# Assessing enactment of content regulation policies: A post hoc crowd-sourced
audit of election misinformation on YouTube

Prerna Juneja  University of WashingtonSeattleWashingtonUSA<EMAIL_ADDRESS>,
Md Momen Bhuiyan  Virginia TechBlacksburgVirginiaUSA<EMAIL_ADDRESS>and
Tanushree Mitra  University of WashingtonSeattleWashingtonUSA<EMAIL_ADDRESS>
(2023)

###### Abstract.

With the 2022 US midterm elections approaching, conspiratorial claims about
the 2020 presidential elections continue to threaten users’ trust in the
electoral process. To regulate election misinformation, YouTube introduced
policies to remove such content from its searches and recommendations. In this
paper, we conduct a 9-day crowd-sourced audit on YouTube to assess the extent
of enactment of such policies. We recruited 99 users who installed a browser
extension that enabled us to collect up-next recommendation trails and search
results for 45 videos and 88 search queries about the 2020 elections. We find
that YouTube’s search results, irrespective of search query bias, contain more
videos that oppose rather than support election misinformation. However,
watching misinformative election videos still lead users to a small number of
misinformative videos in the up-next trails. Our results imply that while
YouTube largely seems successful in regulating election misinformation, there
is still room for improvement.

misinformation, elections, voter fraud, algorithm audit, fairness,
recommendations

††journalyear: 2023††copyright: rightsretained††conference: Proceedings of the
2023 CHI Conference on Human Factors in Computing Systems; April 23–28, 2023;
Hamburg, Germany††booktitle: Proceedings of the 2023 CHI Conference on Human
Factors in Computing Systems (CHI ’23), April 23–28, 2023, Hamburg,
Germany††doi: 10.1145/3544548.3580846††isbn: 978-1-4503-9421-5/23/04††ccs:
Information systems Web search engines††ccs: Information systems Web
crawling††ccs: Information systems Personalization††ccs: Information systems
Content ranking††ccs: Human-centered computing Human computer interaction
(HCI)

##  1\. Introduction

> ?Oregon GOP frontrunner for governor embraces claims of election fraud… said
> he doubted Oregon’s vote-by-mail system?—The Texas Tribune, Feb 11, 2022
> (Shumway, 2022)

> ?Election Deniers Go Door-to-Door to Confront Voters After Losses (in US
> primaries)?—Bloomberg, Aug 23 2022 (Beckwith, 2022)

> ?With 10 weeks until midterms, election deniers are hampering some election
> preparations Some election deniers have “weaponized” against us, one
> election official says.?—ABC News, Aug 30, 2022 (Soo Rin Kim and Holland,
> 2022)

Skepticism around the legitimacy of the US electoral process, which primarily
gained momentum during the 2020 US presidential election, had serious
ramifications. For example, endorsement of election conspiracy theories was
found to be positively associated with lower turnout in the 2021 US Senate
election in Georgia (Green et al., 2022). In 2022, the false narratives around
the 2020 elections still persist (Lima and Schaffer, 2022; Locher, 2022) and
continue to threaten democratic participation in the upcoming US midterm
elections (Lima and Schaffer, 2022; Locher, 2022). In the last two years, 19
US states altered voting procedures and enacted laws to make voting more
restrictive, creating information gaps and fresh opportunities for election
misinformation to emerge and proliferate in the real and online world (Locher,
2022). Thus, battling election misinformation has never been more important.

Studies show that social media platforms have become important mediums for
political discourse (Allcott and Gentzkow, 2017; Vitak et al., 2011). In
particular, YouTube—the most popular platform among US adults (Perrin and
Anderson, 2019)—has emerged as a political battleground as demonstrated by the
fact that both political parties extensively used the platform for election
campaigning (Thompson, 2020). However, the platform came under fire from
technology critics for being a hub of electoral conspiracy theories (Kaplan,
2020; Wakabayashi, 2022). Given the concern that search engines can play a
significant role in shifting voting decisions (Epstein and Robertson, 2015;
Epstein et al., 2017) and can confine users into a filter bubble of
misinformation (Hussein et al., 2020), there has been a push for online
platforms to enact policies that minimize election misinformation (Seitz,
2022). In response to this push, YouTube introduced content policies to remove
videos spreading election-related falsehoods and claimed that misinformative
videos would not prominently surface or get recommended on the platform (Team,
2020b; YouTube, 2022b; Lima, 2020; Stepnick, 2022). However, the formulation
of policies does not equate to effective enactment. It’s evident from the
results of two misinformation audits conducted on the platform for the same
conspiratorial topics (such as vaccine controversies, and 9/11 conspiracies),
first in 2019 (Hussein et al., 2020) and second in 2021 (Tomlein et al.,
2021), both of which found echo chambers of misinformation on the platform.
Despite changes to YouTube’s misinformation policies in 2020 (Team, 2020a),
the authors of the second audit study did not find improvements when compared
to the results of the first audit, rather they found recommendations worsening
for topics like vaccination. These findings iterate the need to continuously
audit platforms to investigate how a platform’s algorithms fare with respect
to problematic content and how effectively a platform’s content policies are
implemented (Simko et al., 2021). While multiple studies have audited YouTube
for misinformation (Hussein et al., 2020; Tomlein et al., 2021; Papadamou et
al., 2022), these were mostly conducted using sock-puppets (bot accounts
emulating real users) in conservative settings111For example, sock-puppet
building account history by watching videos that only promote misinformation.
which often do not reflect true user behavior. There is a dearth of crowd-
sourced misinformation audits that test the algorithms’ behavior with real-
world users ((Bisbee et al., 2022) is one of the few exceptions). In this
paper, we fill this gap by conducting a large-scale crowd-sourced audit on
YouTube to determine how effectively YouTube has regulated its
algorithms—search and recommendation—for election misinformation.

To conduct the audit, we recruited 99 participants who filled out a survey and
installed TubeCapture, a browser extension built to collect users’ YouTube
search results, and recommendations. The extension conducted searches for 88
search queries related to the 2020 US presidential elections. We also seeded
TubeCapture with 45 seed videos with three differing stances on election
misinformation—supporting, neutral, and opposing. The extension collected up-
next recommendation trails—five consecutive up-next recommendation videos—for
each seed video. TubeCapture simultaneously collected YouTube components from
both personalized standard and unpersonalized incognito windows allowing us to
measure the extent of personalization. This leads us to our first research
question:

  * RQ1 Extent of personalization:  What is the extent of personalization in various YouTube components?

    * RQ1a: How much are search results personalized for search queries about the 2020 US presidential elections and the surrounding voter fraud claims?

    * RQ1b: How much are YouTube’s up-next recommendation trails personalized for seed videos with different stances on election misinformation—supporting, neutral and opposing?

We find that while search results have very little personalization, up-next
trails are highly personalized. We next venture into determining the amount of
election misinformation real users could be exposed to under different
conditions, such as following up-next trails for videos supporting or opposing
election misinformation.

  * RQ2: Amount of election misinformation: What is the impact of watching a sequence of YouTube up-next recommendation videos starting with seed videos with different stances on election misinformation (supporting, neutral, and opposing) on various YouTube components?

    * RQ2a:  How much do search results get contaminated with election misinformation?

    * RQ2b:  What is the amount of misinformation returned in users’ up-next recommendation trails?

    * RQ2c:  What is the amount of misinformation that appears in users’ homepage video recommendations?

We find that YouTube presents debunking videos in search results for most of
the queries. We also observe an echo chamber effect in recommendations where
trails with supporting seeds contain more misinformation than trails with
neutral and opposing seeds. Since election misinformation is closely entangled
with political beliefs with several right-leaning news sources amplifying the
claims of voter fraud (Bump, 2021; Macdonald and Brown, 2022), we also study
the diversity and composition of the content presented by YouTube in its
various components. We ask,

  * RQ3: Impact on composition and diversity: What is the impact on content diversity when watching a sequence of YouTube up-next recommendation videos starting with seed videos with different stances on election misinformation (supporting, neutral, and opposing)?

    * RQ3a:  How diverse are the search results ?

    * RQ3b:  How diverse are the up-next recommendation trails?

We find that YouTube ensures source diversity in its search results. We also
find a large number of impressions for left-leaning late-night shows (e.g.
Last Week Tonight with John Oliver) and right-leaning Fox news in users’ up-
next trails. Overall, our work makes the following contributions:

  * •

We conduct a post hoc audit on YouTube to determine how its algorithms fare
with respect to election misinformation; post hoc auditing comprises
investigating a platform for a past topic or event which could have a
significant impact on citizenry in the present and future. In turn, we are
able to test the effectiveness of YouTube’s content policies enforced to curb
election misinformation.

  * •

We extend prior work on misinformation audits by conducting an ethical crowd-
sourced audit to see the impact of performing certain actions on the searches
and recommendations of real-world people with complex platform histories
instead of conservative settings of sock puppet audits.

  * •

Our audit reveals that YouTube search results contain more videos that oppose
election misinformation as compared to videos supporting election
misinformation, especially for search queries about election fraud in
presidential elections. However, a filter bubble effect still persists in the
up-next recommendation trails, where a small number of misinformative videos
are presented to users watching videos supporting election misinformation.

##  2\. Related Work

###  2.1. Algorithmic audits

Search engines and social media platforms act as information gatekeepers, with
their algorithmically generated feed, timeline, and recommendations affecting
the information exposure of people. Given the ubiquitousness of the algorithms
and the influence they hold over the citizenry, scholars have emphasized the
need for auditing online platforms, i.e., conducting a systematic
investigation to determine whether the algorithmic output is aligned with
“laws and regulations, societal values, ethical desiderata, or industry
standards” (Shen et al., 2021). As a result, several research studies have
audited algorithmic systems for distortion (e.g. hyper-personalization (Bandy,
2021), ideological skew (Bandy and Diakopoulos, 2021; Trielli and Diakopoulos,
2019; Kulshrestha et al., 2017) ), discrimination (e.g. racial and gender
discrimination (Buolamwini and Gebru, 2018; Kyriakou et al., 2019; Asplund et
al., 2020)), exploitation (e.g. exploiting users’ private and sensitive
information (Datta et al., 2014; Cabañas et al., 2018)) and misjudgment (e.g.
incorrect algorithmic predictions (Kahlisch, 2021; Duwe, 2019)) (Bandy, 2021).
These scholarly studies have used a myriad of audit research methods,
including code audits, scraping audits, sock puppet audits, and crowd-sourced
audits (see (Sandvig et al., 2014) for a review). Among them, sock puppet
auditing, where researchers create bots or fake user accounts to impersonate
real-life users is the most popular since this audit method gives researchers
the greatest control over experimental variables (Wilson, 2019) and doesn’t
require high participant recruitment cost like in the case of crowd-sourced
auditing (Sandvig et al., 2014). Thus, several past studies have employed this
audit method (Asplund et al., 2020; Hussein et al., 2020; Bandy and
Diakopoulos, 2020; Trielli and Diakopoulos, 2019; Bandy and Diakopoulos, 2021;
Juneja and Mitra, 2021). However, in sock-puppet auditing, the bot histories
are built in very conservative settings that do not emulate real-world users’
complex account histories (Juneja and Mitra, 2021). Thus, as an alternative,
scholars have collected and audited algorithmic outputs from real-world users
to study and identify problematic algorithmic behaviors in users’ naturalistic
settings (Robertson et al., 2018b, a; Bisbee et al., 2022; Bandy and
Diakopoulos, 2020; Venkatadri et al., 2019). We add to the existing crowd-
sourced audit studies by conducting a crowd-sourced audit of YouTube to
measure the amount of election misinformation in the searches, and
recommendations of real-world users. In our study, we use a list of pre-
selected videos and search queries to collect data from users’ YouTube
accounts to test whether users’ existing account histories could lead them to
misinformative content on the platform. In the next section, we present the
audits conducted specifically on YouTube and discuss how our work adds to the
growing literature on platform audits.

