as useful for alerting me to
> something that is worth seeing.? \- _Fact-checking editor from The
> Washington Post_ (Post, 2021b)

With the increase in scale and diffusion of online misinformation, efforts to
develop scalable technological systems for fact-checking online information
have also increased. However, such systems are limited in practice (Graves,
2018), primarily for two reasons. First, their design is treated as a
technical solution to what is often seen as a purely technological problem.
But fact-checking is a complex socially-situated technical phenomena involving
collaboration among multiple stakeholder groups at various stages of the
process. Yet, current automated fact-checking systems rarely take into account
the insights and needs of “the human”—stakeholder groups who are central to
this process. Second, automated fact-checking systems are limited in their
applicability. For instance, most systems are either restricted to verifying
claims about very specific public statistics by matching them against official
figures (e.g., unemployment rate, inflation rate, etc.) or they are limited to
identifying simple declarative claims to debunk (Graves, 2018). Hence,
automated fact-checking solutions fail to generalize to real-world fact-
checking scenarios (Graves, 2018). In other words, the rigidity of a purely
technical system lacks the social flexibility necessary to support an inherent
socio-technical process.

In the last decade, HCI and CSCW communities have developed a better
understanding of the gap between the social and the technical (Girardin, 2007;
Dwyer, 2007; Ackerman, 2000; Tuffley, 2009) and thus, are well positioned to
develop an understanding of the socio-technical mechanisms underlying fact-
checking. Yet, till date, we know very little about how fact-checking is done
in practice and what we could do to socially and technically support the fact-
checking process. In this paper, we elucidate how fact-checking is practiced
by laying bare the human and technological infrastructures that facilitate and
shape the fact-checking process in a fact-checking team/organization. We
attempt to foreground the social by revealing the synergistic collaboration
that occurs among human infrastructure —various stakeholder groups that work
together to accomplish fact-checking work. We provide visibility to the
stakeholder groups’ roles, needs and activities, many of which often remain
invisible to the external world. We also highlight the technological
infrastructure —the tools, technology, processes and policies—that supports
and enables the work of the stakeholder groups. The foregrounding of the
infrastructures supporting the fact-checking work helps us unravel the
technical, policy and information barriers to fact-checking. Our hope is that
by considering both the human and technological infrastructures underlying the
fact-checking process, we might narrow the “design-reality gap” (Heeks,
2003)—the gap that exists between the needs of stakeholder groups involved in
fact-checking and design of technical systems for fact-checking. Overall, in
this work we answer the following research questions.

  * RQ1: What are the various infrastructures supporting fact-checking work?

    * RQ1a Human infrastructure: Who are the various stakeholder groups involved in the fact-checking process? What roles do they play? How do they evaluate priorities and collaborate together to make decisions?

    * RQ2b Technological infrastructure: How do tools, technology and policy support stakeholder groups in performing their roles?

  * RQ2 Barriers to fact-checking: What are the various needs and challenges of stakeholder groups involved in the fact-checking process?

To answer the research questions, we adopt a multi-stakeholder approach and
perform semi-structured interviews with 26 participants belonging to 16 fact-
checking organizations or fact-checking teams within publication houses. We
began our study by interviewing fact-checkers and editors—the stakeholder
groups identified in previous works (Sivek and Bloyd-Peshkin, 2019; Amazeen,
2013; Graves, 2017). We discovered the existence of other stakeholder groups
through these interviews and expanded our recruitment by reaching out to them
using convenience (Etikan et al., 2016) and snowball sampling (Goodman, 1961).
Our participants had diverse representations from 4 continents—North America,
Europe, Asia and Africa. We intentionally sampled participants widely across
fact-checking teams, organizations, and countries to capture the practices and
challenges emerging in this space. Our work aims at uncovering the possible
human and technological infrastructures supporting the fact-checking work in
teams/organizations across the regions instead of capturing the variability of
fact-checking process across regions.

Our findings reveal the existence of six distinct stakeholder groups involved
in the fact-checking process and the various roles performed by them. The
identified stakeholder groups are: (1) Editors who are responsible for
overseeing the fact-checking process, including planning what topics to target
and ensuring the integrity of the fact-checks produced, (2) External fact-
checkers who are responsible for monitoring the external world (social media
platforms, presidential speeches, etc.), investigating dubious claims and
writing fact-checks, (3) In-house fact-checkers who are responsible for fixing
incorrect claims present in the news stories or articles produced internally
in the media/news publication house, (4) Investigators and researchers who
conduct in-depth investigation and data analysis of persistently circulating
disinformation campaigns (e.g. investigating coordinated campaigns that used
anti-Ruto hashtags111William Ruto is the current Deputy President of the
Republic of Kenya. In May 2020, several anti-Ruto hashtags (e.g. #RutoMustGo,
#RutoWantedToKillUhuru, etc.) began trending on Twitter in an attempt to
discredit the Deputy President. on Twitter to spread
misinformation222https://investigate.africa/opt-report-post/), (5) Social
media managers who distribute fact-checks across multiple social media
platforms and strategize on ways to increase audience engagement with the
fact-checks, and (6) Advocators who spearhead initiatives to improve policies
around availability of information and statistics in their countries to
improve the quality of fact-checking.

By studying the roles performed by the stakeholder groups (_human
infrastructure_), we establish how fact-checking has evolved from a process to
debunk individual pieces of misinformation (short-term claims centric fact-
checking) to a multi-step long-term campaign involving research, policy, and
advocacy work (long-term advocacy centric fact-checking). We find that
stakeholder groups mediate their roles via different tools (_technological
infrastructure_) ranging from third party social media monitoring tools (e.g.
BuzzSumo (BuzzSumo, 2021)), public databases, process management tools (e.g.
Trello (Trello, 2021)), color coding schemes, to training and educational
workshops. Our interviews reveal that fact-checkers are skeptical of using
fully automated AI based tools. They desire algorithm explainability and
involvement of humans in the decision making process as key values in the
systems they would use. We also identify several technical, policy and
informational challenges. For example, there are limited tools to monitor and
flag content on private messaging platforms and investigate false claims in
videos and content in local regional languages. We also find that in some
countries, information to investigate claims from government and civic bodies
is either unavailable, difficult to obtain, or is not updated periodically.

Overall, our work makes the following contributions to the CSCW/HCI community:

  * •

Our study identifies the human infrastructure supporting the fact-checking
process. We present the six key stakeholder groups involved in fact-checking
and the roles performed by them. Through their roles, we show the evolvement
of fact-checking process from short-term claims centric to long-term advocacy
centric work.

  * •

We reveal the technological infrastructure that supports and facilitates the
role of each of the stakeholder groups identified in the study.

  * •

We offer a comprehensive understanding of complexities and peculiarities of
the current practices of fact-checking and provide a holistic view of the
collaborative effort among various stakeholder groups, technological
infrastructure and the social-informational context in which the fact-checking
takes place.

  * •

We present the technical, policy and informational challenges that stakeholder
groups face while performing their roles and state the implications of our
study on the design of new tools and systems for the fact-checking process. We
also highlight how in addition to the need for developing new tools and
improving current platform design and affordances, there is an exigency to
improve the availability and quality of data and statistics globally.

##  2\. Related Work

We start by presenting the definition, origin and evolution of fact-checking
(Section 2.1). Next, we situate our analysis of the fact-checking process with
the existing literature on human-technological infrastructure (Section 2.2)
and invisible work (Section 2.3). Finally, we present the current landscape of
computational research in fact-checking . We show how previous work engages
with the shortcoming of the fact-checking practices and tools in a limited
manner and describe how our work addresses this gap (Section 2.4).

###  2.1. Fact-checking: Definition, Origin, and Evolution

The American Press Institute defines the process of fact-checking as “re-
reporting and researching the purported facts in published/recorded statements
made by politicians and anyone whose words impact others’ lives and
livelihoods.” (Elizabeth, 2014). One of the early examples of fact-checking
emerging as an integral part of journalism was when the Time magazine set up a
separate research department to objectively verify every printed word before
releasing the publication, a phenomenon now known as ante hoc, internal, or
in-house fact-checking (Dickey, 2019). The last decade also witnessed the
emergence of post-hoc or external fact-checking which consists of publishing
an evidence based analysis of claims made in any public text (e.g., news
report, political speech, social media posts, etc.) after it is released to
the world (Graves and Amazeen, 2019). Today, fact-checking has emerged both as
a principal part of news reporting as well as a separate entity (van der
Meulen and Reijnierse, 2020). According to Duke Reporters’ Lab, by 2019 there
were around 188 active fact-checking initiatives spread across 60 countries
(Stencel, 2019). These initiatives have incorporated a range of methodologies
and data-driven journalistic practices to not only hold disinformation
spreading individuals and organizations accountable through their fact-checks,
but to also disseminate fact-checks in such a way that increases engagement
with the public (Cheruiyot and Ferrer-Conill, 2018; Ballotpedia, 2021). With
the aim of bringing together these fact-checking initiatives and in order to
promote common fact-checking standards through a code of principles, the
International Fact-checking Network (IFCN) was established in 2015 (Poynter,
2021). Major social media companies such as Facebook and Google have since
then partnered with IFCN signatories to debunk false claims surfacing on their
platforms (Graves and Anderson, 2020). While existing work describes the
evolution of fact-checking from journalism to external fact-checking (Graves,
2013; Graves and Glaisyer, 2012), there are still gaps in understanding how
fact-checking is actually practiced, the identity and role of the
participating stakeholders and the various collaborations and partnerships
occurring in the process. Our work deep dives into answering these missing
aspects of the fact-checking phenomenon.

###  2.2. Human and Technological Infrastructures

The foundational work of studying infrastructure as a subject could be
credited to Star and Ruhleder (Star and Ruhleder, 1996). The authors consider
infrastructure as “something that emerges for people in practice, connected to
activities and structures” (Star and Ruhleder, 1996). Rather than being a
thing to use, Star and Ruhleder refer to infrastructure as a relational
concept (Star and Ruhleder, 1996). Scholars have since advocated for
broadening the understanding of infrastructure by also including social
practices, processes and flow of information (Sambasivan and Smyth, 2010) and
have called for investigating the complexities and particularities of
infrastructures in practice (Cass et al., 2018; Verdezoto et al., 2021;
Karasti and Blomberg, 2018). In response to this call, several research
studies in Computer Supported Cooperative Work (CSCW) and related fields, such
as Human Computer Interaction (HCI) have examined the infrastructures—both
human and technological—supporting the diverse socio-technical systems (Dye et
al., 2018; Jack et al., 2017; Nguyen, 2016; Lee et al., 2006) in various
contexts such as, in health-care (Pendse et al., 2020; Tang et al., 2015;
Ferguson, 2005), e-governance (Chaudhuri, 2019), crisis situations (Mark et
al., 2009), etc. For our study, we first focus on human infrastructure which
Lee et al define as “organizations and actors that must be brought into
alignment in order for work to be accomplished” (Lee et al., 2006). Scholars
have used this concept of human infrastructure to denote the human
partnerships that are necessary for a successful socio-technical system
(Sambasivan and Smyth, 2010). Drawing on such scholarly work (Dye et al.,
2018; Lee et al., 2006; Sambasivan and Smyth, 2010), we use the analytical
lens of human infrastructure to “magnify the social” by rendering visibility
to the stakeholder groups who collaborate to enable the fact-checking work.
Highlighting the human infrastructure also allows us to focus on how
collaboration and coordination is accomplished in socio-technical systems (Lee
et al., 2006). Sustaining online collaboration among various groups in an
organization can be challenging (Morgan et al., 2013). Thus, within CSCW, a
lot of attention has also been paid on examining collaborative (Jirotka et
al., 2006; Lampinen et al., 2016; Lee et al., 2006) and coordination efforts
(Morgan et al., 2013; Lundberg and Tellioğlu, 1999; Grinter, 1995; Wasson,
1998) in socio technical systems, determining ways to foster collaboration and
coordination (Gao et al., 2011; Rummel et al., 2002; Mota et al., 2011) and
designing cooperative work tools (He and Han, 2006; H. Carstensen, 1995; Fish
et al., 1988; Karp and Pardo, 2017). We complement these prior studies on by
establishing fact-checking as a distributed problem that requires
collaboration and coordination of the human infrastructure supporting the
fact-checking process.