###  2.2. Auditing YouTube for problematic content

Given the popularity of YouTube and the criticism the platform has faced for
not regulating problematic content, several scholarly studies have audited
YouTube’s search and recommendation algorithms for the prevalence of
misinformation, extremism, and echo chambers of problematic content. Sock
puppet audits on YouTube revealed that while the platform’s channel
recommendations radicalize users by recommending extreme channels (Ribeiro et
al., 2020), video recommendations drive users away from radical content by
recommending videos from mainstream news channels (Huszár et al., 2022). A
crowd-sourced audit further revealed that real users with high prior levels of
racial resentment get more exposure to extremist content since they typically
subscribe to extremist channels (Chen et al., 2022). In another line of
inquiry, several studies audited YouTube for conspiracy theories (Sanna et
al., 2020; Hussein et al., 2020; Faddoul et al., 2020; Papadamou et al.,
2022). Notably, first such audit on YouTube was conducted by Hussein et al
(Hussein et al., 2020). This audit revealed the prevalence of echo chambers of
misinformation in YouTube’s top-5 video recommendations for topics such as the
moon landing, 9/11 conspiracies, etc. (Hussein et al., 2020). Recently,
Tomlein et. al re-conducted the audit performed by Hussein and Juneja et al
(Hussein et al., 2020) and found that video recommendations for topics like
9/11 conspiracies have worsened on the platform (Tomlein et al., 2021).
Another study (conducted in the fall of 2020), closest to this work collected
real-world YouTube recommendations for election fraud videos by asking users
to manually click on recommendations following certain traversal rules (Bisbee
et al., 2022). The study aimed at proving that users skeptical about the
legitimacy of elections receive more voter fraud videos in their
recommendations. On the other hand, we audit YouTube’s searches, homepages,
and default algorithmic pathway (up-next videos that are auto-played by the
platform) of users with different political leanings and investigate how its
algorithm fares under different conditions (watching videos of different
stances) for the same individual. Additionally, we conduct the audit two years
after the presidential election event. Post hoc auditing of the platform
allows us to determine how well the platform has enacted its content policies
and regulated harmful content.

##  3\. Methodology

###  3.1. Developing search queries to measure election fraud based
misinformation

The first methodological step in any algorithmic audit is to determine a
viable set of relevant search queries that would be used to probe the
algorithmic system. For our study, we identified search queries that satisfy
two properties. First, we select high-impact search queries that were used by
people to search about Presidential Election as well as the voter fraud claims
about the 2020 elections. Second, we curate search queries that have a high
probability of returning misinformative results which would result in
meaningful measurements of algorithmically curated misinformation about the
audit topic. To compile such queries, we used Google Trends and YouTube video
tags (refer Figure 1).

Figure 1. Figure illustrating our method to curate search queries for our
audit experiment

The figure illustrates the two methods to curate search queries for the audit.
First, we use high-impact queries from Google Trends. Second, use relevant
YouTube video tags from YouTube videos that were shared by users promoting
voter fraud claims on Twitter.

####  3.1.1. Curating high-impact queries via Google Trends

First, we leveraged Google Trends which contain Google’s daily and real-time
search trends data. As the most popular search service, its trends are a good
indicator for understanding the real-world search behavior of a large number
of people. Using Election Fraud 2020 and Presidential Election as search
topics, United States as location, April 2020 to Present as date range, and
search service as YouTube search, we extracted the top 15 most and least
popular search queries that people used on YouTube. We choose April 7 as the
start date since this was the day when Donald Trump made one of his first
fraudulent claims about the security of mail-in ballots (Inskeep, 2022). We
included the most popular queries since they represent the ones that people
mostly use to get information on elections. To explore the data-voids
(Golebiewski and Boyd, 2019) associated with our audit topic, we also included
the least popular search queries to determine if those terms have been
hijacked by conspiracists to surface misinformation.

Figure 2. List of video tags associated with YouTube video titled Is Voter
Fraud Real? (video id: RkLuXvIxFew) that promotes voter fraud misinformation.
Video tags are added by content creators while uploading YouTube videos on the
platform. The tags can be extracted from videos via YouTube APIs or third-
party tools. We use tags associated with videos shared by users promoting
voter fraud claims on Twitter as search queries in our audit experiments.

The figure shows a list of video tags associated with the YouTube video titled
Is Voter Fraud Real?, such as election tampering, non-citizen voters, threat
to democracy, ballot harvesting, etc.

presidential election 2020
---
us elections 2020 latest news
election fraud 2020
rigged election
dominion voting exposed
mail in ballots 2020
stop the steal
joe biden voter fraud
usps whistleblower
voter fraud evidence
trump biden general election
dominion voter fraud

Table 1. Sample search queries for our YouTube audit

####  3.1.2. Curating misinfo-queries queries using YouTube video tags

Second, we used YouTube video tags that content creators associated with
misinformative videos while uploading them on the YouTube platform (see Figure
2 for an example). These tags could be thought of as search words representing
how content creators would like their videos to be discovered. To extract
video tags associated with election misinformation videos, we leveraged a
large-scale Voter Fraud 2020 dataset released by Abilov et al (Abilov et al.,
2021). The dataset contains over 12,002 YouTube video URLs that were shared on
Twitter by accounts that tend to refute and promote voter fraud claims. We
extracted YouTube video tags associated with videos shared by accounts
promoting voter fraud claims to probe YouTube (n=200K). To curate a viable
number of search queries from the extracted video tags, we employed several
steps. First, we manually curated a list of 10 keywords related to elections
and fraudulent claims surrounding the elections222 steal, fraud, ballot,
elect, seal, dominion, sharpiegate, whistleblower, harvest, and sunrise zoom
from the list of keywords provided by Abilov et al (Abilov et al., 2021) as
well election 2020 misinformation report produced by the Election Integrity
Partnership (for an Informed Public et al., 2021). Then for each of the
keywords, we extracted 15 top and 15 least occurring video tags containing
that term. For example, one of the most occurring tags containing keyword
whistleblower was ‘usps whistleblower’ while the least occurring tag was
‘whistleblower jesse morgan’.

####  3.1.3. Filtering search queries to obtain the final set

We combined search queries obtained from both Google Trends and YouTube video
tags in our final query set and employed several filtering steps to obtain a
reasonable number of relevant search queries. First, we only kept queries
related to the election 2020, for example, we kept ‘election fraud 2020’ and
removed ‘election fraud 2016’. We removed duplicate and redundant search
queries and replaced them with a single randomly selected query. For example,
we replaced queries ‘voter fraud 2020’, ’voter fraud’, and ‘vote fraud’ with
‘voter fraud 2020’. We removed queries with lengths greater than five since
they were overly specific (e.g. ‘we’ve got pictures of the check stubs paid to
people to ballot harvest’). We also removed queries containing names of news
channels, news anchors, and presidential candidates because they were too
generic and not directly related to the audit topic. However, we kept the
search queries where the names of the presidential candidates were together
with the election or election fraud-related terms (e.g. ‘Joe Biden voter
fraud’). We also removed search queries that were in languages other than
English. Finally, we had 88 search queries in total. Table 1 presents a
sample.

Figure 3. Figure illustrating our method to curate seed videos for our audit
experiment

The figure illustrates the method used to curate seed videos for the audit
experiment that is described in detail in the Section titled “determining
popular seed videos to collect up-next video trails”

Annotation label |  Video title |  Video id
---|---|---
Supporting election fraud misinformation |  Poll worker gives his account of what happened when he tried to monitor the vote in Nevada |  4X2V5hPPp6w
Joe Biden says he’s built most extensive ”voter fraud” org in history |  WGRnhBmHYN0
Neutral |  Ex-Trump official shares his prediction if Trump loses 2020 |  KuqhhrmhfCI
’Don’t be ridiculous’: Rudy Giuliani learns about Biden win from reporters |  Z0hEFa52Bdo
Opposing election fraud misinformation |  Voting by Mail: Last Week Tonight with John Oliver (HBO) |  l-nEHkgm_Gk
Trump and the GOP Still Refuse to Accept Biden’s Win: A Closer Look |  QoPA3unjQgA
Table 2. Sample seed videos curated for the audit experiment.

###  3.2. Determining popular seed videos to collect up-next video trails

The second step of our audit experiment is to curate YouTube videos that would
act as seed videos to collect the up-next video recommendation trails. We
again leveraged Abilov et al’s YouTube video dataset (Abilov et al., 2021).
Recall, the authors identified clusters of Twitter users who either shared
tweets promoting or detracting from voter fraud claims and released the
YouTube videos related to election fraud 2020 shared by those users. At the
the time of analysis, out of the $\sim$12K videos present in the dataset, 8.9K
were present on YouTube. The remaining videos were either removed or made
private. Out of the videos that were still present, 1K videos were shared by
users in the detractor cluster, 6.5K videos were shared by users in the
promoting cluster, and the rest were shared by users who were suspended from
Twitter. We sampled 445 videos that had accumulated the maximum number of
views from both the promoting and detracting clusters (890 in total). Since
the videos were not annotated by the authors for misinformation, we could not
assume that videos shared by users in the promoting cluster would contain
misinformation. Therefore, we conducted an intensive and iterative process to
determine the labels and heuristics for annotating the YouTube videos for
misinformation. We describe the process in detail in Section 3.6. Through the
annotation process, we labeled the videos as supporting, neutral, or opposing
election misinformation. Out of the 890 videos, 74 were opposing, 16 were
neutral, 101 supported election misinformation while remaining were
irrelevant. We selected the top 15 videos that had accumulated maximum
engagement, determined by the number of views, for each stance (except the
irrelevant) as seeds. Figure 3 illustrates the seed video curation method.
Table 2 presents a sample of seed videos.

Figure 4. Figure (a) presents an overview of our crowd-sourced audit of
YouTube for election misinformation, Figures (b) and (c) show how our
extension TubeCapture collected YouTube components from both standard and
incognito windows simultaneously.

Figure (a) presents an overview of our crowd-sourced audit of YouTube for
election misinformation, Figures (b) and (c) illustrate how our extension
TubeCapture collected YouTube components from both standard and incognito
windows simultaneously.

###  3.3. Experimental design

To conduct the crowd-sourced audit, we designed a chrome browser extension
named TubeCapture that enabled us to watch videos, conduct searches, and
collect various YouTube components from users’ browsers. Figure 4 presents an
overview of our experimental design. To select the study participants, we
conducted a screening survey of a large sample of people (details in Section
3.4). Next, participants were instructed on how to use TubeCapture and
provided with a unique code to activate the extension. Once activated, they
used TubeCapture for a period of 9 days. We seeded our extension with 45 seed
videos and 88 search queries. For each participant, each day the extension
opened YouTube in two browser windows, one standard window and one incognito
window. While the personalized results act as treatment for our experiments,
results obtained from incognito act as control since YouTube does not
personalize content in the incognito browsing window (YouTube, 2022a). By
comparing the results from standard and incognito windows, we determine the
role of YouTube’s personalization algorithms in exposing users to
misinformative content.

TubeCapture first collected and stored the user’s YouTube homepage from
standard and incognito windows. The extension ensured that the user had signed
in to their YouTube account in the standard window and remained logged in
using the same YouTube account throughout the study period. We also ensured
that the homepage from the standard window is stored without the user’s email
address to ensure the participant’s anonymity. Next, the extension opened a
seed video (previously selected) that supports election misinformation,
watched it for 2 minutes, saved the video page, clicked on the up-next video,
and again saved the video page of the up-next video. This process was repeated
until we collected 5 levels of up-next recommendations’ video pages. We refer
to the collection of 5 up-next video recommendations as up-next trails. Each
day we collected up-next trails for five seed videos. Then, the extension
again collected the user’s homepage followed by personalized (via standard
window) and unpersonalized (via incognito window) search results for the
curated search queries. The extension collected the search results for queries
in the same order for every participant to control for carry-over effects of
the search queries (Hannak et al., 2013). For days 1-3, the extension
collected up-next trails for seed videos supporting election misinformation.
At the beginning of the fourth day, the extension deleted the search and watch
history created by the browser extension. According to YouTube, removing an
item from search or watch history removes the impact of consuming that content
on future searches and recommendations. This essential step helped us in two
ways- 1) it ensured that the history created by our extension in the first
three days does not impact the rest of the experiment, and 2) it also ensured
that the user histories built by our extension did not pollute users’ future
recommendations and search results after the study period is over. For days
4-6, the extension collected up-next trails for seed videos that were neutral
in stance. At beginning of the seventh day, again search and watch history
developed by the extension was deleted. For days 7-9, the extension collected
up-next trails for opposing seed videos. Towards the end of the 9th day, we
again deleted the YouTube history developed by the extension. All the data
collected by the extension was sent to a back-end server. The participants
were instructed on how to remove the extension after the study period was
over. Our current mixed design allows us to test how YouTube’s algorithm fares
under different conditions—watching videos of different stances—for
individuals with different political beliefs. Note that we did not opt for a
randomized assignment in a between-subject design since it would require a
large number of participants to test all the conditions (3 political
affiliations X 3 misinformation stances).

We built the YouTube capture extension using JavaScript libraries. The back-
end server was set up using Flask and Nginx. We load-tested the server using
Jmeter and ensured that the server could simultaneously handle 500 GET and 200
POST requests and added mechanisms to handle errors and server timeouts. We
used a MySQL database for storing the data collected using the extension. The
communication between the extension and our back-end server was encrypted
using SSL. Note that to collect data, TubeCapture opened windows in the
background of the currently active browser window, thereby allowing
participants to continue working on their device while the extension is
running. In case, the participant accidentally closed any of the windows
opened by our extension, we informed users via a pop-up window and instructed
them on how to resume running the extension.

After building the TubeCapture extension, we tested it with our research group
and conducted three pilot studies. The aim of the pilot studies was to fix
technical issues, examine the impact of running the extension on devices with
different configurations, RAM, and operating systems as well as improve the
usability of the extension.

###  3.4. Screening and study survey

In order to select participants for our study, we screened users according to
several criteria. To be eligible for the study, users should be 1) 18 years of
age or older, 2) reside in the United States, 3) have a YouTube account, 4)
consume content on YouTube primarily in the English language, 5) have a chrome
browser installed, 6) willing to run a chrome browser extension for 9 days and
7) have at least 8GB RAM on their device to ensure smooth running of the
extension333We warned users against participating in the study if their
device’s RAM is less than 8GB and informed them that their device or browser
might hang in such a situation. The users who qualified for the screening
survey were sent another study survey. The study survey contained questions
about users’ demographics, political affiliation, YouTube usage, trust in
online information, their opinion on personalization and bias in various
components of YouTube, and their view on the results of the presidential
elections 2020 as well as conspiracies surrounding the elections. We also
included two attention-check questions. The study survey was also used for
screening participants. We disqualified users who 1) answered both attention
check questions incorrectly, 2) did not frequently use YouTube, and 3) did not
use YouTube to access news or information about the 2020 presidential
elections. We also used the survey responses to obtain a balanced number of
participants across three political affiliations (Democrats, Republicans, and
Independents). Later in the recruitment phase, we had enough democrats and
independents as participants and thus, added being a republican as a
qualifying criterion in the study survey.

###  3.5. Recruitment and study deployment

For our pilot studies, we recruited users from a combination of platforms such
as Reddit444https://www.reddit.com/r/SampleSize/, Facebook ads, Twitter, and
Amazon Mechanical Turk (AMT). The retention rate was highest for participants
recruited from Twitter and AMT. Thus, we used these two platforms to recruit
participants for the main study. The pilots and the main study were approved
by our university’s Institutional Review Board. Out of the 575 users who
submitted the screening survey, 400 qualified, and 99 participated in the
study. Out of the 99 participants, 94 ran the extension for the entire study
duration. Overall, our study sample of 99 users constituted of 60.6% males and
39.39% females, was predominantly White/Caucasian (60.6%) and the majority
(53.53%) of the participants had a bachelor’s degree. Politically, 39.39% of
our participants were Democrats, 34.34% independents, and 26.26% Republicans.
Based on the results of 2020 presidential
elections555https://www.politico.com/2020-election/results/president/, 66.67%
of our participants lived in the blue states, 32.32% in red while one
individual resided in Puerto Rico666Puerto Rico is not considered a state but
is considered an unincorporated territory of the United States. We report
additional participants’ characteristics in Appendix A.1.

###  3.6. Developing data annotation scheme

Developing the qualitative coding scheme to label YouTube videos for election
misinformation was hard and time-consuming, requiring four rounds of
discussions and consultation with an expert to reach a consensus on the
annotation heuristics. In the first round, the first author and an
undergraduate research assistant sampled 196 YouTube videos from Abilov et
al’s YouTube dataset (Abilov et al., 2021) and separately annotated the
videos. They considered prior work on election misinformation narratives (for
an Informed Public et al., 2021) and YouTube content policy (YouTube, 2022b)
as references to identify election misinformation, and came up with an initial
annotation scale and heuristics to classify videos. Then they came together to
reach a consensus on the annotation values. However, even after multiple
rounds of discussions, annotations diverged for 33.6% of the videos. We then
conducted additional rounds of annotation exercises with seven researchers,
out of which five had extensive work experience on online misinformation. In
every round, researchers independently annotated 15 videos and later discussed
every video’s annotation value and the researchers’ annotation process. We
also reached out to a postdoctoral researcher who has extensive research
experience on online multi-modal election misinformation for feedback. Based
on the insights provided by the external researchers and postdoc, we refined
the annotation criteria and heuristics 777It is important to note that all
annotators and the post-doctoral researcher are left and center-left leaning
individuals which may have affected how the content of YouTube videos was
perceived and how the annotation heuristics were developed.. Below we describe
the annotation guidelines and heuristics in detail.

####  3.6.1. Annotation guidelines

In order to annotate a YouTube video, the annotators were required to go
through several fields present on the video page in the following order: title
and description, the overall premise of the video which could be determined by
going through the video transcript or watching the video content, and
considering channel bias. We encouraged participants to perform an online
search to gain more contextual information about events or individuals
discussed in the video that they were unaware of. This strategy is grounded in
the lateral reading technique that is often used by fact-checkers for
credibility assessments (Wineburg and McGrew, 2017). Note that we did not ask
participants to consider video comments for the annotations because we found
during our annotation exercises that comments could be misleading. For
example, video Dominion Voting Systems representative demonstrates voting
machines (Q7kPSzYsR6Y) contains a demonstration of dominion voting machines,
however, the comments indicate the video to be supporting misinformation.

####  3.6.2. Annotation heuristics

In this section, we describe our annotation scale and heuristics.

Supporting election misinformation (1): This category includes YouTube videos
that support or provide evidence for misleading narratives around the
presidential elections. We did not include videos showing incidents of mail
dumping, destroyed ballots, etc. in isolation. However, if the videos use
these incidents to push a specific narrative/agenda like undermining
confidence in mail-in voting, then we considered them as supporting
misinformation. We also considered live YouTube videos (live press
conferences, court hearings, etc.) that highlighted voter fraud claims without
giving any additional context in the title, description, or beginning of the
video as supporting misinformation. A few examples of videos in this category
include NO RETREAT! America Is About To #StopTheSteal — Good Morning #MugClub
(Xqcwzi8Onsk) where video’s title, description, and content hint towards
massive voter fraud incidents in the US 2020 presidential elections and LIVE:
Trump Legal Team Presents CLEAR Evidence of Fraud Before Georgia Senate
Committee 12/3/20 (e35f4pUIYOg) which contains live footage capturing the
testimony of individuals claiming occurrence of voter fraud in 2020
presidential elections. The video’s description, title, and beginning do not
contain any statements questioning or contradicting the claims of widespread
voter fraud.

Neutral (0): We consider videos as neutral when they are related to the 2020
elections but do not support or oppose false narratives surrounding the
elections. For example, video WATCH: The first 2020 presidential debate
(w3KxBME7DpM) is considered neutral since it covers the first presidential
debate of the elections.

Opposing (-1): We annotate videos as opposing when they oppose or debunk the
misinformation narratives behind the 2020 US presidential elections. We also
include satire videos making fun of the misinformative claims in this
category. For example, video Trump Has Yet To Show Real Evidence Of Fraud, But
Getting Him Out Of Office May Be A Bumpy Ride  (7mJwuKhfvqY) whose title and
description indicate that Donald Trump made false claims of massive voter
fraud.

Other annotations: We mark a video as Irrelevant (2) if its content is not
related to the presidential elections, as URL not accessible (3) if the
YouTube video was not accessible at the time of annotation and as Other
languages (4) when the content, title, or description of the YouTube video was
in a language other than English.

###  3.7. Classifying YouTube videos for election misinformation

Our crowd-sourced audit experiments resulted in $\sim$47K unique YouTube
videos and 35 unique YouTube shorts888YouTube shorts are short YouTube videos
with lengths equal to or less than 60 seconds. Given a large number of videos,
we scaled the annotation process using a machine learning classifier. In this
section, we present our method of creating the ground truth dataset, a
description of features used in our classification model, model architecture,
and the results of our classification.