In a socio-technical system, human infrastructure does not exist in a vacuum
(Tang et al., 2015). It is intertwined with the technological infrastructure
which is the software, hardware and processes supporting the human actors in
performing their roles (Tang et al., 2015; Rasmussen, 2007). Scholars argue
that technology and human actors are mutually constituting; one mediates the
other (Tang et al., 2015; Robinson et al., 2015). Our work also borrows the
concept of technological infrastructure to shed light on how use of various
tools facilitates the enactment of the stakeholder groups’ roles in the fact-
checking process.

###  2.3. Invisible Work of Fact-checking

Within CSCW, a lot of attention has also been paid on highlighting the
invisible or the overlooked work in a process or within an organization
(Sawyer and Tapia, 2006; Dye et al., 2018; Abraham and Reddy, 2013; Ferreira
et al., 2011). Invisible work can include situations where person performing
the work is visible but some of the work they perform is “functionally
invisible or taken for granted” (Stisen et al., 2016). Such work remains
hidden in the background but is essential for collective functioning of a
workplace (Nardi and Engeström, 1999). It often includes informal work
practices such as informal conversations, operational and maintenance work,
etc (Nardi and Engeström, 1999; Verdezoto et al., 2021; Unruh and Pratt,
2008). There are also situations where the person performing the work itself
is invisible, such as, service, design or domestic work (Star and Strauss,
1999; Markussen, 1996). In certain complex environments (e.g. hospitals), both
visible and invisible work practices can take place simultaneously (Stisen et
al., 2016). In similar vein, fact-checking is a complex ecosystem that
includes somewhat _visible_ editorial and investigative work and _invisible_
advocacy, policy and research work. Most of the prior research has looked at
fact-checking as a process to debunk misinformative claims (short-term claims
centric fact-checking) (Sivek and Bloyd-Peshkin, 2019; Amazeen, 2013; Graves
and Amazeen, 2019; Graves, 2017). Scholars have mostly engaged with the role
of stakeholder groups such as fact-checkers and editors in supporting the
fact-checking work (Sivek and Bloyd-Peshkin, 2019; Graves and Cherubini, 2016;
Amazeen, 2013). We add on to the existing literature by not only expanding on
the previously reported roles of fact-checkers and editors but also
identifying other stakeholder groups, such as investigators and researchers,
and advocators whose roles remain invisible and unexplored by prior research.
We shed light on the invisible work that fact-checking organizations are doing
to improve the availability and quality of information in their country (long-
term advocacy centric fact-checking).

###  2.4. Current Landscape of Computational Research in Fact-checking

Past quantitative and computational research studies on fact-checking have
primarily focused on automating multiple stages of the fact-checking process
(Hassan et al., 2017; Shao et al., 2016; Ghenai and Mejova, 2017; Cerone et
al., 2020; Kartal et al., 2020; Bondielli and Marcelloni, 2019), determining
the perception and believability of fact checks (Fridkin et al., 2015;
Agadjanian et al., 2019; Oeldorf-Hirsch et al., 2020) and construction of
fact-check databases (Wang, 2017; Thorne et al., 2018; Kotonya and Toni,
2020). Scholars have adopted several approaches to determine the veracity of
content, such as use of knowledge graphs (Shiralkar et al., 2017), crowd-
sourcing (Hassan et al., 2019; Kim et al., 2018), deep learning models
(Karadzhov et al., 2017), natural language processing techniques coupled with
supervised learning techniques (Hassan et al., 2017) and combination of human
knowledge and AI (Nguyen et al., 2018). Work in the field of multimedia
forensics has also led to the development of content verification tools
especially for image and video verification such as Tineye, InVID etc.
(Teyssou et al., 2017). Despite the plethora of automated systems and tools
available for fact-checking, our understanding of their usefulness in practise
is limited. Furthermore, there is a dearth of scholarly work that engages with
the limitations of the current fact-checking tools and practices ((Graves,
2018; Dias and Sippitt, 2020) are a few exceptions). Our paper addresses this
gap by interviewing the various stakeholder groups involved in the fact-
checking process to understand the technological infrastructure supporting
their work including the tools that are actually used by the stakeholders in
practice, limitations of the current tools and the challenges faced by
multiple stakeholder groups.

The remainder of our paper is organized as follows. We begin by describing our
study method and interview protocol in Section 3. Then, we present a brief
overview of short-term claims centric and long-term advocacy centric fact-
checking in Section 4. Next, we answer RQ1 by describing in detail the human
and technological infrastructures supporting both types of fact-checking in
Section 5 and Section 6. We answer RQ2 by identifying the needs and challenges
faced by the stakeholder groups in Section 7. Finally, we discuss the
implications of this study in Section 8.

##  3\. Method

To better understand how fact-checking is practised in real-world, we
conducted semi-structured interviews with six stakeholder groups (N=26): (1)
Editors (2) External fact-checkers (3) In-house fact-checkers (4)
Investigators and researchers (5) Social media managers, and (6) Advocators.
All interviews were conducted with the approval of the Institutional Review
Board. We started by interviewing fact-checkers and editors employed in fact-
checking organizations and publication houses. The qualitative analysis of the
initial interviews (with P3, P4 and P5) as well as conversations with our
contacts in the fact-checking organizations during the initial recruitment
gave us a new perspective on fact-checking revealing the complex workflows
that include several other stakeholder groups (apart from editors and fact-
checkers) who work together to achieve the end goal of fact-checking. We then
expanded our recruitment to interview people belonging to these other
stakeholder groups.

###  3.1. Participant Sampling Technique

We adopted convenience (Etikan et al., 2016) and snowball sampling (Goodman,
1961) to recruit our subjects. First, we employed convenience sampling to
identify fact-checkers via Twitter search. We sent out personal recruitment
messages to those Twitter users whose Twitter bio revealed them to be fact-
checkers and whose accounts allowed direct messaging. The second author had
established collaboration with a few fact-checking organizations. We also
reached out to individuals working in these organizations. Next, we used
snowball sampling for recruitment. We requested the individuals who
participated in our study to further connect us with other individuals
belonging to different stakeholder groups in their or other organizations. We
interviewed a total 26 participants from 16 fact-checking teams/organizations
including a Pulitzer Prize winning editor and journalist. Tables 1 and 2 list
our participants’ demographics, experience in current role, stakeholder groups
that we studied, participating organizations and the continents we covered by
interviewing participants based in those continents. Note that the names of
the roles of various stakeholder groups were not always self-reported (with an
exception of fact-checkers, news editors and copy editors), but were rather
found during qualitative analysis. Therefore, these roles might not match with
participants’ designation in the fact-checking team or organization. For
example, the professional designations of two participants performing advocacy
work in their respective fact-checking organizations were Head of Public
Policy & Institutional Development and Partnerships manager. Several of our
participants’ roles were fluid and overlapping, i.e in some fact-checking
teams/organizations, at times a single person enacted several roles. For
example, participant P12 performs both editorial and advocacy work. In some
cases, a participant provided us with insights about more than one role since
they either led or managed the entire fact-checking team or work(ed) closely
with the other stakeholder groups and thus, were aware of the various roles
involved in the fact-checking pipeline. For example, we interviewed an
advocator working at Meedan, an organization that provides institutional and
programmatic support to partner organizations doing fact checking work. Their
job at Meedan allows them to work closely with people performing various roles
at the fact-checking organizations. Thus, the participant was able to describe
the responsibilities and tasks conducted by various stakeholder groups such as
fact-checkers, advocators and investigators.

P# | Gender | Exp.(yrs) | P# | Gender | Exp.(yrs) | P# | Gender | Exp.(yrs) | P# | Gender | Exp.(yrs)
---|---|---|---|---|---|---|---|---|---|---|---
P1 | Female | 0.67 | P8 | Male | 2 | P15 | Female | 2 | P22 | Male | 3
P2 | Male | 0.21 | P9 | Female | 1 | P16 | Female | 0.5 | P23 | Male | 1
P3 | Female | 2.5 | P10 | Male | 0.42 | P17 | Male | 0.83 | P24 | Male | 15
P4 | Male | 2.25 | P11 | Female | 2 | P18 | Male | 10 | P25 | Female | 17
P5 | Male | 3 | P12 | Male | 4 | P19 | Male | 4 | P26 | Female | 1
P6 | Female | 0.75 | P13 | Female | 2.5 | P20 | Female | 7 |  |  |
P7 | Female | 0.92 | P14 | Male | 0.5 | P21 | Male | 0.75 |  |  |
Table 1. Table showing list of participants with their gender and experience (in years) in their current role. Some participants have been associated with fact-checking work for a longer duration. We only report their experience in the current role in the organization. Stakeholder group |  Organization |  Continent
---|---|---
1) News desk editors and copy editors 2) External fact-checkers 3) Social media managers 4) Investigators and researchers 5) Advocators 6) In house fact-checkers |  Pesacheck (Pesacheck, 2021), Meedan (Meedan, 2021), First Check (Leads, 2021), Full Fact (Fact, 2021), The African Network of Centers for Investigative Reporting’s Investigative Lab (fnc, 2021), AFP (AFP, 2021), Africa Check (Check, 2021), The New Republic (Republic, 2021), The Quint (Quint, 2021), Al Jazeera (Jazeera, 2021), The Washington Post (Post, 2021b), DPA (dpa, 2021), Maldita (ort, 2021), India Today (Fac, 2021), Der Spiegel (DER, 2021), Fine Tip Research & Editing, Freelance |  Africa, Asia, Europe, North America
Table 2. Table showing the stakeholder groups identified in the study, the
participating organizations and the continents we covered through the
interviews. In the organization column, freelance refers to no association
with a particular fact-checking organization/team. We aggregated the roles of
stakeholders and their association with fact-checking organization/team to
ensure anonymity as in some cases knowledge of network affiliation and role
could potentially reveal the identities of few participants. Note that the
participants that we interviewed sometimes provided insights about more than
one role.