####  3.7.1. Creating a ground truth dataset

Two researchers manually annotated 1196 videos using the guidelines and
heuristics mentioned in Section 3.6. We obtained annotations for 545
additional videos using AMT. We describe the process of obtaining video
annotations from AMT workers in Figure 16 and Appendix A.2. Overall, in our
ground truth dataset, we had 1741 videos out of which 124 are supporting999Out
of these 67 videos were removed from the platform at the time of analysis.,
257 opposing, 228 neutral, and 1132 irrelevant videos.

####  3.7.2. Feature description

We considered the following features for our classifier.

Snippet (title+description): We concatenated the title of the YouTube video
with its description together, as done by (Papadamou et al., 2022), and used
the concatenated string as a feature.
Transcript: Transcript contains the textual content of the video. We use
transcripts auto-generated by YouTube.
Tags: Video tags are words that a content creator associates with their video
while uploading it on the platform.
Video Statistics: Video statistics include the number of views, likes,
comments, and date of publication.
Channel Bias: Since the election misinformation is closely entangled with the
political beliefs (Bump, 2021; Macdonald and Brown, 2022), we used partisan
bias of YouTube channels as a feature. Using existing data sets on media bias
and manual annotations (described in Appendix A.3), we annotated YouTube
channels’ partisan bias on a 5-point scale of far-left to far-right.

Apart from the features listed above, we also tried several other features
like LIWC dictionary (Tausczik and Pennebaker, 2010), Credibility Cues (Mitra
et al., 2017), and hashtag matching from the Voter Fraud dataset on the text
features (Abilov et al., 2021) that didn’t improve performance. Therefore, we
do not discuss them in detail. Recall, while manually annotating the videos,
we discovered that comments are not a good indicator of the veracity of the
video. Therefore, we chose not to include those in our feature set.

Classifier[Feature + Vectorizer + Imbalance Handling + Data] |  Acc. |  F1
---|---|---
SVM[Video Engagement Statistics] |  0.38 |  0.14
SVM[Snippet + FastText] |  0.61 |  0.56
SVM[Transcript + FastText] |  0.58 |  0.51
SVM[Tags + FastText] |  0.59 |  0.53
SVM[Snippet,Transcript,Tag + FastText] |  0.63 |  0.57
SVM[Snippet,Transcript,Tag + Count] |  0.65 |  0.58
SVM[Snippet,Transcript,Tag + TFIDF] |  0.71 |  0.65
SVM[Snippet,Transcript,Tag,Channel Bias + Sentence Transformer] |  0.73 |  0.69
SVM[Snippet,Transcript,Tag,Channel Bias + TFIDF] |  0.74 |  0.70
SGD[Snippet,Transcript,Tag,Channel Bias + TFIDF] |  0.64 |  0.57
KNN[Snippet,Transcript,Tag,Channel Bias + TFIDF] |  0.61 |  0.58
XGB[Snippet,Transcript,Tag,Channel Bias + TFIDF] |  0.74 |  0.68
Voting SVM+SGD+KNN+XGB [Snippet,Transcript,Tag,Channel Bias + TFIDF] |  0.75 |  0.71
SVM[Snippet,Tag,Channel Bias + TFIDF + SMOTE + Additional Training Data] |  0.91 |  0.90
XGB[Snippet,Tag,Channel Bias + TFIDF + SMOTE + Additional Training Data] |  0.91 |  0.91
Table 3. A sample of classifiers and feature set with the performance
progression.

####  3.7.3. Classifier Selection

To find a classifier that performs well on our dataset, we applied a series of
machine learning classifiers on several combinations of feature sets. To
create feature vectors, we tested two types of word vectors (count and tf-idf
vectors) and two types of sentence vectors (FastText
101010https://fasttext.cc/ and BERT (Devlin et al., 2018)). For word vector
generation, we cleaned the dataset by removing stop words and lemmatization,
followed by up to 3-gram generation. To deal with data imbalance in our
dataset, we used Synthetic Minority Over-sampling Technique (Chawla et al.,
2002) We applied several classifier models on our feature set including
support vector machine, stochastic gradient descent, decision trees, nearest
neighbor, and ensemble models. To find the best model, we performed a grid
search on a five-fold cross-validation dataset by looking into standard
parameter space for each classifier. For the sake of brevity, we only show a
sample of combinations tested in Table 3. Out of all the combinations, both
SVM and XGBoost performed the best (ACC=91%) when trained with snippet, tags,
and channel bias features and tf-idf text vectorizer 111111If we merge
irrelevant and neutral videos into one class resulting in a three-class
classification problem, SVM classifier performs with a 93% accuracy.. Based on
Occam’s Razor principle (Wikipedia contributors, 2022), we selected SVM as the
final classifier, i.e., the simplest model with maximum accuracy. Using our
final classifier, we determined the annotation labels for the remaining
videos. In total, our dataset consisted of 431 supporting, 1868 opposing, 1658
neutral, and 43041 irrelevant videos.

##  4\. Ethical considerations

Our browser extension TubeCapture uses crowd workers’ YouTube account to watch
videos (including videos containing election misinformation) and conduct
searches on the platform. It was possible that participants would have seen
more misinformation than they would have otherwise during and also after the
research study due to the watch and search history built during the audit. In
order to eliminate the potential harm of our experiments, we included two
essential steps in our experimental design. First, our extension always opened
the browser window in the background so that participants don’t actively see
the videos being played. Second, the extension deleted users’ search and watch
history built during the study period. Note that YouTube allows the deletion
of items from the search and watch history for a specific date range.
YouTube’s website (YouTube, 2022c; Viewers, 2022) clearly states that “search
entries you delete will no longer influence your recommendations. At any time
you can (also) remove videos (from watch history) to influence what YouTube
recommends to you”. We explicitly informed users that their YouTube history
during the study period would be deleted. We ensured that the extension
expires after the study period so that it does not perform any action. In
addition, we ensured that the YouTube pages saved by our extension do not
contain users’ personally identifiable information such as email addresses.

##  5\. RQ1 Results: Extent of Personalization

To measure the extent of personalization in YouTube components, we compare the
personalized list of video URLs present in the standard window with the
baseline unpersonalized videos obtained from the incognito window. Below we
discuss the metrics that we used to quantify personalization.

Measuring personalization in web search: In our study, to determine
personalization in search results, we employ two metrics: jaccard index and
rank bias overlap (RBO). Jaccard index measures the similarity between two
lists and has been used in several previous audit studies to measure
personalization in web search (Kliman-Silver et al., 2015; Hannak et al.,
2013; Juneja and Mitra, 2021). However, Jaccard index does not take into
account the rank of the lists being compared. Thus, we used the RBO metric
introduced by Webber et al (Webber et al., 2010) which takes into account the
order of elements in the list. The RBO function includes a parameter p which
indicates the top-weightedness of the metric, i.e. how much will the metric
penalize the difference in the top rankings. A previous audit study used the
click-through rate (CTR) of Google search results to estimate the value of p
(Robertson et al., 2018b). Because of the lack of CTR statistics available for
YouTube, we consider the default value of p which is 1 (prior audit studies
such as (Le et al., 2022) opted for a similar approach), indicating that
differences in all rankings are equally penalized. Both jaccard and RBO scores
range between 0 and 1, with 1 indicating that the two lists have similar
elements while 0 indicating that the lists are completely different.

Measuring personalization in up-next trails: To measure personalization in up-
next trails, we employ jaccard index and Damerau-Levenshtein (DL) distance
(Damerau, 1964). DL distance is the enhanced version of edit distance that
computes the number of transpositions in addition to insertions, deletions,
and substitutions required to make the treatment list identical to the control
list. DL distance has been used by prior audit work as a metric to estimate
the ranking differences between two lists (C. dos Santos et al., 2020). It
returns a score from 0 to 1 (identical lists) indicating how similar the two
lists are. We refrain from using the RBO metric to determine personalization
in up-next trails because RBO is suitable for indefinite lists while the
trails collected through our experiments have a known maximum length of five.
We also refrain from using the Kendall tau metric since it requires the two
ranked lists being compared to be conjoint121212There are alternative versions
of Kendall Tau that assume the dissimilar elements to be present at the end of
the list. However, conceptually, the metric does not fit our collected trail
data.. Given, jaccard, RBO, and DL distance return similarity values, we
define personalization as:-

(1) $1-similarity\\_metric(URL_{incognito},URL_{standard}).$

###  5.1. RQ1a: Personalization in search results

When asked in our study survey how much YouTube personalizes search results
(Figure 5(a)), 34.34% believed YouTube personalizes search results to a great
extent while 19.19% believed the extent of personalization to be very little.
On quantitatively measuring the extent of personalization in YouTube search
results, we found little to no personalization indicating that search results
present in standard and incognito windows are highly similar. Figures 5(b) and
5(c) show the extent of personalization in SERPs calculated using jaccard
index and RBO metric respectively for democrats, republicans, and independents
for each day of the experiment run. We did not find any significant difference
in the personalization values of SERPs for participants with respect to their
political leaning.

(a) Participant’s belief in extent of personalization in YouTube search
results

(b) Measuring extent of personalization in SERPs using jaccard index

(c) Measuring extent of personalization in SERPs using RBO

Figure 5. RQ1a results: Figure (a) shows participants’ response to the survey
question: “How much, if at all, do you think YouTube personalizes search
results”. Figures (b) and (c) show personalization calculated via jaccard
index values and RBO metric values respectively in YouTube’s standard-
incognito SERP pairs. We observe that search results are slightly personalized
meaning search results obtained from standard windows are very similar to the
search results obtained from incognito windows.

Figure (a) shows participants’ response to the survey question: “How much, if
at all, do you think YouTube personalizes search results”. 19.2% believe there
is little personalization in YouTube search results, 46.5% believe that search
results are somewhat personalized, and 34.3% believe that YouTube personalizes
search results to a great extent. Figures (b) and (c) show personalization
calculated via jaccard index values and RBO metric values respectively in
YouTube’s standard-incognito SERP pairs for democrats, republicans, and
democrats. The figures are line graphs with the x-axis showing the nine days
of the experiment run (day0-day8). The y-axis shows the magnitude of
personalization. The magnitude of personalization is very low (near 0) for all
days for all users.

(a) Participant’s belief in extent of personalization in YouTube up-next
recommendations

(b) Distribution of percentage of up-next video recommendations coming from
users’ subscribed channels.

(c) Measuring extent of personalization using jaccard index

(d) Measuring extent of personalization using DL index

Figure 6. RQ1b results: Figure (a) shows participants’ response to the survey
question: “How much, if at all, do you think YouTube personalizes up-next
recommendations”. Figure (b) shows the distribution of the percentage of
YouTube videos recommended to our study participants from their subscribed
channels. Figures (c) and (d) show personalization calculated via jaccard
index values and DL distance metric values respectively in YouTube’s standard-
incognito up-next trails pairs. We observe that up-next recommendation trails
are highly personalized.

Figure (a) shows participants’ response to the survey question: “How much, if
at all, do you think YouTube personalizes up-next recommendations”. 9.1%
believe there is little personalization in YouTube’s up-next recommendations,
39.4% believe that up-next recommendations are somewhat personalized, and
51.5% believe that YouTube personalizes up-next recommendations to a great
extent. Figure (b) shows the distribution of the percentage of videos
recommended to our participants in up-next trails that are coming from their
subscribed channels. For around 50% of users, 10% or fewer videos come from
their subscribed channels. Figures (c) and (d) show personalization calculated
via jaccard index values and DL distance metric values respectively in
YouTube’s standard-incognito up-next trails pairs. The personalization value
is very high (between 0.8-1) for the up-next trails collected from all the
users.

###  5.2. RQ1b: Personalization in up-next trails

When asked how much YouTube personalizes up-next recommendations, 51.5% of
participants believed that YouTube personalizes up-next recommendations to a
great extent (refer Figure 6(a)). The quantitative measurements are in line
with this belief showing that up-next trails are highly personalized. Figures
6(c) and 6(d) show the extent of personalization in up-next trails using
jaccard index and DL distance. The graphs indicate that the up-next trails
obtained from the users’ standard and incognito windows are highly dissimilar
and thus, highly personalized. Statistical test revealed that the amount of
personalization in trails with supporting, neutral, and opposing seeds is
significantly different [F(2)=15.2, p¡0.0001]. Post hoc test revealed that up-
next trails with seed videos opposing misinformation have lesser
personalization (higher jaccard index131313The jaccard index values obtained
were highly correlated with DL distance scores (pearson correlation
coefficient = 0.96). Thus, we used jaccard index values to perform the
statistical test.) when compared with up-next trails with supporting and
neutral seed videos.

Next, we checked the influence of users’ subscriptions on personalized trails.
81 (out of 99) participants had subscribed to at least one YouTube channel
(mean=109.4, median=31, SD=207.8). The maximum number of subscriptions for a
participant was 1073 and the minimum was 1. The participants had subscribed to
7670 unique channels out of which 79 either did not exist or were suspended
due to violation of YouTube’s moderation policy and thus, we did not consider
these channels for analysis. To determine how many video recommendations in
users’ up-next trails were coming from their subscriptions, first, for each
user we extracted the unique videos recommended in all the up-next trails
collected for the user. Then we filtered and calculated the number of videos
coming from the users’ subscribed channels. Figure 6(b) shows the distribution
of the percentage of videos recommended to our participants in up-next trails
that are coming from their subscribed channels. This percentage value is
moderately correlated with the number of channels subscribed (r=0.61) and
highly correlated with the number of news-related channels subscribed141414To
get a rough estimate of YouTube channels that broadcast news, we considered
the news sources from mediabiasfactcheck.com and allsides.com. Additionally,
we extracted the description of each channel and categorized it as a news
channel if the description contained terms such as ‘breaking news’,
‘politic*’, ‘current affairs’, ‘government’, ‘national tv’, ‘national news’,
‘international news’, ‘world news’, ‘global news’, ‘current affairs’, ‘wall
street’ etc. These terms were curated by the first author after manually going
through the description of 50 national and regional news channels on YouTube.
We found that 44 users had subscribed to news and politics-related
channels.(r=0.71).

##  6\. RQ2 Results: Amount of Misinformation

When asked how much do participants trust the credibility of videos in search
results and recommendations, less than 20% reported that they trust the
credibility of content shown to them by YouTube to a great extent (Figure 7).
To determine how much credible information is presented by YouTube to users in
reality, we quantify the misinformation present in the YouTube components by
adopting the misinformation bias score developed by Hussein and Juneja et al
(Hussein et al., 2020). The score determines the misinformation in ranked
lists and is calculated as
$\frac{\sum_{r=1}^{n}{(x_{r}*(n-r+1))}}{\frac{n*(n+1)}{2}}$; where x is the
video annotation, $r$ is rank of the video, and $n$ is the total number of
videos present in the SERP/up-next trail. To conform to the video annotation
scale in (Hussein et al., 2020), we map our annotation values to a normalized
scale of -1, 0, and 1. We assign scores of -1 and 1 to videos opposing and
supporting election misinformation respectively. Videos marked as irrelevant,
neutral, belonging to a non-English language, or removed from the platform are
assigned a 0 score. Thus, the misinformation bias score of a SERP/trail is a
continuous value ranging between -1 (all videos are opposing election
misinformation) to +1 (all videos are supporting election misinformation).
Note that a positive score indicates a lean towards misinformation, while a
negative score indicates a lean towards content opposing misinformation. For
analysis, we consider the top ten search results and five consecutive videos
in the up-next trails.

(a) Participant’s trust in the credibility of information presented in search
results

(b) Participant’s trust in the credibility of information presented in up-next
recommendations

Figure 7. RQ2: Figure showing participants’ response to survey question: “How
much do you trust the credibility of information present in the ” a) search
results and b) up-next videos recommended by YouTube.

Figure (a) shows participants’ responses to the survey question: How much do
you trust the credibility of information present in the search results? 4% say
not al all, 16.2% say very little. 60.6% say somewhat, and 19.2% say to a
great extent. Figure (b) shows participants’ responses to the survey question:
How much do you trust the credibility of information present in the up-next
videos recommended by YouTube? 7.1% say not al all, 18.2% say very little.
60.6% say somewhat, and 14.1% say to a great extent.

###  6.1. RQ2a: Misinformation in search results

The results of RQ1 showed that YouTube’s SERPs are very slightly personalized
suggesting that search results present in the standard and incognito windows
are mostly similar. Therefore, to quantify the misinformation bias in SERPs we
only consider the SERPs obtained from the standard YouTube windows of all the
participants. We first calculated the average misinformation bias score for
each of the 88 search queries for 9 days of the experiment run across all 99
participants. Figure 8 shows the distribution of misinformation bias scores
for all the search queries. We observe that the average misinformation bias
scores of 84 (out of 88) search queries are negative indicating that the
search results contain more videos that oppose election misinformation as
compared to videos supporting election misinformation151515Only four search
queries in our query set (‘stop the seal’, ‘voting machine fraud’, ‘ballots in
garbage’ and ‘ballots thrown out’) have a positive misinformation bias. .

Figure 8. RQ2a results: Mean misinformation bias scores for 88 search queries
for all participants. A negative score indicates that SERPs contain more
videos opposing election misinformation.

The figure shows the distribution of misinformation bias scores for 88 search
queries for all participants. The majority of search results have a negative
score indicating that SERPs contain more videos opposing election
misinformation.

Cluster1: Search queries containing keyword fraud in conjunction with keywords
voter, election, and dominion
---
voter fraud evidence, dominion voter machine scandal, sharpie voter fraud,
election fraud 2020, election fraud whistleblower
Cluster2: Search queries containing keywords election, and 2020
trump biden general election, presidential election 2020, presidential
election results 2020, mail in ballots 2020

Table 4. The misinformation bias scores form a bimodal distribution, each
constituting a cluster of similar queries. This table describes the clusters
and presents sample queries for each cluster.

(a) Search queries with highest and lowest mean misinformation bias scores

The figure shows search queries with the highest (stop the seal, voting
machine fraud, ballots in garbage, ballots thrown out, us elections 2020
pennsylvania) and lowest (voter fraud claims, voter fraud evidence, ballot
fraud, electoral fraud, ballot box fraud) mean misinformation bias scores

(b) Misinformation bias scores of search queries for each day of experiment
run

Figure shows the misinformation bias scores (y-axis) for the participants
belonging to the different political leanings for the days of the experiment
run (x-axis). The bias scores coincide for all users on all days.

Figure 9. RQ2a results: a) Search queries with highest (labeled in red) and
lowest (labeled in blue) mean misinformation bias scores. Positive
misinformation bias scores indicate a lean toward misinformation where as
negative bias scores indicate a lean toward information that opposes
misinformation. b) Figure showing the distribution of misinformation bias
scores of search queries for democrats, republicans, and independents. Note
that the bias scores for the participants belonging to the different political
leanings coincide indicating that misinformation bias in SERPs remains
constant throughout for each participant.

Furthermore, we observe in Figure 8 that the misinformation bias scores of the
SERPs form a bimodal distribution constituting two clusters of search queries
(Table 4). The cluster1 search queries have the most negative bias, i.e. they
contain more opposing videos. This cluster mostly consists of search queries
containing the keyword fraud in conjunction with keywords voter, election, and
dominion. Cluster2 on the other hand consists of search queries with keywords
election and 2020. Overall, cluster1 consists of more search queries biased
towards finding misinformation compared to search queries in cluster2. This
indicates that YouTube pays more attention to search queries about election
fraud and ensures that users are exposed to opposing videos when searching
about fraudulent claims surrounding the elections.

Figure 9(a) shows five search queries with the highest and 5 search queries
with the lowest misinformation bias. The search query ‘voter fraud claims’ has
the least amount of misinformation bias, indicating that most of the search
results for this query oppose election misinformation. On the other hand, the
search query ‘stop the seal’ has the most amount of videos supporting election
fraud claims. Next, we determine how do misinformation bias scores in SERPs
vary for democrats, independents, and republicans. Figure 9(b) shows that the
bias values for democrats, independents, and republicans for all days coincide
indicating that the amount of misinformation bias is almost constant for all
days for all participants irrespective of their partisanship. Overall, our RQ2
results indicate that YouTube pushes debunking information in search results,
more for search queries about voter fraud claims as compared to generic
queries about the presidential elections.

Pol. aff. | Statistical tests | Mean diff.
---|---|---
Democrats | F(2,3407)=4035.1 , p=0 | S¿N¿O
Republicans | F(2,2265)=2981.4, p=0 | S¿N¿O
Independents | F(2,2941)=3593.8, p=0 | S¿N¿O

Figure 10. RQ2b results: Mean misinformation scores of standard up-next trails
with seed videos that are supporting (S), neutral (N), or opposing election
misinformation (O) for Democrats, Independents, and Republicans. A positive
misinformation score indicates a lean toward misinformative content while a
negative score indicates a lean toward content that opposes election
misinformation. Statistical tests reveal a significant difference in the
amount of misinformation contained in up-next trails. We find that democrats,
republicans, and independents find more misinformation in supporting trails
compared to neutral trails, and more misinformation in neutral trails as
compared to opposing trails.

The mean misinformation score of trails with supporting seeds for democrats,
republicans, and independents is 0.28, 0.31, and 0.33 respectively. The mean
misinformation score of trails with neutral seeds for democrats, republicans,
and independents is -0.02, 0.01, and -0.01 respectively. The mean
misinformation score of trails with opposing seeds for democrats, republicans,
and independents is -0.49, -0.49, and -0.51 respectively.