###  3.2. Interview Protocol and Data Analysis

All interviews were conducted between November 2020 to September, 2021. We
designed a generic semi-structured interview script for our study that
contained a set of broad questions about participant’s and their
organization’s role. Based on participants’ responses to these questions, we
inquired them about the specific details of their roles. Thus, recruiting and
interviewing different stakeholder groups did not require us to file any
changes with our university’s Institutional Review Board. We first asked
participants to describe their role within their organization, and the
function of the organization itself. We encouraged participants to share their
screen and describe various aspects of their work using real-world examples.
We also asked the participants to demonstrate the tools they use wherever
applicable. We probed them about the role of technology in their day-to-day
work and how the affordances provided by online platforms facilitates or
impede their work. To get insights about how fact-checking is practised in the
participant’s team/organization, we asked them to describe all the steps
involved in the fact-checking pipeline. We inquired about the other
stakeholder groups working in their team/organization who also contribute
towards the fact-checking process and how these various groups collaborate
with each other. We also discussed various challenges participants face in
their job. The interviews lasted between 60 to 125 minutes and averaged over
90 minutes. All interviews were recorded via Zoom or Google Meet. The first
author transcribed all the video and audio recordings. Then, two authors
independently went through the transcripts and observation notes taken during
the interviews and thematically analyzed them using a mixture of deductive and
inductive coding scheme (Braun and Clarke, 2006). First, both authors
conducted a deductive scan where the transcripts were coded for categories:
fact-checking process, stakeholder groups, participant’s role, decision
making, use of tools, collaboration within stakeholder groups and challenges
faced by the participant in performing their role. Then within each deductive
code, inductive coding was conducted. Both authors read the transcripts
multiple times to determine the codes. Then, the two authors compared and
contrasted their codes with each other to refine the codes and resolve the
inconsistencies. After several rounds of discussions, both authors converged
on a final set of themes. We present these themes with respect to the research
questions in the following sections.

Figure 1. Figure presenting the ecosystem of fact-checking, the whole or part
of which could exist in a fact-checking organization or a news publication
house.  indicates the two types of fact-checking (short-term claims centric
and long-term advocacy centric fact-checking) introduced in the study,
presents the stakeholder groups involved in the fact-checking process (human
infrastructure),  shows work done by the stakeholder groups as part of their
role, and  specifies the tools stakeholders use to mediate their roles
(technological infrastructure). The numbers indicate the sequence in which
various roles are performed.

##  4\. Types of Fact-checking: Short-term Claims and Long-term Advocacy

Our study aims to identify the infrastructures—both human and
technological—supporting the fact-checking work. We identify and present the
human infrastructure by elucidating the role of six stakeholder groups that
need to come in alignment to accomplish fact-checking. The six stakeholder
groups are editors (news desk and copy editors), external fact-checkers, in-
house fact-checkers, social media managers, investigators and researchers, and
advocators. We show how these stakeholder groups’ roles are supported by
technological infrastructure. Through the study of the fact-checking
infrastructures, we establish that fact-checking exists as both short-term
claims centric and long-term advocacy centric fact-checking. In this section,
we first provide an overview of the two types of fact-checking before deep
diving into the infrastructures supporting them in Section 5 and Section 6.
Figure 1 provides an overview of the fact-checking ecosystem including the
stakeholder groups, their roles and various tools that support them in
performing their roles.

###  4.1. Short-term Claims Centric Fact-checking

Short-term claims centric fact-checking aims at informing the public by
debunking misleading claims circulating on the online platforms. It begins
with fact-checkers continuously monitoring the online spaces for potentially
misleading content. They identify the exact claim(s) that they want to fact-
check and make a pitch to the editorial team about how they plan to debunk
that claim (Section 5.3). After gaining editor’s approval (Section 5.1), they
archive the content, find the source of the claim, and investigate the claim
by using online tools, consulting experts and employing authoritative publicly
available evidence (e,g, statistics on unemployment, census data, etc.). Based
on their investigation, fact-checkers assign a label indicating the veracity
of the claim and then write a report declaring all the sources they gathered
(Section 5.3). The written report goes through a rigorous copy editing
pipeline to ensure integrity of the fact-check (Section 5.2). If the claim is
false, fact-checking organizations reach out to person/organization who made
the claim for correction(s) (Section 5.3). Finally, social media engagement
team publishes the fact-check story/document on the social media pages of the
organization and adopts several strategies to increase audience’s engagement
with the published fact-check (Section 5.5). Our work also examines the role
of in-house fact-checkers doing short-term claims centric fact-checking in
news and media publication houses. In house fact-checkers assist
reporters/journalists in verifying and validating their articles by ensuring
that the facts and quotes present in the article are correct and backed by
authoritative sources (Section 5.4).

###  4.2. Long-term Advocacy Centric Fact-checking

> ?Fact checking is more than publishing fact checks. In order to change the
> information ecosystem.., we need to spot patterns and try and do something
> about those patterns.. We try to influence policymakers, information
> producers and media to raise their standards and improve the quality of
> information and public debate. [Our role is] more akin to a campaigning
> organization. ? \- P9

We find that the work of the fact-checking organizations is more than a one-
off engagement with misleading claims and fact-checks (short-term claims
centric fact-checking). Most organizations also perform long-term advocacy
centric fact-checking. They run several investigative projects to study the
misinformation ecosystem in their country (Section 6.1). They are also
actively involved in advocacy and policy work where they try to influence
civic bodies and policy makers to improve the quality of data, organize
workshops to train organizations and journalists to do fact-checking and work
towards forming coalitions among various fact-checking organizations and
internet companies (Section 6.2). To demystify the fact-checking process, in
the following sections, we present in detail the roles performed by the
stakeholder groups involved in both types of fact-checking along with the
collaborations occurring in process. For each stakeholder group, we also
discuss how technological infrastructure supports the enactment of their
roles.

##  5\. Infrastructures Supporting Short-term Claims Centric Fact-checks

Short-term claims centric fact-checking is supported by five stakeholder
groups—news desk editors, copy editors, external and in-house fact-checkers,
and social media managers. We present the roles played, activities performed,
decisions made, and tools used by them.

###  5.1. News Desk Editors—Approving Claims and Guiding Fact-checkers

News desk editors are one of the most critical stakeholder groups supporting
the short-term claims centric fact-checking. They decide what their
team/organization is going to fact check. They approve or reject the claims
pitched by the fact-checkers and guide them in their work.

####  5.1.1. Approving claims to fact-check

News desk editors are looking for newsworthy claims that impact a lot of
people. First criteria of approving a claim is the popularity and reach of the
person making the claim since it increases the chance of the claim spreading
far and wide (“it should be newsworthy, said by an important person,… if a
popular public figure makes a claim, a lot of people are likely to be exposed
to that claim, given the the bully pulpit that public figures have”—P18).
Second criteria includes number of people likely to be misled based on the
context surrounding the claim. For example, a claim touching upon a communal
angle is likely to impact a lot of people in a country that has “many
illiterate people..who process information based on their communal experience
(P12)”. Third, news editors approve content that is gaining a lot of traction
on social media platforms by accumulating engagement in the form of likes,
comments and shares. Content that has received less attention from the public
is not fact-checked from the fear of amplifying the false information by
inadvertently bringing attention to the false claims (“Are people believing
this or are they taking it just as a joke? Does it just have one share,
meaning if I fact check it, it will just amplify the fake news and not really
give the correct information”—P11). Fourth, opinion pieces and claims that
cannot be verified using sources are not fact-checked (“we check things that
have facts, that can be verified using records and information. We don’t want
a situation where we see this person says this and this person says this, that
will just be hearsay”—P3). Fifth, fact-checkers and news desk editors consider
several stages of harm that false information is likely to cause—physical,
mental, social or emotional and prioritize the claims that are likely going to
cause maximum damage to the public by affecting their health and well being.

> ?We have stages of harm that you have to check. Is it causing physical harm?
> Is it causing mental harm? Is it causing someone to lose social standing?
> How much effect will it have to the person.. if I don’t fact-check the
> story? .. We do the ones that actually have greater harm, we give them
> priority.. We have to get it out before most people see it to reduce the
> harm that it’s causing.? \- P3

####  5.1.2. Guiding fact-checkers

The job of news editors does not end after approving a claim. They also guide
and help fact-checkers in “gathering sources and evidence (P11)” for verifying
stories, “understanding concepts, finding best available data [for
research],..and connecting with the experts given [editors’] long experience
in the media ecosystem (P12)”.

###  5.2. Copy Editors—Ensuring Quality of the Fact-checks

Copy editors do quality control of the fact-check story/document through
multiple iterative copy editing cycles. A fact-check story or document is a
report written by fact-checkers containing the claim investigated, sources
used for investigation, and verdict indicating veracity of the claim. This
stakeholder group acts like the first readers who determine whether fact-
checkers have accurately interpreted the claim in the fact-check, used
multiple primary sources as evidence for investigation, provided working links
to the sources used for investigation and presented the evidence in such a way
that it leads to a logical and correct conclusion. They also work towards
making the fact-checks engaging by checking the phrasing and grammatical
errors. We discuss the tasks performed by copy editors in detail below.

####  5.2.1. Performing copy editing cycles

Through our interviews we realized that a fact-check goes through two to three
copy editing stages. Each stage is supervised by a different copy editor to
ensure higher quality. The written fact-check is provided to the copy editor
in a shared document (e.g. Google doc) where they leave comments to provide
feedback. At the first copy edit stage, copy editors check the central premise
of the fact-check.

> ?When I get a fact-check, I read through it three times to understand it
> before I make any change on it, before I ask any questions. I look for the
> claim and the debunk. Does it really hold? If there are questions about the
> debunk then we send it back to the news desk.. At that stage, the fact
> checker will pick it up and go back and try to sort out any queries that
> have been raised.? \- P1

The second copy-edit stage focuses on refining the language and flow of the
fact-check. The final copy-edit stage focuses on making the fact-check more
engaging and interesting to read. We discuss this aspect briefly.

####  5.2.2. Making fact-checks more engaging

Copy editors try to keep the fact-check short, clear, crisp and interesting.
They ensure that the fact-checks are written in a language that is understood
by laymen. P1 spoke most candidly about the engagement aspect in the editorial
process. They revealed that they often collaborate with social media managers
to get feedback on the engagement aspect of the fact-checks. For example,
ensuring that the country relevant to the fact-check is present “in the title
or in the blurb (P1)” so that people could quickly determine if its of
interest to them, or making certain that the verdict on the claim is placed
high up in the fact-check to get more attention from the public. Copy editors
along with social media managers also suggest addition of info-graphics
(engaging visuals, imagery, tables, charts, etc.) to fact-checks in order to
attract the eyes of the readers. The info-graphics are added to quickly
communicate “complex information in a visual manner (P2)”. They are usually
added in long-form fact-checks—the ones that debunk multiple claims and delve
into the subject of the claim in greater depths.

Copy editors also ensure that the fact-check contains terms that people are
most likely to search online. P1, P9, P12 and P18 talked about claim-review
schema used by fact-checking organizations to allow internet companies like
Google to index their fact-checks (FullFact, 2021). These participants believe
that using popular search terms in fact-check helps increase its visibility
since search engines then rank it higher in the search results. We briefly
touch upon this collaboration between fact-checking organizations and internet
companies later in Section 6.2.

####  5.2.3. Use of collaborative systems, labelling and color coding schemes

In most of the organizations that we interviewed, both news desk and copy
editors use Google docs to comment and provide feedback on the fact-checks. In
addition, fact-checking organizations like Pesacheck, Africa Check, etc. also
use an open-source project management and collaboration tool called Trello
(Trello, 2021). The tool provides a dashboard with a series of columns that
contain cards. The columns are named so that they denote the current stage of
the fact-check. For example, P2 showed the Trello board of their organization
which had several stages such as complete fact checks, copy edit 1, copy edit
2, copy edit 3, Q/A & final review, final review done, published live, and
amplification done. Trello cards are the basic functioning unit in the tool.
The cards hold various information about the fact-checks including due dates,
conversations, attachments etc. The tool allows editors to add colors and
labels to the cards. Copy editors use labels and color coding schemes for
different purposes. For example, P2 uses labels for prioritizing the fact
checks (“Earlier this year there was a lot of interest in COVID, so I would
label fact-check as COVID because we were prioritizing those). P10 uses colors
to indicate tasks allocated to people (“It’s easier for me to see what people
are working on.. If its research, it will be purple, if its documentation, it
will be green.).”