###  6.2. RQ2b: Misinformation in up-next trails

The results of RQ1 showed that participants’ up-next trails are highly
personalized. In other words, videos in up-next trails obtained from the
standard window are different from videos in trails obtained from the
incognito window. Recall, that trails extracted from the incognito window act
as baseline unpersonalized trails while trails extracted from the standard
window, where users had signed into their accounts, act as personalized
treatment trails. Therefore, to determine the impact of personalization on the
amount of misinformation in up-next trails, we compare the misinformation bias
scores of trails collected in standard windows with the trails collected in
incognito windows. We find that the difference in misinformation bias scores
of standard and incognito up-next trails is not significant (t=-0.62, p=0.53).
This means that although the standard up-next trails are very different from
the incognito up-next trails, there is no difference in the amount of
misinformation present in them. To avoid inflating our sample size, for
further downstream analysis, we only consider up-next trails obtained from
participants’ standard windows. This similar strategy was adopted by Robertson
et al for analyzing bias in Google search results when they did not see any
significant difference in the amount of partisan bias in incognito-standard
SERP pairs (Robertson et al., 2018b).

####  6.2.1. Misinformation in standard up-next trails for different scenarios

In this section, we determine the amount of misinformation encountered by our
study participants in the standard up-next trails for seed videos with
different stances on election misinformation—supporting, neutral and opposing.
Figure 10 shows the mean misinformation scores of different up-next trails
collected from the standard windows of democrats, republicans, and
independents. Recall that a positive misinformation score (¿0) indicates a
lean toward misinformation, while a negative misinformation score indicates a
lean toward information that opposes election misinformation. We conduct
within-group statistical tests to determine the difference in misinformation
for the three scenarios (following trails for supporting, neutral, and
opposing seed videos). The tests indicate a filter bubble effect. If users
watch supporting videos, they are led to supporting videos in the trails. But
if they watch neutral videos, they are led to less misinformation compared to
when they watched supporting videos. However, if users watch opposing videos,
they are led to more opposing videos in the up-next trails. The same trend is
observed for democrats, republicans, and independents.

Is the amount of misinformation in trails with different seeds different for
democrats, republicans, and independents? Between-group statistical tests
reveal that the amount of misinformation in supporting trails (KW
H(2)=11.9,p=0.002) and neutral trails (KW H(2)=8.69,p=0.01) for democrats,
independents, and republicans is significantly different. We find that
independents in our sample receive more misinformation in their supporting
trails as compared to democrats. Additionally, republicans receive more
misinformation in their neutral trails compared to democrats.

Overall, by observing Figure 10, we realize misinformation scores of
supporting trails are positive and opposing trails are negative. However, the
magnitude of misinformation scores of opposing trails is much more than the
supporting trails indicating that the strength of the filter bubble effect was
more when our study participants watched videos opposing election
misinformation.

(a) Mean % of transitions in trails with seed videos supporting elec. misinfo.

(b) Mean % of transitions in trails with neutral seed videos

(c) Mean % of transitions in trails with seed videos opposing elec. misinfo.

Figure 11. RQ2b results: Mean percentage of various transitions present in the
standard up-next trails of democrats, independents, and republicans. S
represents a video supporting election misinformation, N represents a neutral
video and O represents a video opposing election misinformation. Transition
S-¿S denotes that a YouTube video supporting election misinformation leads to
an up-next video recommendation supporting election misinformation.

The figure shows the mean percentage of transitions (S-¿S, S-¿N, S-¿O, N-¿S,
N-¿N, N-¿O, O-¿S, O-¿N, and O-¿O) in trails with seed videos that are
promoting, neutral, and opposing. The percentage of N-¿N transitions are
highest in all trails collected from all users. For the opposing trails, O-¿N
and O-¿O transitions are around 20% for all users. Problematic transition N-¿S
is 2.67%, 3.78%, and 4.26% in neutral trails of democrats, republicans, and
independents.

####  6.2.2. Transitions in standard up-next trails

In this section, we gain more insights into the anatomy of YouTube’s up-next
trails by studying the various transitions present in them. This allows us to
determine how users get pushed towards misinformative or debunking videos in
the trails. Since our annotation scale consists of three values, supporting
(S), neutral (N), and opposing (O), there are 9 transitions possible in the
trails (S-¿S, S-¿N, S-¿O, N-¿S, N-¿N, N-¿O, O-¿S, O-¿N, N-¿O). For each
participant, we first individually determine the percentage of each of these
transitions present in the three types of standard up-next trails collected
(ones starting with a supporting seed video, neutral seed videos, and opposing
seed video). Then we calculated the mean percentage of all of these
transitions for democrats, independents, and republicans. From Figure 11, we
see that the maximum number of transitions across all participants and all
types of up-next trails is N-¿N. Problematic transitions like S-¿S and O-¿S
are less than 2% in trails of all users. However, comparatively S-¿S
transitions are still more in the supporting up-next trails of independents
(1.78%) compared to democrats (0.38%) and republicans (0.86%). In the neutral
up-next trails of republicans and independents, N-¿S transitions dominate
(after N-¿N transitions) indicating that independents and republicans are
sometimes led to supporting videos in their up-next recommendations even when
they are viewing neutral YouTube videos. We also observe that the opposing up-
next trails majorly consist of transitions O-¿N and N-¿O (after N-¿N
transitions) indicating that once a user watches a video that opposes election
misinformation, YouTube pushes more videos that are either neutral or opposing
in stance in the up-next trails of all the participants. We also observe that
S-¿O transitions are less than S-¿N transitions in the supporting trails of
democrats, republicans, and independents. Previous work has shown that
watching YouTube videos that debunk misinformation helps in bursting filter
bubbles of misinformation (Tomlein et al., 2021). Our work also shows that
opposing videos could lead to more opposing videos (O-¿O transitions in
opposing trails). Thus, increasing the number of S-¿O transitions can lead
users to trustworthy information on the platform.

Figure 12. RQ2c results: Figure showing the average change in the amount of
bias present in homepages because of watching a trail of up-next videos
starting with either supporting, opposing, or neutral seed videos for
democrats, republicans, and independents.

Figure showing the average change in the amount of bias present in homepages
because of watching a trail of up-next videos starting with either supporting,
opposing, or neutral seed videos for democrats, republicans, and independents.
All the values are equal to or less than 0.01.

(a)

(b)

Figure 13.  RQ3 results: a) Figure showing Top-10 YouTube channels with
impressions in the most number of search queries for all study participants.
For example, on average CNN appears in 61.86% of search queries for all our
study participants. b) Figure showing the average number of impressions for
Top-10 YouTube channels that appear in the most number of standard up-trails
collected for users. For example, on average, videos from the Fox News channel
appear 3.27 times in those up-next trails where videos from the channel are
observed.  is a left-leaning channel,  is right-leaning and  is center-
leaning.

Figure (a) shows the Top-10 YouTube channels with impressions in the most
number of search queries for all study participants. The channels are in order
CNN (61.86%), NBC News (39.85%), CBS News (33.64%), CNBC Television (31.36%),
60 minutes (29.51%), MSNBC (27.79%), Fox news (27.08%), PBS NewsHour (26.27%),
11Alive (24.72%), Today (23.88%). Figure (b) shows the average number of
impressions for Top-10 YouTube channels that appear in the most number of
standard up-trails collected for users. The channels are in order
LastWeekTonight, Saturday Night Live (4), Fox News (3.6), Late Night with Seth
Meyers (3.27), The Late Show with Stephen Colbert (2.98), Jimmy Kimmel Live
(2.65), NBC News (2.02), Sky News Australia (1.93), Fox Business (1.92), and
PBS NewsHour (1.71)

###  6.3. RQ2c: Misinformation in homepages

We collected participants’ YouTube homepages to determine how the bias in the
homepage changes ($\delta$) after watching a trail of videos starting with a
seed video that is either supporting (${\delta}_{S}$), opposing
(${\delta}_{O}$) or neutral (${\delta}_{N}$) in stance with respect to
election misinformation. We calculated the impact of trails by using the
following formula:-

${\delta}_{stance}$ = Misinformation
$score_{Homepage\\_before\\_the\\_trail}$\- Misinformation
$score_{Homepage\\_after\\_the\\_trail}$

${\delta}_{S}$, ${\delta}_{N}$ and ${\delta}_{O}$ represent the change in the
amount of bias present in homepages because of watching a trail of up-next
videos starting with supporting, opposing and neutral seeds. A negative
$\delta$ would indicate that the YouTube homepage collected after the trail
contained more opposing videos compared to the YouTube homepage before the
trail. A positive $\delta$, on the other hand, indicates either presence of
more videos supporting election misinformation or a lesser number of opposing
videos on the homepage collected after the trail as compared to the homepage
collected before the trail. We consider the top ten recommendations present on
the homepage for analysis. Figure 12 shows $\delta$ values for all three kinds
of trails for democrats, republicans, and independents. We discuss a few
results. We observe that after following the up-next video trails starting
from a neutral seed, the homepages of democrats and independents contain more
supporting videos. However, recall that the average misinformation score of
the up-next trails with neutral seeds for both democrats and independents was
negative (Figure 10). This indicates that although the up-next trails with
neutral seeds lead users to more opposing videos, the homepages, however,
contain more misinformation or a lesser number of opposing videos after the
trail. We also observe that after watching up-next trail videos with
supporting seed, republicans’ homepage contain more opposing videos (Figure
12) while the trail itself contained more misinformation (Figure 10). However,
note that the magnitude of the $\delta$ is low in all the conditions
indicating that fewer videos supporting or opposing election misinformation
appear on the participants’ homepages.

##  7\. RQ3: Composition and Diversity

In this research question, we want to characterize source diversity in YouTube
when users search for election misinformation on the platform. Source
diversity in searches and recommendations is an important characterization of
fairness (Ge et al., 2021). Furthermore, given that the narratives about the
election misinformation were closely intertwined with news sources and their
leanings, it is important to determine what kinds of YouTube channels are
users exposed to. News and media diversity can be characterized in multiple
ways (Joris et al., 2020). One typology characterizes media diversity with
respect to source (content providers), content (perspectives) and exposure
(actual consumption of diverse content) (Napoli, 2011; Trielli and
Diakopoulos, 2019). Our work analyzed the content diversity in RQ2 by
analyzing the video’s stance on election misinformation. We cannot study
exposure diversity since it requires determining the actual content consumed
(clicked, watched, etc) by our study participants in their naturalistic
settings. For this study, we focus on source diversity in terms of the
identity of top content providers (YouTube channels) and distribution and
concentration of channels in the standard SERPs and up-next trails. We
acknowledge that future studies should also examine the ideological position
of news sources and study the filter bubbles of partisan content on the
platform.

(a) Democrats

(b) Republicans

(c) Independents

Figure 14. RQ3a results: Distribution of Gini coefficients for all search
queries (n=88) for a) Democrats, b) Republicans, and c) Independents,
calculated based on the distribution of impressions of YouTube channels
appearing in the search results.