###  5.3. External Fact-checkers—Monitoring, Investigating and Publishing
Fact-checks

External fact-checkers are the most evident stakeholder group supporting the
fact-checking process. Their role is to continuously monitor the external
world for potentially false claims, investigate them for veracity, assign the
verdict and publish the fact-check. We discuss these roles below. We also
specify the technological infrastructures supporting the roles along with the
description of the roles.

####  5.3.1. Monitoring online spaces

Monitoring content is one of the most tedious steps in the fact-checking
workflow. Fact checkers monitor content reactively (in response to user tips),
preemptively (before events like elections, presidential speeches, etc.) and
in real time (tracking current affairs via trends and listening to
conversations in real time). They monitor the content via several tools and
artifacts. First, they rely on user reports and tiplines which are useful ways
to access misleading content circulating in private groups and WhatsApp that
are otherwise hard to access.

> ?We have our WhatsApp tipline and emails.. Public who wants to verify a
> particular piece of information send it on WhatsApp to us.. We verify those
> queries at our end and then send them the replies with the fact check story
> if possible. If we don’t have fact check story, then we send people whatever
> information we have on the query. ? \- P14

Second, fact-checkers create watch lists and track social media accounts,
groups, pages and websites of repeat offenders–those who posted misinformative
content multiple times in the past (“We’ve a database where we’ve tracked all
accounts spreading misinformation.. We go back and check these accounts, see
what they’ve posted on their website and personal account.”—P3). Third, fact-
checkers rely on manual searches. They follow the current events via news or
Twitter trends to get updated about topics that people are talking about and
track them on all online platforms. Creation of relevant search queries to
track these topics is mostly a tedious “hit-n-trial (P7)” method. Searching
for a query can give millions of results on search platforms. Therefore, fact-
checkers rely on search query syntax to reduce the number of search results.

> ?I do not want the news content. I want user generated content. So one of
> the simplest tools is to write minus news (search_query -news) so that it
> cancels out the major news content.? \- P7

> ?I use keywords like “intitle” [on Google search]. [intitle:coronajihad
> site:facebook.com].. is showing me every search term, every post on
> Facebook, which has the title “coronajihad”.? \- P8

Fourth, fact-checkers use several tools to track content on the internet. For
example, they use CrowdTangle (CrowdTanle, 2021) to track Facebook’s public
pages and groups. Organizations that have partnered with Facebook have access
to a Facebook proprietary tool colloquially known as the “Facebook Queue”. The
tool aggregates potentially misleading content that is accumulating engagement
on the platform. In some of the organizations, fact-checkers also rely on
several third party tools (e.g. Social searcher (Searcher, 2021), Influencer
(Influencer, 2021), BuzzSumo (BuzzSumo, 2021) etc.) to search and filter
content on platforms since they provide them varied search filter options that
the original interface of the social media platform lacks. For example,
Facebook search allows to filter content by year and not by months and dates.
Lastly, fact-checkers also rely on a network of stringers—“reporters who work
for a publication or news agency on a part-time basis” (Webster, 2022)—who
inform them about the misinformation “circulating in their region..[and]
language (P11).”

####  5.3.2. Making a decision to fact-check and extracting claim(s)

Once fact-checkers have identified potentially false content on the internet,
they identify claim(s) in the content to verify. The fact-checkers can decide
to do a short-form or a long-form fact-check depending on the number of claims
they identify in the content. For their partnership with Facebook, the fact-
checkers only do short-form fact-checks where they identify one claim from the
body of the content. In long form fact-checks, fact-checkers will debunk
multiple claims present in the content.

> ?The reason we do [short-forms] is to make sure we are clear, we’re not
> confusing the audience. This is a largely inline with the Facebook
> partnership where we mainly focus on one claim.? \- P2

Next, fact-checkers prepare a pitch to convince news desk editors why the
content needs to be fact-checked along with a plan of how they would
accumulate proofs to debunk the claim. Once the claim is approved, they
archive the content using online websites like archiveis (Archive.today,
2021), wayback machine (Archive, 2021), etc. Many times people and
organizations delete the false claims made by them once they are fact-checked.
Thus, archiving becomes essential to prove that the content existed.

> ?Once you identify the claim you archive it because what purveyors of
> misinformation do mostly, they delete it. So once they delete it you’re
> unable to read that particular claim.? \- P11

After archiving, fact-checkers find the original source of the claim–“who
shared [the claim], context with which it (the claim) was originally shared
(P14)”, without which the fact-checker would not have a complete picture of
the context in which the content was originally shared.

####  5.3.3. Researching

Once a claim is identified, fact-checkers collect multiple primary sources to
prove or disprove the claim. They use three ways of gathering sources. First,
they use quotes by experts such as doctors, physicians, meteorologists and
academics. Second, they use public authoritative sources like government
databases (e.g. Kenya National Bureau of Statistics), mainstream news sources
(e.g. CNN), and peer reviewed scientific research papers and journals. Third,
they rely on several tools. For example, fact-checkers use image and video
verification tools such as InVid (InVID, 2021) followed by a reverse image
search on search engines to collect metadata and digital trail of the
image/video in order to determine its authenticity. Fact-checkers from First
Check, Pesacheck, and Africa Check also reported that they depend on third
party tools such as whois (Whois.net, 2021), Spoonbill (Spoonbill, 2021), edit
history functionality in Facebook, etc. to determine the veracity of a website
or post. Through our interviews we realized the important role played by
comments in fact-checking. Comments contain useful clues that help in
investigating the claims.

> ?What we do first is read comments before checking. We found in comments
> that one woman said this is not the entire video, here is the link to entire
> video. So that’s what led us to entire video. [In comments] we see some
> clues, how to look for what really happened. ? \- P16

####  5.3.4. Assigning veracity label and publishing fact-check

After the investigation, fact-checkers assign a label to the claim that
reflects its veracity. Through our interviews, we realized fact-checking
organizations use a range of labelling conventions, for example, 5 point scale
ranging from completely false to true, four point Pinocchio scale (Post,
2021a), etc. There is no commonly accepted standard for labelling
misinformation. If the verdict on the fact-check is false, fact-checkers also
ask the person/organization that had posted the misinformative content for
correction. The last task is publishing the fact check. The fact-check
contains the link to the content being debunked, description of the claim,
names, quotes and links to all sources used for claim verification followed by
the verdict/label. By publishing these details, the fact-checker takes the
reader through the entire investigation “so that the readers can replicate
[the process] themselves (P18)”.

###  5.4. In-house Fact-checkers—Gathering Sources and Verifying Claims

In-house fact checkers are employed by publication houses to fact-check the
stories produced by the journalists before they are published and
disseminated. They receive the script or the news story from the journalist
along with all source material that they used while researching and writing
the piece. The in-house fact-checker then verifies every claim present in the
story and delivers a modified story along with a list of proposed changes.
Unlike the job of external fact-checkers, the job of this stakeholder group is
to fix or remove incorrect claims without publicizing or calling attention to
the inaccuracies (Graves, 2013). The need for in-house fact-checkers arises in
a publication house not only to ensure that the stories published are
reporting accurate facts to readers but also to “protect the publishing house
from any liability or future lawsuit (P13)”. We discuss the roles of this
stakeholder group below.

####  5.4.1. Identifying and verifying claims

In-house fact-checkers verify every line present in the story. Each line
usually has more than one fact to be checked, from how a proper noun is
spelled, grammatical idiosyncrasies to every phrase making a claim.
Journalist’s opinions and arguments, and quotes from a reputable expert are
the only phrases in the content that are not verified. However, for opinions,
the in-house fact-checkers check the context surrounding the argument to
ensure it is “right and mainstream (P13)”. All the claims that are vague and
without proper sources backing them are modified or removed from the text.

> ?Vague and broad facts which I.. try to get people to remove.. I recently
> had a whole debate with someone about a line that said America is more
> divided today than ever. I was like, how do you check that line.. what are
> the sources for that kind of statement? [It] is just so broad.? \- P15

In-house fact-checkers employ a myriad of techniques—top down approach,
prioritization and batch processing—to identify and verify claims. The
majority of the in-house fact-checkers we interviewed first perform a top down
linear scan of the document to get the most central ideas “which if were
incorrect, the entire piece (article) would be called into question” (P13).
Second, they prioritize the claims for verification. Different fact-checkers
prioritize claims differently. For example, P13, P15 and P25 work with
organizations that have strict timelines for publications. Thus, they first
prioritize the claims that will take maximum time to investigate. On the other
hand, P20 does not have strict deadlines and prioritizes claims that they are
certain are false.

> ?[I prioritize] a claim that I know is going to take me a while to nail
> down. So for example, if there’s a claim … about someone committing a crime
> and I need to file a FOIA request333https://www.foia.gov/how-to.html or go
> through public records or order a document from sort of government agency, I
> want to do that as early as possible so that I get myself as much time. You
> know, basically any claim that relies on other actors in order to meet to
> verify.? \- P13

> ?When I prioritize I start with the things that I know that are wrong, and
> then I look at the things I think are right. And then I check all dates and
> all names. ? \- P20

P15 revealed that they “process the claims in batches”. Everyday they work on
a batch of claims and send enquiries and suggestions regarding those claims to
the journalist. This technique gives journalists ample time to respond and
does not inundate them with several queries towards the very end of the
schedule.

####  5.4.2. Gathering sources for verification

In-house fact-checkers use “primary reputable sources (P15)” to verify claims.
The journalist is expected to give the primary sources that they used while
doing research and writing the article. However, if the sources are missing,
in-house fact-checkers look for primary documentation (like death certificate,
house deed, etc.), mainstream news sources (like CNN, New York Times, etc.),
academic peer reviewed journals and relevant experts to verify the claims. In
case the source used by the journalist is not reliable, the usual journalistic
practice is to gather a total of three to four sources to back up the claim.
In-house fact-checkers heavily rely on Google search to hunt for sources. Some
of the in-house fact-checkers also use Nexis search (Sea, 2021)—a paid
service—that gives news articles, blogs, legal documents as search results.
They find latter to be more effective for searching news and documents.

####  5.4.3. Organizing corrections

After identifying and verifying claims, in-house fact-checkers organize and
communicate the list of questions and suggested changes to the journalist.
According to in-house fact-checkers, there is no standard convention for
organizing corrections. However, we found that all fact-checkers use color
coding schemes for organization but in different ways. For example, P13
highlights verifiable and unverifiable claims in the Google doc with different
colors and leaves comments containing information about the sources. P15
copies each claim from the doc into a separate row in a spreadsheet and then
uses color coding to indicate the state of the claim (pending, verified,
incorrect, etc).

###  5.5. Social Media Managers—Disseminating Fact-checks, Increasing
Engagement

Social media managers are responsible for leading all social media initiatives
including publishing and disseminating fact-checks on their organization’s
social media handles. They determine how to make fact-checks more appealing so
that they attract people’s eyes. They also measure the engagement received by
the posted fact-check(s) and based on the feedback continuously update their
amplification strategies to attract more audience to engage with the fact-
checks. We discuss these tasks below.

####  5.5.1. Disseminating fact-checks

Social media manager’s primary responsibility is to post fact-checks and
educational tip-sheets produced by fact-checking organizations on all major
social media platforms. Few organizations also have a “WhatsApp number where
they broadcast weekly newsletters containing the top fact-checks of the week
(P12)”. Social media managers want to make their fact-checks accessible to the
people with disabilities. Since Twitter did not have captioning feature with
its audio tweets, P17 usually posts fact-checking videos with captions. They
use tools like Kapwing (Kap, 2022) to add captions in the videos.

> ?We wanted to see how can we reach, for example, blind and deaf audiences..
> The audio tweets did not have captioning, they did not have accessibility
> features for disabled audiences. And so this is why we decided to use the
> video instead because videos allow us to caption and so people who can’t
> hear the video can still read the information. ? \- P17

####  5.5.2. Adopting strategies to increase engagement

Social media managers adopt several innovative ways to increase their
content’s reach inorganically (via ads) and organically (via visual
storytelling). We discuss some of the strategies.

  * •

Running advertisements: A few fact-checking organizations that have partnered
with Facebook receive free advertisement credits to promote their fact-checks
on the platform. P17 delved deep into the ad usage process. They advertise
fact-checks that are not bounded by time since they could be promoted via ads
for an extended period. To select the audience for ad targeting, they look at
three attributes, namely, the audience’s education, relevance to the fact-
check and interests. They target audiences who have “graduated from a
university” (education), live in “countries that are most relevant to the
fact-check” being promoted (relevance) and are “interested in news, advocacy
and community issues” (interests).

  * •

Visual storytelling:  Social media managers find visual storytelling to be a
very effective way of getting people to engage with fact-checking content.
They’ve found that many people prefer “watching their content over reading it
(P18)”. Therefore, they convert fact-checks into a visual narrative (images or
videos) before posting them on social media platforms. To create the visual
content, these stakeholder groups rely on video and image editing tools. For
example, social media managers in few of the organizations that we interviewed
use multimedia editing tools such as Adobe illustrator (Ind, 2022), Photoshop
(Off, 2022), etc. Social media managers also create comic strips where a
cartoon walks readers through the fact-checks (“I myself had started this
small comic strip thing to get more engagement.. it became quite popular and
people were liking it, and sharing it”—P7). To engage with the local non-
English speaking audience, the comic strips, images and videos are also
converted to regional languages.

> ?We translate [comics] into Swahili because there’s been this type of
> content, .. being done in like mainstream languages like English, French,
> Portuguese, But some of the more widely spoken local languages aren’t really
> a priority. That was a gap that we identified. ? \- P5

Figure 2 contains three examples of fact-checks leveraging visual storytelling
techniques.

(a)

(b)

(c)

Figure 2. (a) A short YouTube video explaining a fact-check using comic like
visuals (b) An Instagram post containing a fact-check (c) A “postcard”
containing fact-check in Hindi language to be shared on mediums like WhatsApp.
The single image contains the false-claim and the debunk.

####  5.5.3. Measuring engagement and updating strategies

Measuring social media engagement is important to determine what content is
gaining more traction and which engagement strategy is working. P17 informed
us how they track click through rates to determine how many people are
engaging with the content by clicking on the links posted by them.

> ?[Bitly] can tell how many people clicked on a particular link, and we can
> track that.. [It allowed us to] analyze how people engage with these links,
> how many clicks come from the newsletter, as opposed to Facebook or Twitter
> or Instagram? And then that allows us to know whether we need to change how
> we present the information. ? \- P17

The engagement statistics help social media teams to update their engagement
strategies. For example, P17 described how they changed the number of stories
published in their weekly WhatsApp newsletter from five to three after
realizing that public only clicked on the top URLs.

> ?Earlier editions [of WhatsApp newsletter] had up to 5 stories per edition.
> And we were seeing that people were only clicking the top 2 or 3 links, and
> were ignoring the rest. So a decision was made to reduce the number of
> stories that we feature from 5 to 3 to make it shorter.? \- P17

##  6\. Infrastructures Supporting Long-term Advocacy Centric Fact-checking

Long-term advocacy centric fact-checking aims at improving the information
landscape by conducting research about various aspects of online
disinformation, influencing policies surrounding availability and quality of
data and statistics, conducting educational training for aspiring fact-
checkers, organizing literacy campaigns for general public and forming
coalitions with various fact-checking organizations and internet companies.
This type of fact-checking is supported by two stakeholder
groups—investigators and researchers who conduct long-term investigative
projects that include data and network analysis, and advocators who are
involved in policy and advocacy work. In this section, we elaborate on the
role of these stakeholder groups.

###  6.1. Investigators and Researchers—Conducting In-depth Research and
Investigation

Few fact-checking organizations (e.g. Full Fact, Code for Africa, etc.) have a
separate team of investigators and researchers. Unlike fact-checkers who
engage with individual pieces of misinformation, this stakeholder group
conducts an in-depth investigation of persistently circulating misinformation
and disinformation campaigns via data and network analysis.

> ?Part of my work at the moment is creating or developing a framework for
> misinformation crises. So, as opposed to individual pieces of
> misinformation.. looking at when responses need to go over and above the day
> to day..everybody who works in the kind of anti misinformation space gets
> together and they normally introduce new responses and new policies to
> manage that.? \- P9

> ?If.. we are seeing a certain type of misinformation occurring.. every
> single day, debunking each individual one will not help. So what they [fact-
> checking team] do is now they refer that case to us. And then..[we do an] in
> depth investigation to try and see where is this narrative originating
> from.. Data analytics team is the one that does the sifting through the data
> sets that we obtained from social media.. Forensic team does profiling of
> key accounts..that we identified.? \- P10

####  6.1.1. Conducting long term investigative projects

Investigators and researchers undertake several investigative projects such as
verifying the backfire effect444https://fullfact.org/blog/2019/mar/does-
backfire-effect-exist/ of fact-checking (when a claim aligns with a person’s
beliefs, proving that it is wrong will make them believe it more strongly),
studying long term effects of conspiracy theories, examining public engagement
with political news, determining how to communicate fact checks effectively,
etc.

> ?You’ve probably heard about the backfire effect which is kind of mythical
> idea that fact-checking.. does more damage than good. I think the original
> research was repeated and the same effect wasn’t found.. We also did a
> project recently where we looked at.. how conspiracy theories affect
> people’s beliefs in the long term.. who believes and shares misinformation,
> how to communicate your fact checks [while] presenting them to audience.? \-
> P9

> ?Projects include doing investigations into Russian disinformation or
> Russian influence in African countries.. investigating Chinese influence
> operations into .. African countries,.. human trafficking in four South
> African countries.? \- P10

> ?There’s been a lot of research that was done around.. how to design a fact
> check to make it more engaging, how do you phrase a headline,.. how much of
> this information can you put into like a video. What is the ideal video
> length? How to get more people to interact with them and then how to try to
> make them much more long lasting and persistent in people’s memory.? \- P5

These stakeholder groups analyze misinformative content that is going viral on
social media platforms and then trace it back to the social media accounts who
started sharing it. They also conduct qualitative research by conducting
surveys.

> ? We have like a survey and we’ve been trying to find what kind of
> narratives against immigrants are more popular here in Spain. We’re trying
> to prepare another big survey about how fact checking works in Spain and
> what kind of debunk works better for us. ? \- P21

The results of the investigative projects are released as dossiers (e.g. (Pre,
2021), (Uga, 2021)). The dossier provides background of the issue
investigated, data collection and analysis method(s), results of
investigation, and conclusion. For example, in (Uga, 2021), investigators
study the misinformation influence operations that occurred in Uganda before
the January 2021 elections. The dossier first briefly describes the political
landscape in Uganda and provides examples of misinformative tweets that acted
as the starting point of investigation. These tweets leveraged trending
hashtags such as #StopHooliganism, #UgandaIsBleeding 555In November 2020,
Robert Kyagulanyi, a presidential candidate in Uganda was arrested on two
separate occasions. The protests that broke out after his arrests were
documented in Twitter posts containing hashtags such as #StopHooliganism,
#UgandaIsBleeding, etc., etc. to spread false narratives against the
opposition party using past events from other countries. The dossier then deep
dives into the methods used to collect and analyze relevant tweets and finally
attributes the influence operation to the supporters of National Resistance
Movement666https://en.wikipedia.org/wiki/National_Resistance_Movement.

Investigators and researchers use tools like python libraries for data
analysis and gephi (Gep, 2022) for network visualization in addition to the
tools used by fact-checkers.

> ?We were actually using an open source tool called tweepy to collect tweets
> from Twitter.. The network analysis is [done] using a tool called gephi
> [which] will be able to show relationships between tweets using the retweet
> function like which are the accounts that has been highly retweeted, which
> are the influential accounts within the network. ? \- P10

###  6.2. Advocators—Influencing Policy, Building Coalitions, Conducting
Educational Workshops and Literacy Campaigns

Several of our study participants revealed that fact-checking doesn’t stop
with generation and distribution of fact-checks and is much more like a
sustained campaign. It also includes influencing policymakers and information
producers to improve the quality of information (and in turn the quality of
fact-checking), building coalitions with other fact-checking organizations,
social media companies, and journalistic organizations as well as providing
fact-checking training to newsrooms and organizations. Such initiatives are
led by advocators. This stakeholder group identifies and realizes several ways
to improve the short-term claims centric fact checking process. They steward
multiple outreach programs, policy initiatives and advocacy projects locally
and globally. All the initiatives started by these stakeholder groups could be
considered actions that are performed via technological and informational
infrastructures such as workshops, appeals and training programs. We present
the tasks performed by this stakeholder group below.

####  6.2.1. Creating new generation of fact-checkers and fact-checking
organizations

The advocators conduct training, workshops and fellowship programs for people
and organizations all over the world, teach them nitty-gritty details of fact-
checking along with how to setup and operate a fact-checking organization of
their own.

> ?Ethiopia.. [is] a country where press freedom is very limited, online false
> information often leads to offline.. And most of the media there are state
> controlled. So we were conducting trainings on how they can set up fact
> checking desks and try to be independent.? \- P2

> ?In Germany, we only got two IFCN signatories. And that’s not enough. So we
> try to convince traditional media outlets in the regions..to start with fact
> checking.. We are training them. [We have created] a community of fact
> checkers..and more than 600 journalists and we are doing training,
> encouraging them to start with fact checks.? \- P19

Advocators conduct trainings through webinars or online platforms. Some
organizations have set up their online learning platforms where they provide
video tutorials to fact-checkers (e.g. (DPA, 2021)), while others have
partnered with academic universities to conduct educational trainings.