Figure a shows the gini index distributions for search queries for democrats,
54.5% search queries have gini between 0-0.1. 33% have gini between 0.1-0.2.
Figure b shows the gini index distributions for search queries for
republicans, 54.5% search queries have gini between 0-0.1. 33% have gini
between 0.1-0.2. Figure c shows the gini index distributions for search
queries for democrats, 56.8% search queries have gini between 0-0.1. 30.7%
have gini between 0.1-0.2.

(a) Democrats (supp. trails)

(b) Republicans (supp. trails)

(c) Independents (supp. trails)

(d) Democrats (neutral trails)

(e) Republicans (neutral trails)

(f) Independents (neutral trails)

(g) Democrats (oppos. trails)

(h) Republicans (oppos. trails)

(i) Independents (oppos. trails)

Figure 15. RQ3b results: Figure showing the top YouTube channels appearing in
supporting, neutral, and opposing trails of democrats, republicans, and
independents and the percentage of users in whose trails these channels
appear.  is a left-leaning channel,  is right-leaning and  is center-leaning.

Figure (a) shows the top YouTube channels appearing in supporting trails of
Democrats (Fox news, NBC, News, CBS News). Figure (b) shows the top YouTube
channels appearing in supporting trails of republicans (Fox news, Fox
Business, Sky News Australia). Figure (c) shows the top YouTube channels
appearing in supporting trails of independents (Fox news, NBC news, PBS
Newshour). Figure (d) shows the top YouTube channels appearing in neutral
trails of Democrats (Fox news, PowerfulJRE, Fox Business). Figure (e) shows
the top YouTube channels appearing in neutral trails of republicans (Fox news,
Sky News Australia, PowerfulJRE). Figure (f) shows the top YouTube channels
appearing in neutral trails of independents ((Fox news, PowerfulJRE, Fox
Business)). Figure (g) shows the top YouTube channels appearing in opposing
trails of Democrats (Saturday Night Live, Last Week Tonight, Late Night with
Seth Meyers). Figure (h) shows the top YouTube channels appearing in opposing
trails of republicans (Saturday Night Live, Last Week Tonight, Late night with
Seth Meyers). Figure (i) shows the top YouTube channels appearing in opposing
trails of independents (Saturday Night Live, Last Week Tonight, Late night
with Seth Meyers).

###  7.1. RQ3a: Diversity in search results

For analysis, we consider the top ten search results in standard SERPs. Figure
13(a) shows the top 10 YouTube channels with impressions in the most number of
search queries.161616The top 10 YouTube channels and their mean percentage of
total impressions were almost similar when calculated separately for
democrats, republicans, and independents. Thus, we show the overall
distribution for all users combined together. Here, we define impression as
the occurrence of a channel’s video in SERP. We observe that the left-leaning
channel CNN on average appears in SERPs of more than half (61.86%) search
queries. Additionally, except Fox news and 11Alive, all other top channels are
left-leaning. We further analyzed which channels were responsible for the most
relevant YouTube videos in our collected data. In our standard SERPs, we
obtained a total of 4901 unique videos out of which 1940 (39.51%) videos were
relevant, i.e. related to elections (959 opposing, 865 neutral, and 103
supporting). Overall, in these relevant videos, most videos come from CNN and
MSNBC. The most opposing videos come from channels MSNBC followed by CNN, most
supporting videos come from Fox News followed by Daily Mail while most neutral
videos come from NBC news followed by CNN. Given, CNN is one of the channels
with the most opposing videos, it is encouraging to see that it has the most
search query impressions.

Next, we determine the source diversity in the SERPs using gini coefficient
metric (Ge et al., 2021; Xiao et al., 2019; Trielli and Diakopoulos, 2019).
Gini coefficient determines inequality in a frequency distribution. For our
case, we use this metric to determine the inequality in the distribution of
YouTube channel impressions. For a given SERP consisting of videos from n
unique channels, given a list of impressions for all YouTube channels
[$g_{1}$, $g_{2}$,…$g_{n}$], then gini coefficient would be calculated as,

Gini coefficient (G) = $\frac{1}{2\bar{g}n^{2}}$ $\Sigma^{|n|}_{i=1}$
$\Sigma^{|n|}_{j=1}$ —$g_{i}$ \- $g_{j}$— where $\bar{g}$ is the mean of all
impressions.

A fairer search engine would have lower values of gini coefficient indicating
uniform distributions of YouTube channel impressions. Figure 14 shows the
distribution of gini coefficients for all SERPs for democrats, republicans,
and independents. The distributions are similar for users with different
political leanings. Furthermore, for approximately 96% of search queries, the
gini coefficient of SERPs is less than 0.3 indicating that YouTube has mostly
evenly distributed videos from different channels in its search results.

###  7.2. RQ3b: Diversity in up-next trails

Overall, we collected 6943 videos in standard trails out of which 1082 are
relevant, i.e. related to elections. The most number of opposing videos in
trails come from channels MSNBC and Late Night with Seth Meyers*, most
supporting videos in trails come from Fox News* and Fox Business, and most
neutral videos come from Fox News* and NBC News171717* indicates that seed
videos of our experiments also belonged to these channels.. Next, we determine
the top ten YouTube channels occurring in the standard trails. Note, we do not
consider the seed videos while analyzing the trails. Figure 13(b) shows the
average number of impressions of the top 10 channels appearing the most number
of times in the trails. Here, impression indicates the number of occurrences
of a channel’s videos in a trail, while considering trails containing videos
from that channel. Note that the top channels are also channels of some of the
seed videos in our dataset. The figure reveals that on average, videos from
LastWeekTonight, Saturday Night Live, and Fox News appear more than 3 times in
a trail, when taking into account all the trails where the channel was
observed. This finding indicates that videos from these channels lead to more
videos from these channels in the up-next recommendations.

Next, to determine the diversity in trails, we determine the proportion of
channels that are different than the channel of the seed video in the trails.
We find that on average, in an up-next trail of length five, we find 2.07
YouTube channels other than the channel of the seed video. The number of non-
seed channels in up-next trails is the least for trails with seed videos from
Saturday Night Live (0.85), LastWeekTonight (0.86), and Late Night with Seth
Meyers (1.07). Note, we did not calculate this metric for supporting, neutral,
and opposing seeds separately since the channels of our supporting, opposing,
and neutral videos are not unique. For example, we have a supporting as well
as a neutral seed from Fox news. Given this scenario, there is no way to
determine whether the videos appearing in the trails are due to the channel
lean of the seed video or because of other factors. We also refrain from
determining the diversity in up-next trails using gini coefficient since
several trails had just one or two unique channels (M=3.1, SD=1.46) in which
case gini coefficient would not give a good representation of diversity.

To get a sense of what kinds of channels are presented to users in the up-next
trails, we determine the channels appearing in the most number of trails of
democrats, republicans, and independents for trails with supporting, neutral,
and opposing seeds (Figure 15). We observe that Fox news appears in up-next
trails with supporting and neutral seeds of all users. Fox Business and Sky
News Australia appear in both the supporting and neutral up-next trails of
more than half of the republicans (Figure 15(b), and 15(e)). None of the seed
videos belonged to these channels and they still appear in the up-next trails.
Similarly, Sky News Australia also appears in the neutral up-next trails of
44.12% independents (Figure 15(f)) despite no neutral seed belonging to the
channel. Furthermore, PowerfulJRE (Joe Rogan’s YouTube channel) did not appear
in the neutral up-next trails of all the users even though two neutral seed
videos belonged to the channel (Figure 15(d), 15(e) and 15(f)). On the other
hand, the top channels appearing in the up-next trails with opposing seeds of
all users (Figure 15(g), 15(h) and 15(i)) are the channels of the opposing
seed videos used in our experiment. Furthermore, three channels out of the top
four appear in the trails of more than 96% of the users. This indicates that
watching a video belonging to these left-leaning channels will probably lead
to one or more videos belonging to this channel in the up-next recommendation
trail.

##  8\. Discussion

In this paper, we conduct a crowd-sourced audit of the YouTube platform to
determine how effectively the platform removed election misinformation from
its various components. We discuss the implications of our findings below.

###  8.1. Standardization of search results

We find little to no personalization in the search results. We also did not
find any effect of personalization on the amount of misinformation returned in
search results. Throughout the study period, the amount of personalization and
misinformation remained constant in the searches. On analyzing the standard
SERPs, we find that YouTube returns more videos opposing election
misinformation in 95% of the search queries that we tested. Interestingly, we
see that misinformation scores of search queries having a misinformation lean
(e.g. dominion voter fraud) are more negative compared to misinformation
scores of queries that are neutral in stance (e.g. presidential election
2020). This finding implies that YouTube has paid more attention to the
queries with misinformation lean and ensured that users are exposed to more
debunking information when they search about the fraudulent claims surrounding
the elections. This selective attention is also in-line with results of past
audits that showed YouTube improving the recommendations of topics like
vaccination over 9/11 conspiracies (Hussein et al., 2020).

Our analysis also indicates that gini index of 96% of search queries is less
than 0.3, with $\sim$54% queries having a gini index of less than 0.1. Such
low values of gini index imply that YouTube is ensuring source diversity in
searches by evenly distributing videos from different channels in its SERPs.
Furthermore, the distribution of gini coefficients was similar for all users
irrespective of their partisanship. This finding indicates YouTube’s attempt
to expose users to videos from different channels rather than a select few
based on participants’ partisanship. Interestingly, in line with a previous
audit on Google search (Trielli and Diakopoulos, 2019), we find that CNN is
one of the top channels whose videos appear in 61.8% of search queries. Future
studies can test whether the dominance is due to emergent bias or the
strategies adopted by the channel to enhance algorithmic visibility (Trielli
and Diakopoulos, 2019). Overall, our analysis reveals that YouTube’s search
results are largely unpersonalized and the platform has had varying levels of
success in removing misinformation and presenting videos that debunk election-
related falsehoods in different clusters of search queries.

###  8.2. Scope for improvement in up-next trail recommendations

We find that up-next trails are highly personalized. However, for 50% of the
users, only up to 10% videos in the up-next recommendations come from users’
subscribed channels. Future audit studies should further investigate the
impact of users’ channel subscriptions (both news and non-news channels) on
the platform’s recommendations. We also find that there is no significant
difference in the amount of misinformation that users are exposed to in up-
next recommendation trails in the signed-in standard window and unpersonalized
incognito window. On examining the standard up-next trails, we do find an
echo-chamber effect. Users, irrespective of their partisanship, receive more
misinformation in the up-next trails with supporting seeds as compared to the
trails with neutral and opposing seeds (Figure 10). We also observe that the
magnitude of misinformation scores of trails with opposing seeds is more than
the magnitude of misinformation scores of trails with supporting seeds. This
implies that users are exposed to a small number of misinformative videos when
they follow the up-next recommendations of a video supporting election
misinformation. On the other hand, users are exposed to a larger number of
opposing videos in the opposing up-next trails. This is a key finding also
supported by prior work that showed that echo chambers of misinformation can
be burst by watching debunking videos (Tomlein et al., 2021). The platform can
leverage this phenomenon by making its recommendation engine present more
debunking videos to users which would then expose them to more credible videos
in the recommendation trails.