> ?The advocacy campaigns.. on fact-checking, we conduct them virtually. We
> have partnerships with Kenyan institutions of higher learning, such as
> Daystar and Aga Khan university, which allows us to conduct webinars on
> fact-checking. Those sessions are attended by media professors and their
> students. We mainly use Jitsi, Slack and Google Meet to do the training. ?
> \- P2

> ?We are proud of digital e learning platform we have built at DPA. we have a
> lot of videos here, where we explain how to work with the Internet Archive.
> Those are webinars [where we] train maximum 15 people, zoom webinars means
> it’s live training. ? \- P19

####  6.2.2. Pitching the importance of evidence based decision making to
policymakers

The advocators are also actively trying to reach their country’s policymakers
and civil society organizations, informing them about their work and bidding
the importance of facts and evidence based decision making. For example, P12
attended parliamentary researchers’ conference in Kenya where they pitched the
importance of facts in informing the country’s policies and intervention
programs. Similarly, P9’s organization tried to get parliamentary support to
attribute electoral imprints to election campaigners during referendum and
elections in the United Kingdom.

> ?Civil society organizations generate data that then is used by government
> to put in place policy intervention, say poverty eradication.. interventions
> in healthcare. So we just want them to understand that as you’re doing this,
> you also have to check your facts, don’t just rely on a news story or rely
> on a document or rely on a statement by a public politician to define your
> problem.. And so I.. tell them.. their job is to tell the leaders what the
> data says.? \- P12

> ?We sort of started a project, a couple of years ago about imprints. So in
> the UK if you distribute companion materials on paper, you have to say who
> is that from whereas online that’s not the case. And during the referendum
> and.. election.. where certain information come from online, it wasn’t
> attributed to campaigners in the same way that it would be offline. And so
> we tried to get some parliamentary support to change those rules.? \- P9

####  6.2.3. Organizing literacy campaigns

The advocators actively organize literacy campaigns educating people how to
critically examine information that they find on online platforms. For
example, P5 shared how they partner with community radio stations, design MOOC
(MOO, 2022) courses and frequently share tip sheets explaining people what
fake news is and how they can identify it.

> ?We try to also work with community radio stations .. and talk through what
> like what fake news is,.. talking about information literacy and information
> disorder and how it manifests? \- P5

####  6.2.4. Spearheading initiatives to improve accessibility and quality of
data and information

Better decisions are made when better data is available. Quality data and
information is essential because it acts as a source to verify facts in the
short-term claims centric fact-checking. The advocators are actively working
to improve the code of practices in releasing data, for example, updating it
from the paper to the digital age.

> ?Improving the code of practice for official statistics and updating it to
> the internet age. And we’ve done lots of individual pieces of work trying to
> improve specific statistical releases because obviously they form the basis
> of a lot of what we do.. we try and get ..the office for statistics
> regulation to be a bit bolder in how they treat misuse of statistics
> officially.? \- P9

Our interviews also revealed that data in most of the African countries is
either old, or is not accessible. The advocators there are engaging with
government agencies, making them aware about the problem and stressing the
importance of having data publicly accessible .

> ?Before the census in 2019, the last census had been done in 2009. So while
> there are estimates available on the census data, we would use those
> estimates. But then the data is just not accurate when it’s estimated.. [So
> we] talk to the people at the National Statistics office and say we would
> like this data. [We] talk to people at the ministry, and trade unions
> telling them that this is how it would be better if you track unemployment.
> ? \- P12

####  6.2.5.  Building collaborations and coalitions

Several advocators stressed on the need for all fact-checking organizations to
work together, collaborate, and share resources. Such collaborations would be
helpful in understanding common challenges and needs of the fact-checkers. The
coalition would also better position the organizations while making certain
demands from the internet companies. IFCN has played a huge part in forming
such a collaboration and several advocators are actively working to expand
this network.

> ?There’s not a lot of us working together. So that’s what I’m working on in
> collaboration with IFCN.. how different organizations and sectors can work
> better together to complement each other and collaborate and kind of
> information sharing and resources.., [understand] common challenges. For
> example, when we work with internet companies, there are certain things we
> might all want to ask them for which, at the moment, [only] some of us
> asking.? \- P9

In addition to a coalition among themselves, few fact-checking organizations
are also actively partnering with companies like Google and Facebook to fight
fake news on their platform (Conditt, 2017) “because they have a huge impact
on the way people experience misinformation (P23)”. P9 and P12 informed us how
Google is working with their organizations to make their fact-checks more
visible by ranking them higher in Google search. One of our study participant
(P18) was in-fact instrumental in starting the initiative.

> ?When we want what our fact checks to rank higher or to be more visible, so
> there is a back end tool claim review that we use that is integrated into
> WordPress.. And so the search engine looks at whatever you post as a fact
> check and then it ranks it higher in the matrix.? \- P12

##  7\. Needs and Challenges of Stakeholder Groups

In this section we answer RQ2 by presenting the challenges faced by various
stakeholder groups categorized by emerging themes.

###  7.1. Skepticism Towards AI and Automation

Fact-checkers expressed skepticism and distrust towards artificial
intelligence (AI) and automation of the fact-checking process. This finding
resonates with prior work that revealed that the black box nature of
artificial intelligence techniques and machine learning algorithms make their
inner workings unintelligible to humans thereby decreasing users’ trust in
their outputs (Schmidt et al., 2020; Páez, 2019). P4 divulged that they are
skeptical of Facebook’s AI-based tool that aggregates potentially
misinformative content for fact-checkers to verify and rate on its platform
because they think that “algorithms hide a lot of stuff”. They believe that
fact-checkers or independent organizations should be responsible for
aggregating content to fact-check on the social media platforms rather than
“[companies] that are running the platforms.”

Fact-checkers understand that machine learning models work when there are lots
of similar data for training and pattern recognition. Thus, P18 doubts AI’s
capability to detect falsehood in politicians’ statements which can be very
diverse in language and topics. They also doubt AI’s capability to
differentiate between a true and a false statement especially when there are
only subtle differences between the two. P19 does not believe that fact-
checking could be automated since its a complicated process that requires
several humans to discuss and make decisions.

> ? I think [AI based tools are] going to be less useful for most..
> politicians, because the problem is people don’t repeat stuff the same way
> and the addition of a word or two can make a huge difference.. I don’t think
> a computer is ever going to be able to figure that out. ? \- P18

> ?[Fact-checking is] such a complex process, for example, extracting a
> claim,- what’s the underlying meaning of a certain claim, how to understand
> it. Even [for humans] it’s a process of discussing and then deciding. So I
> think it still will be humans work in a way. ? \- P19

Despite the skepticism towards AI, we found that some fact-checking
organizations have indeed adopted AI based tools and a few others showed a
willingness to adopt such tools. However, such tools are only acceptable for
low stake scenarios of monitoring content on social media as compared to high
stake scenarios of assigning a veracity label to the content. This observation
is inline with recent work that found that the use of AI is more acceptable in
low stake compared to high stake decision making processes (Ashoori and Weisz,
2019; Scharowski, 2020).

> ?I will not trust any algorithm or any AI to flag something as right or
> wrong, at least not at this stage.. I prefer AI only to give us a curated
> list, flag us that this is something that we should look in. Make a tool
> that picks up the signals that [indicate the content is] misleading and
> makes a curated list. ? \- P22

> ?If you’re serious with fact-checking, you cannot replace it by
> automatization or things like that. But it could be helpful in [social-
> media] monitoring. ? \- P19

To increase fact-checkers acceptance of AI based tools, P16 and P12 stressed
how its essential to have humans in the fact-checking loop. P12 further said
how they would only trust the AI output if the tool is able to explain how it
arrived at a particular conclusion. Past studies have also developed human-in
the loop AI systems (Smith et al., 2018; Xin et al., 2018) and tried to make
them more explainable in order to foster trust in them (Linardatos et al.,
2021; Abdul et al., 2018a; Schmidt et al., 2020; Miller, 2019; Danilevsky et
al., 2020).

> ?I think at a certain point, AI stops and human being needs to come in to
> verify.. I don’t think that AI can really do exactly the same what we can
> do. Not yet at least.? \- P16

> ? The manual process would still have [to be there]. I would be willing to
> use it [automated tool] to see how it arrives at that conclusion. So if you
> say this is misinformation, and these are the sources of data that we’re
> using to make that. And we check and find that the algorithm.. is not using
> them out of context. So at that point, we would be in a position to say
> let’s check it. ? \- P12

###  7.2. Need For Tools and Limiting Social Media Affordances

We found stakeholder groups divulging several challenges related to the tools
they used and the affordances provided by social media platforms. In the
process, we also discovered a few needs with respect to tools and systems. We
present them next.

####  7.2.1. Monitoring Social Media Platforms is Manual, Time Consuming and
Difficult.

Fact checkers complained about the information overload problem. With the
emergence of a variety of social media platforms, the amount of online
information is increasing exponentially. However, for the fact-checkers,
searching for misleading content mostly remains a manual task. In addition,
generation of search queries that could lead to potentially dubious content is
still based on hit and trial method. As discussed in Section 5.3, only
Facebook has provided a tool to its partner fact-checking organizations that
can aggregate potential misinformation. While a number of social media
monitoring tools have been developed to identify and aggregate misinformation,
particularly on Twitter (such as (Tolmie et al., 2017; Alrubaian et al., 2016;
Hunt et al., 2020; Shao et al., 2016; Cerone et al., 2020; Resnick et al.,
2014), etc.), other social media platforms (e.g. YouTube, Google, Yandex,
etc.) where misinformation is equally prolific also need attention.

> ?The search is not easy to be honest.. it takes a lot of time to actually
> find the content because we only have a manual method to do that, hit n
> trial method to do that.. I have even scrolled to the point where YouTube
> shows me no more results. So that’s how manual it gets.? \- P7

> ?If there’s some.. tool that helps filter..different types of misinformation
> will help because now you have information overload and you don’t know what
> to choose and what not to choose.? \- P3

####  7.2.2. Limitations of platform affordances.

Many fact-checkers complained how online social media platforms’ affordances
become a hindrance for searching and filtering content. For example, inability
to search for posts and comments on Facebook, lack of search trends feature on
platforms (with an exception of Twitter), unavailability of fine grained
search filters, inability to download content on platforms like Instagram and
inability to search for same videos that were uploaded with different keywords
(title, description, etc.) on YouTube are some of the limitations of platform
affordances.

> ?Facebook is actually tricky to be honest, because there’s no one way to
> search content. you can only search people..pages and groups, etc.. but I
> need user generated content.? \- P7

> ?Youtube search engine is not very good. It will just show you only videos
> which are popular which has no use. Misleading videos won’t have too much
> views, but they have a lot of uploads. What happens is that since a lot of
> people.. upload videos using different caption, different keywords. So..
> [there might be] 10 versions of the same thing. ? \- P8

####  7.2.3. Systems and tools needed to detect misleading claims on private
message platforms.

Privacy settings on social media groups (e.g. Facebook) and end-to-end
encryption in messaging platforms (e.g. WhatsApp) act as a hindrance in
accessing misleading content circulating on these platforms. The fact-checkers
informed that they need tools that allow them to access and flag content on
these platforms. Recent research work has focused on building crowd sourced
WhatsApp tip lines for discovering content to fact-check (Melo et al., 2019;
Kazemi et al., 2021). However, being able to track or report private messages
via tools necessitates serious ethical considerations. Private messaging
platforms give users a false sense of security, thereby making them share
sensitive information (e.g. clinical records of patients (Mars and Scott,
2016)), without anonymizing it (Guerra et al., 2021) and hence, expose users
to privacy risks (Barbosa and Milan, 2019).

####  7.2.4. Overload of tools.

During our interviews, fact-checkers showed and talked about several tools
that they use during fact-checking. One of the participants P8 showed us
around 15 tools. There is a tool overload problem. P3 suggested building a
single tool that could provide all functionalities that fact-checkers need.
However, since fact-checking is a complex task involving multiple steps, it is
difficult for a single tool to cater to a divergent set of requirements and
functionalities (Tolmie et al., 2017). Thus, building a suite of purpose-built
tools that cater to specific needs of fact-checkers in the various steps
involved in fact-checking would be more useful for the fact-checkers.

> ?[If one could] put all these tools in one, .. So I don’t have to look for
> different tools when I’m analyzing a video or an audio. I can do it in one
> place instead. Like.. I can use 10 tools to analyze the video. But if we can
> have one..[tool that] gives you the information you need.? \- P3

####  7.2.5. Need for specific tools

Despite the problem with tools overload, existing tools lack task-specific
functionalities. For instance, a lot of steps involved in video fact-checking
are manual. While tools like Invid and reverse image search are available to
verify a video, they are only useful to check if the video is digitally
altered or used in a different context than the original. For all other videos
(e.g. videos with conspiracy theories), claim extraction and claim
verification process is manual. For lengthier videos, this process could get
even more tedious.

> ?There is no tool to debunk [conspiracy theory videos]. For example, I
> cannot do a reverse image search, I cannot divide the video into key frames,
> because it is a narrative that is false.. We have to really go line by line
> and see what the person is saying, and then all we can do is search quotes
> from different organizations to [verify the claim] .? \- P7

Fact-checkers believe that “efforts against misinformation advance much more
and much quicker in English” (P23) than in other regional languages. They need
tools to transcribe videos in local regional languages.

> ?The problem with transcribing videos online or using any other software is
> languages because India has so many languages and we tend to get a video and
> information in every possible language. So it becomes difficult to have one
> dedicated tool to transcribe all our videos.? \- P14

Editors revealed that editorial work is mostly manual. P1 spoke about how
editing work is “still very human” and the human resource management tools
like Trello could be further strengthened. For example, currently, “there are
a lot of issues of accountability” with the Trello board, it lacks control
features because of which “anybody can move a card anywhere.”

####  7.2.6. Getting organic engagement for fact-checks without advertisements

P17 revealed that while on some platforms (e.g. Twitter) it is easier to “get
organic reach and engagement” by adopting appropriate strategies, it is “a big
challenge to get [same].. attention without advertising” on other platforms
(e.g. Facebook).

###  7.3. Issues around policy and information infrastructure

Stakeholder groups discussed several challenges surrounding information
availability and quality. They understand that quality data is essential not
only for developing AI based automated tools but also for investigating
claims.

####  7.3.1. Need to improve information quality before automation

AI models are as good as the data available. If the information against which
the claims are to be verified is missing or of low quality, the models would
never work.

> ?Automation is tricky because.. in a place like East Africa ..information is
> not readily available. You cannot say that I’ll go to this site and get this
> information so that when these numbers are presented it can easily be
> automated.? \- P12

P9 raised an interesting point along the same lines. They explained how
success of automated fact-checking is dependent on the accessibility and
format of statistics and information available. The data has to be in the same
format for the machine to be able to understand it. Their organization is
working with institutes around the world to improve statistics globally.

> ?[We want] statistics being published in a kind of open accessible and
> consistent format. So, for example, like any symbols that are used to show a
> caveat about data needs to be the same so that our machine can understand
> them each time.. My colleague.. is working with the open data Institute in
> UK and also globally.. to [determine] whether there are ways of improving
> statistics so that automated fact-checking can work.? \- P9

P22 elicited how data in their country is not available in a user friendly or
machine usable format.

> ?The data, you will find that it is in a PDF… file or a photograph on the
> website..then how do we use it?…You need the data ..[to be] put into a excel
> sheet so you can clean it. Data should be in a format which is user friendly
> so it can be used, [it should be] machine learning friendly so that the
> machine can pick up and use the data. ? \- P22

The aforementioned concerns elicited by fact-checkers are in line with the
prior research that has also listed absence of structured and quality data as
well as lack of adherence to data standards, as few of the major challenges
faced in the field of big data (Adnan et al., 2019; Desouza and Smith, 2014).
Lack of harmonization between data sets makes data integration a complicated
and time consuming task (3Ch, 2021; Cha, 2021). Data integration is necessary
in various scenarios related to fact-checking (Leblay et al., 2018), for
example, querying different databases originating from different sources to
determine the veracity of content or determining whether a piece of content
has already been fact-checked by searching in various fact-checking databases
(Leblay et al., 2018; Cazalens et al., 2018). Thus, scholars have stressed on
the need to adhere to common data standards to help facilitate data
integration and reuse (Bertot and Choi, 2013). There have been a number of
cross country initiatives to set common standards for data in various domains.
For example, in 2017, European Medicines Agency held a meeting to discuss the
opportunities and challenges in applying a common data model to healthcare
data across the countries in Europe to support regulatory decision-making
(ACo, 2021; Aco, 2021). Furthermore, several advocators (Section 6.2) have
also been spearheading initiatives to improve the data quality in their
respective countries.

####  7.3.2. Lack of information sources.

Our interviews with fact-checkers in the Global South revealed that the
information needed to investigate claims either does not exist or is not
updated periodically.

> ?There’s a lot that we don’t cover because of lack of sources.? \- P2

> ?In Kenya,.. demographic Health Survey, which .. shows the health situation
> in the country, the last one was done in 2014, this is 2020. It’s just too
> old, and we can’t use it.? \- P12

####  7.3.3. Difficulty in getting information for research from civic
organizations.

In most cases, fact checkers need multiple sources to debunk a claim. To
obtain these resources they rely on public data sets and information from
government and civic organizations. Six fact-checkers working in African
countries and the Balkan regions informed us how information needed for
research is not publicly available and getting it from officials is a long and
difficult process.

> ?It’s so hard to get information from the government.. because everybody
> wants to protect themselves. They don’t want to give you the information
> that you actually need.? \- P3

####  7.3.4. Algorithmic bias against local content

P5 complained about algorithmic bias in terms of how content indexed in search
engines is skewed towards the Global North region making it very difficult to
search for local content in regional languages of the Global South.

> ?The way the algorithms works was very I’d say Euro-centric or like North
> America [centric].. Trying to find tools that would enable me to find..
> stuff that’s not in English, like things in local languages was quite
> challenging.. it would take a while for some of the stuff from from this
> side to be indexed on.. Google searches and other platforms .. So, there’s
> sort of algorithmic bias when it comes to.. find stuff like that.? \- P5

###  7.4. Emotional cost of fact-checking

In addition to the manual labor involved in fact-checking, fact-checkers also
face significant emotional toll and stress in their job. They are often
victims of online threats and abuse from users and conspiracy theorists whose
posts they were tasked to debunk. Manually scanning through misinformative
content about certain topics, such as riots, conspiracy theories, also has
adverse affects on their mental health.

> ?It becomes kind of very stressful job. Seeing all violence and getting into
> each and every detail, kind of takes a toll on your mental health. And then
> again you have to listen to abuses. And the situation is worse when you like
> put this content online and then people attack you? \- P8

> ?We are exposed to threats, these conspiracy theorists are very aggressive
> and I worked only for like a couple weeks when I saw a CrowdTangle post with
> my own photo saying, this is your censor and it was very unpleasant feeling
> to find that.? \- P16

While previous work has studied emotional labor and psychological
symptomatology in content moderation work (Roberts, 2014; Kerr and Kelleher,
2015; Arsht and Etcovitch, 2018; Steiger et al., 2021; Dosono and Semaan,
2019), no study has investigated the emotional cost of fact-checking work.
Studying human costs underlying the fact-checking process and determining
wellness interventions to psychological effects of fact-checking are another
fruitful avenues for future research.

##  8\. Discussion

In this section, we discuss how our study renders visibility to the human and
technological infrastructures supporting the fact-checking work and the
collaborative efforts involved in the process. We also discuss the needs of
the stakeholder groups and the implications of our findings on the future
research directions in fact-checking.

###  8.1. Rendering visibility to the human infrastructure of fact-checking

Till date the primary objective of fact-checking is considered as debunking
misleading claims. Based on this objective, prior work suggests that fact-
checking can only influence three constituencies— people, journalists and
political operatives (Amazeen, 2013). By identifying and examining the human
infrastructure—the stakeholder groups that need to be brought into alignment
to accomplish fact-checking, our work provides a means to think about fact-
checking as a multidimensional initiative which then assists in understanding
1) the invisible aspects of the process, and 2) the other ways fact-checking
process can have an influence. Our work shows that fact-checking is supported
by several processes, such as editorial work, social media engagement work,
in-depth research and data analysis, as well as advocacy and policy work that
might not be visible to the external world. Through the study of these
processes, we establish how fact-checking has evolved to include both short-
term claims centric and long-term advocacy centric fact-checking. We make
visible the efforts that fact-checking advocators are putting in to improve
the availability, accessibility and quality of data and statistics by aligning
the focus and interests of governments and internet companies with fact-
checking organizations. Through these efforts, the fact-checking organizations
are not only improving the information landscape of their country but in turn
are also improving the quality of (short-term claims centric) fact-checking
itself. Rendering visibility to the work of the human infrastructure and the
invisible processes of fact-checking has helped in uncovering the needs—both
social and technical—of the entire fact-checking ecosystem consisting of all
the stakeholder groups. The knowledge of needs of the ecosystem could enable
design and development of tools and policies to support various aspects of the
fact-checking process.

###  8.2. Collaborative efforts in the fact-checking process

Our study highlights fact-checking as a distributed problem where
collaboration takes place at multiple stages among people with different skill
sets within and outside the fact-checking team/organization. First,
collaboration occurs among the stakeholder groups: editors, fact-checkers,
social media managers, researchers & investigators, and advocators (refer
Figure 1 for an overview). Second, collaboration extends to the outside world
with experts such as doctors, oncologists, academics, etc. whose expertise is
needed to investigate dubious claims. Third, fact-checkers collaborate with
civic and government organizations during the investigative stage to access
data and statistics related to the claims under investigation. Fourth, in
parallel, advocates reach out to policymakers to influence policy by
highlighting the challenges faced by fact-checkers. Fifth, several internet
companies, like Facebook collaborate with fact-checking organizations to fact-
check and debunk misleading claims on their platform. Finally, collaboration
occurs between social media users and the stakeholder groups at two stages: 1)
at the content monitoring stage where users report dubious claims that they
encountered online directly to fact-checkers via tip-lines, and 2) when users
engage with fact-checks disseminated by social media managers. Rendering
visibility to the collaborative efforts in the fact-checking process has
multiple benefits. We discuss a few.

####  8.2.1. Increase in efforts to foster collaborations

Making visible the collaborative efforts in the fact-checking ecosystem can
lead to efforts and policies that foster these collaborations. There has been
growing research on how to make users engage with the published fact-checks
(Amazeen et al., 2019; FROM, 2020; Young et al., 2018; Ecker et al., 2020;
Chen et al., 2021). For example, fact-checking organization Pesacheck created
a Twitter bot named debunk bot that detects tweets containing URL(s) to
misinformative content and replies to them with the link to the fact-check
that their organization has published (Deb, 2022). Similar investigations and
efforts can be put to support other collaborations, such as between experts
and fact-checkers. There has been only a handful of recent efforts in this
direction, for example, Meedan’s Digital Health
Lab777https://meedan.com/digital-health-lab and Facebook’s Journalism
project888https://www.facebook.com/journalismproject/facebook-partners-with-
meedan-digital-health-lab-to-help-fact-checkers-debunk-health-
misinformation?locale=pa_IN support fact-checkers in debunking health related
misinformation by connecting them with health experts and providing them with
resources on the health topics that they are covering. Imagine a private
social media platform consisting of separate communities (like subreddits) of
fact-checkers, and experts from different fields (from doctors, journalists,
meteorologists, to university librarians and professors) to facilitate easy,
and targeted communication and information sharing. Fact-checkers can post
questions in relevant communities, seek quotes from experts, and get
suggestions for online and offline resources to support their investigations.
Fact-checkers can also share with each other their concerns or information
about new tools that they discovered. Such a platform could facilitate fact-
checking organizations in addressing online misinformation effectively and in
a timely manner.