We also examine various transitions in the up-next trails to study how users
get pushed towards misinformation. Overall, we observe that problematic
transitions where a supporting video is recommended in the up-next video
recommendation of a supporting (S-¿S) or opposing video (O-¿S) are less than
2%. However, S-¿S transitions are more in trails with supporting seeds for
independents compared to democrats and republicans. Furthermore, N-¿S
transitions are also high in up-next trails with neutral seeds for
independents. These findings are problematic. Showing misinformative videos to
independents who might not have developed a strong opinion on the election
fraud conspiracies could increase their chances of forming a pro-conspiracy
belief. We also observe that N-¿S transitions are more for republicans in the
up-next trails with neutral seeds (3.78%) compared to trails with supporting
seeds (1.61%). This finding is again troublesome. Past studies have indicated
that republicans are more susceptible to electoral fake news (Owen, 2017).
Thus, recommending videos supporting election misinformation to republicans
watching neutral videos would expose them to more misinformation which might
reinforce or lead to forming conspiratorial beliefs.

On analyzing the up-next trails for channel diversity, we observe several
interesting phenomena. First, the number of impressions for left-leaning late-
night show channels on YouTube such as LastWeekTonight is very high. On
average, approximately 3-4 videos from these channels appear in the up-next
trails (of length five) when starting with opposing seed videos. Furthermore,
these channels appear in the video recommendations of almost all of our study
participants. Similar to the late-night shows, we find that fox news also
appears on average 3.27 times in the up-next trails of all participants.
Future studies can look into the reasons behind the strong “algorithmic
recognizability” (Gillespie, 2017) and high amplification of these channels in
YouTube recommendations. Overall, we conclude that while YouTube has reduced
misinformative videos in its up-next recommendations, there is still scope for
improving the recommendation algorithm.

###  8.3. Participants’ beliefs vs algorithmic reality

The study survey conducted before our audit experiment provided us with an
opportunity to map participants’ beliefs about personalization and trust in
YouTube’s algorithms with the reality of the situation as determined by our
audits. The majority of participants believe that YouTube somewhat
personalizes search results. However, in reality, they are hardly
personalized. On the other hand, only half of the participants believe up-next
recommendations to be highly personalized which is in line with our findings.
This mismatch in beliefs and reality indicates users’ lack of algorithmic
awareness. It also acts as a call to action for the platform to make users
aware of the functioning of the algorithms. Users could be made aware of
personalization or lack of it by adding design features that promote
algorithmic reflection, for example, seeing search results or recommendations
of other users (Bhuiyan et al., 2022).

Our survey also showed that, respectively, 19.2% and 14.1% users trust the
credibility of information presented to them by YouTube in the search results
and up-next recommendations to a great extent. This belief is problematic and
indicates reliance on the platform’s algorithms to show credible information.
In reality, while we find the majority of YouTube’s search results to be
credible, up-next recommendations still contained misinformative videos. One
way to make people spot misinformation on the platform and not blindly trust
YouTube’s recommendations could be by providing additional context about the
content that the participant is searching for or viewing. While YouTube has
started displaying Wikipedia links on the platforms (D’Onfro, 2018),
additional cues in the form of credibility citations, existing fact-checks or
knowledge
panel181818https://support.google.com/knowledgepanel/answer/9163198?hl=en
could also be helpful (Hughes et al., 2021).

##  9\. Limitations and future work

Our work is not without limitations. Our audit study is observational in
nature, i.e our experiment does not isolate user attributes that produce the
differences in misinformation measurements. We only make observations on the
differences in misinformation received in searches and recommendations of
users with different political affiliations. We recruited participants who
used YouTube extensively to get information about the 2020 elections. However,
for ethical reasons, we did not analyze participants’ account histories to
verify their self-reported data. Our participant sample was also not balanced
with respect to demographic attributes and political affiliation. We selected
YouTube videos that had accumulated the most number of views as the seed
videos for our audit experiments. One potential pitfall of such a sampling
strategy is that it reduces the ecological validity of the experiment since
the participants in our study might not have engaged with those videos in the
past. Another limitation is that YouTube might have specifically tailored the
recommendations of popular misinformative videos. Future studies could
consider alternative strategies for sampling videos, such as selecting videos
that were more recently published on YouTube or sampling a combination of
videos that have accumulated the least and most amount of engagement. The
search queries used in our audit also might not be representative of how our
study participants formulate queries about the elections. Future studies can
survey the study participants to determine how they used YouTube searches in
the context of political elections as well as their information needs about
the elections.

Our classifier developed to annotate the YouTube videos for election
misinformation has an error rate of 9% which could have affected the
downstream analysis that we performed to quantify the amount of misinformation
in various YouTube components. Additionally, we assign an annotation value of
0 to all videos that were removed from YouTube after our audit data
collection. While the number of such videos is very small (¡1%), it would
result in a conservative estimate of misinformation bias present in the search
results and recommendations. We use the misinformation bias score adopted from
Hussein and Juneja et al’s study that captures the amount of misinformation
along with the rank of the video (Hussein et al., 2020). However, this metric
does not take into account the relevance of the videos. Future studies can use
metrics that measure simultaneously the relevance and credibility in ranked
lists such as Normalised Weighted Cumulative Score and Convex Aggregating
Measure (Lioma et al., 2017). In our audit experiment, after testing every
condition (watching supporting, neutral, and opposing videos), we performed a
step to delete users’ YouTube history created by our extension so that it does
not impact the other experimental condition. The first author tested out the
effect of deletion on users’ search and watch history for a few sample queries
and videos and found that the effect of such deletion is almost immediate.
However, we did not test out this scenario for all search queries and videos
used in our audit. Future studies can determine how soon the deletion of
history impacts users’ recommendations and search results across various
topics.

Our study focuses on users’ beliefs about the personalization and credibility
of content on YouTube as well as the role of YouTube’s algorithms in driving
users to the filter bubbles of problematic content. Future studies can focus
on the impact of algorithmic recommendations on the radicalization of users.
There are several scholars who argue that algorithms are not centrally
culpable for the polarization or the filter bubbles that users experience on
online platforms (Bruns, 2019b; Whittaker et al., 2021; Bruns, 2019a). Many
times the users of social media have a more diverse media diet than the non-
users (Bruns, 2019b, a). Scholars posit that while algorithms can observe what
a user consumes on social media, they cannot determine what the user actually
prefers (Dahlgren, 2021). In other words, a digital choice is not always a
true reflection of an individual’s preference (Dahlgren, 2021). Furthermore,
users might use different online platforms for different types of content
(Dahlgren, 2021). Thus, to gain a holistic idea of the extent algorithms play
a role in user polarization, future audit studies can conduct multi-platform
crowd-sourced audits for individuals. These audit studies can determine the
impact of algorithmic recommendations on users’ social/political viewpoints
via surveys and monitor users’ patterns of content consumption simultaneously
on multiple search engines and social media platforms used by the users.

##  10\. Conclusion

In this study, we conducted a crowd-sourced audit on YouTube to determine the
effectiveness of its content regulation policies with respect to election
misinformation. We find that YouTube returns videos that debunk election
misinformation in its searches. We also find that YouTube leads users to a
small number of misinformative videos in up-next trails with seed videos that
support election misinformation. Overall, our study shows that while YouTube
has been largely successful in removing election misinformation from its
searches, there is still scope to fix up-next recommendations.

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##  Appendix A Appendix

###  A.1. Participants’ characteristics

In our study survey, we asked participants how often they used the YouTube
platform for getting news about the 2020 US Elections. Out of the 99 users who
participated in the study, 40.4% reported using YouTube to access election-
related news several times a day. When asked whether the 2020 election was
stolen from Donald Trump, 59.59% strongly disagreed, 14.14% somewhat
disagreed, 11.11% somewhat agreed, and 11.11% strongly agreed. 19.19%
participants believed that the US presidential elections were a result of
illegal voting or election rigging while 70.70% believed it to be legitimate
and accurate. 31.31% participants believed fraud in the United States with
respect to the presidential election 2020 – that is, votes being cast in the
name of people who are not eligible to vote was a major problem. 41.41%
believed voter disenfranchisement in the United States with respect to the
presidential election 2020, i.e, eligible voters being prevented from casting
their ballots or not having their ballots counted was a major problem. 30.30%
participants believed fraud in voting by mail in the U.S. with respect to the
presidential election 2020 to be a major problem. 57.57% participants rated
Donald Trump’s conduct during the presidential elections as poor while 13.13%
rated it as excellent. On the other hand, 17.17% of participants rated Joe
Biden’s conduct during presidential elections as poor while 16.16% rated it as
excellent.

###  A.2. Amazon Mechanical Turk Job

We used AMT to get annotations for 545 videos for the ground truth dataset.
During our manual annotations, we realized that the majority of the videos
were irrelevant. In order to get annotations for relevant videos—classes
supporting, opposing, and neutral, we curated a list of keywords (such as
’fraud’, ’ballot’, ’election’, ’steal’, etc.) and news channels (such as CNN,
NTD, Fox news, etc.) on YouTube. Then, we filtered out videos that were
published by the curated channels and had the keywords in the title or
description. To get high-quality annotations for these videos, we trained and
screened the AMT workers. Below we describe the screening process and our
annotation task briefly.

Figure 16. Figure illustrating the process of obtaining YouTube video
annotations from AMT workers. The workers were screened via a qualification
test where they were first trained by providing detailed descriptions of the
annotation labels. To test their understanding, they were asked to annotate
three YouTube videos whose labels were known in advance. Workers who correctly
labeled the three videos proceeded to work on the annotation task. To ensure
that our description of the annotation labels and the task was clear and
comprehensive, we posted on r/mturk—a subreddit community of AMT workers and
AMT workers’ unofficial slack channel. We released our qualification test and
annotation task after receiving positive feedback from the AMT community.

Figure illustrating the process of obtaining YouTube video annotations from
AMT workers. The workers were screened via a qualification test where they
were first trained by providing detailed descriptions of the annotation
labels. To test their understanding, they were asked to annotate three YouTube
videos whose labels were known in advance. Workers who correctly labeled the
three videos proceeded to work on the annotation task. To ensure that our
description of the annotation labels and the task was clear and comprehensive,
we posted on r/mturk—a subreddit community of AMT workers and AMT workers’
unofficial slack channel. We released our qualification test and annotation
task after receiving positive feedback from the AMT community.

Worker training and screening: To train workers to do the annotation task and
screen them on the basis of their understanding of the annotations, we created
a qualification test. The test first described in detail the annotation
labels, heuristics, and the annotation task. We provided several examples of
YouTube videos for each annotation label and described the process and reason
behind assigning a particular label to the video. To ensure that our
description of the annotation labels and the task was clear and comprehensive,
we posted on r/mturk— a subreddit community of AMT workers as well as AMT