####  8.2.2. Revealing the value of fact-checking work to internet companies

In recent times several platforms such as Google search, YouTube, and Google
images have started actively using fact-checks produced by fact-checking
organizations with their search results to help determine their validity and
truthfulness (Goo, 2022; See, 2022; Bri, 2022). Our study also revealed how
fact-checking organizations are collaborating with internet companies and
allowing them to index their fact-checks. This finding contributes towards the
HCI scholars call of making people aware of “the value their data brings to
intelligent technologies” (HCI, 2022; Vincent et al., 2019b). Our work
highlights the value that fact-checking is bringing to the social media
companies who regularly use the fact-checks to regulate the content on their
platforms and provide reliable information to the users. Previous scholarly
works have raised questions on whether volunteer created content such as
Wikipedia articles should receive more economic benefits from the internet
companies (Vincent et al., 2019b, a). On similar lines, we want to raise the
question whether fact-checkers and fact-checking organizations should also
receive more economic benefits for their work.

####  8.2.3. Revealing power dynamics in collaborations

Our study also sheds light on the power dynamics of collaborations happening
in the fact-checking ecosystem. For example, our work shows how editors have
the power over fact-checkers in determining what kinds of misinformative
claims should be prioritized. Additionally, in certain situations, fact-
checkers depend on civic and government organizations to get data for
investigation. Tools released by social media companies, such as Facebook
Queue (refer Section 5.3.1) also dictate what kind of claims fact-checkers
investigate. These companies also have the power to remove the fact-checks
from their platform. For example, Facebook removed a fact-check on abortion
after receiving complaints from Republican senators (Fac, 2022). Further
investigation of the potential consequences of these power dynamics in the
fact-checking process is a fruitful avenue of future research.

###  8.3. Implications for future research on fact-checking

Taking a multi-stakeholder perspective on the fact-checking process helped us
learn the needs of stakeholder groups as well as uncover the challenges that
go beyond the technical aspects of fact-checking. We use our findings to
discuss and propose various directions that the future research on fact-
checking can take. In Section 8.3.1, we start by discussing the needs of
various stakeholder groups and propose solutions for the same. In Section
8.3.2, we discuss the values that the stakeholder groups desire in the tools
and systems built for them. Next, in Section 8.3.3, we discuss how focusing on
technical solutions alone is not enough and how the existing automated
approaches fail to work in real life since they ignore the social aspect of
fact-checking. We also reflect on the social and civic challenges faced by
fact-checking organizations by discussing the role of information
infrastructure on fact-checking. Finally, in Section 8.3.4, we end by
stressing how the current research on misinformation has not focused on the
Global South countries and how there is a dearth of fact-checking tools built
for regional languages of the Global South.

####  8.3.1. Technical needs of fact-checking

Our study reveals that monitoring social media platforms is the most
challenging aspect in the job of a fact-checker. A combination of over
reliance on third party tools to discover potentially dubious content,
limiting platform affordances, and a manual way of going through each search
result to determine if its potentially misleading makes the process extremely
tedious. Access to better search filters on social media platforms, and a
community-based approach to reporting misinformation where users of social
media platforms are able to report problematic content are some useful ways to
assist fact-checkers in finding misleading claims.

Fact-checkers also agreed that its impossible for them to have access to all
corners of the web. For example, they do not have access to content on private
messaging platforms like WhatsApp that have become a popular haven for groups
interested in sharing misinformation (Almeida, 2019). Given fact-checkers’
skepticism towards AI and automation, user reporting via tip lines appears to
be a feasible solution to access content on such platforms. Research on how to
motivate people to report problematic content is a fruitful avenue of future
research. We also found that fact-checkers end up viewing long videos to
extract misleading claims and reading through lengthy comments section to get
clues that would help them investigate the claims. A system that could utilize
comments to highlight all the misleading claims present in the video, leaving
the final decision of selecting what claims to verify to fact-checkers could
be useful for the fact-checking community. Furthermore, a tool that highlights
credibility indicators (such as comments containing useful information for
investigating the claims) would significantly reduce the manual effort put in
by the fact-checkers.

Our interviews revealed that when it comes to assigning a verdict to a claim
there is no single standard labelling system shared across fact-checking
organizations. Collective agreement on the labels could allow for greater
information sharing and interoperability. Future efforts can focus on
developing structured ontologies for representing credibility assessment
metrics that would lead to development of common labels and benchmarks for
assigning veracity labels to the claims.

Our work also sheds light on the needs of stakeholder groups other than fact-
checkers. There are lack of editorial and process management tools that could
be used by news desk and copy editors. Social media managers need effective
strategies to increase users’ engagement with the fact-checks. In order for
fact-checks to really have an impact, they must be “seen and attended to by
audiences” (Amazeen et al., 2019). To accomplish this, it is essential to
understand who shares fact-checks on social media platforms and what modality
or visual storytelling technique is more suited for which platform. Tools are
also needed to convert fact-checks to multiple languages to increase their
reach. Platforms can also help in making the fact-checks more visible and
accessible. Google’s efforts (e.g. claim review) to prioritize the ranks of
fact-checks in searches is one such effort. Technology critics have called
structured journalism, where fact-checks are produced in a machine readable
form, as the future of fact-checking (Adair, 2021). Recent work has tried to
automate extraction of structured information—claim, claimant, and verdict
from the fact-check, to allow search engines to display it in the search
results (Jiang et al., 2020). More such efforts towards structured journalism
are needed to integrate fact-checks with online content.

####  8.3.2. Values desired in fact-checking tools and systems

Our study participants expressed skepticism in automation and AI technology
because of its black-box nature. At the same time, they also showed
willingness to adopt automated solutions for low stake tasks. Fact-checkers do
not want systems that decide the veracity of information, rather they want
tools that could help them with their day-to-day tasks such as monitoring
online platforms, checking whether a video is digitally altered, transcribing
videos in regional languages, etc. Algorithm explainability combined with
tools that have humans in the loop emerged as key values that fact-checkers
desire in the systems built for them. Familiarity with the inner workings of
algorithms along with tools that use both human and machine capabilities for
problem solving can help increase fact-checkers’ trust in the automated
systems. Recent times have witnessed burgeoning interest in the field of
human-centered XAI where researchers draw from formal HCI theories to design
explanations on how machines reach a particular decision (Abdul et al., 2018b;
Adadi and Berrada, 2018; Arya et al., 2019). Scholars are also studying how to
design human and machine configurations to operationalize human-in-the-loop
systems (Grønsund and Aanestad, 2020; Xin et al., 2018). Understanding
specific needs for explainability with respect to fact-checkers,
operationalizing those needs at the conceptual and methodological levels in
tools developed for fact-checking, designing systems that could take fact-
checker’s feedback and use that to modify the algorithm used by the system are
few useful directions for future research.

####  8.3.3. Going beyond the technical: need for socio-technical solutions

Would technology mediated solutions alone lead to improvement in the quality
of fact-checking? While automating the entire fact-checking process and
developing new tools to increase efficiency and scalability seems promising,
it is not a panacea to all the problems faced by the stakeholder groups. A
holistic change could only be achieved via systematic changes in the civic,
political, and informational contexts. Through our study we found how
accessing data from the civic and government bodies is a difficult task,
especially if it portrays the government in a less-than-favorable light. While
fact-checking is now a global endeavor, in some countries information is
either not publicly available or essential information (e.g. census data,
health surveys, etc.) is outdated because of lack of periodic collection. Our
interviews revealed how several claims are left unchecked because of lack of
sources. The availability of high quality up-to date information is essential
for fact-checking—manual or automatic. Additionally, good quality data is
precursor to having good machine learning models (Sessions and Valtorta, 2006)
that would be needed to automate the investigative step in the fact-checking
pipeline where publicly available authoritative statistics are used to
determine the veracity of claims. Thus, as part of long-term advocacy centric
fact-checking, advocators within the fact-checking organizations have been
actively pushing for policy changes to improve the availability and quality of
data and statistics. For example, fact-checking organization Full Fact gave
oral evidence to the House of Commons Public Administration and Constitutional
Affairs Committee on issues of coherent and accessible health statistics in
the United Kingdom (of Commons, 2019). There is a growing interest in the HCI
community to engage with policymakers as a way to inform policy that could
benefit society (Lazar, 2015; Spaa et al., 2019). Future work in fact-checking
could focus on understanding the opportunities and difficulties that
advocators face while engaging with civic organizations and determining
strategies that advocators can adopt to shape policies surrounding statistics
and public data in their respective countries.

####  8.3.4. Fact-checking in the Global South

Recent work in the CSCW community stressed on the fact that academic research,
till date, has primarily focused on misinformation in Western countries, while
not addressing the phenomenon in the Global South (Haque et al., 2020). Our
study reiterates the lack of knowledge and context surrounding the
misinformation landscape in the Global South. Local regional languages are
under resourced both by the online platforms and search engines making it
difficult for fact-checkers to gain access to local context online and for
regional speakers to gain access to reliable information (Draft, 2020a). The
current design-based approaches to fact-checking do not take into account the
lack of fact-checking resources in regional languages and thus, fail to
account for the unevenness of the viability of fact-checking across the globe.
Advocators recognize how access to local community-specific knowledge and
culture is essential to understand the characteristics of misinformation and
why it spreads in a particular region (Draft, 2020b). Thus, they are calling
attention to the need to improve the information infrastructure and research
in the Global South. Our work also acts as call to action for researchers to
study the misinformation landscape in the Global South region.

##  9\. Conclusions and Limitations

Our work sheds light on how fact-checking is practised in the real world by
presenting the infrastructures—both human and technological—supporting the
fact-checking work. We interviewed 26 participants belonging to six primary
stakeholder groups involved in the fact-checking process namely editors,
external fact-checkers, in-house fact-checkers, investigators and researchers,
social media managers, and advocators. By studying the various tasks performed
by these stakeholder groups, we identified the role of tools, technology and
policy in their work. Finally, we also identified key challenges faced by the
stakeholder groups along with opportunities of advancing current tools,
policies and technology for fact-checkers.

Our work is not without limitations. The majority of the organizations that we
interviewed are either IFCN signatories or work closely with IFCN signatories.
Thus, the fact-checking model and tools used by the stakeholders presented by
our study might not be applicable to every fact-checking organization. The
fact-checking process could also be subjected to various degrees of local and
regional variabilities that our work does not capture. We also acknowledge
that not all stakeholder roles are present in every fact-checking organization
that we interviewed. For example, not every organization is doing long term
investigative or advocacy work. We leave the examination of the factors
influencing the fact-checking work in different regions and the variabilities
in fact-checking work across regions to future work.

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