# Social Search: retrieving information in Online Social Platforms - A Survey

Maddalena Amendola  Andrea Passarella  Raffaele Perego

###### Abstract

Social Search research deals with studying methodologies exploiting social
information to better satisfy user information needs in Online Social Media
while simplifying the search effort and consequently reducing the time spent
and the computational resources utilized. Starting from previous studies, in
this work, we analyze the current state of the art of the Social Search area,
proposing a new taxonomy and highlighting current limitations and open
research directions. We divide the Social Search area into three
subcategories, where the social aspect plays a pivotal role: Social
Question&Answering, Social Content Search, and Social Collaborative Search.
For each subcategory, we present the key concepts and selected representative
approaches in the literature in greater detail. We found that, up to now, a
large body of studies model users’ preferences and their relations by simply
combining social features made available by social platforms. It paves the way
for significant research to exploit more structured information about users’
social profiles and behaviors (as they can be inferred from data available on
social platforms) to optimize their information needs further.

###### keywords:

Social Search , Search in Online Social Networks , Social Information
Retrieval

††journal: Online Social Networks and Media

[inst1]organization=Department of Computer Science,addressline= University of
Pisa, city=Pisa, postcode=56127, country=Italy

[inst2]organization=Institute for Informatics and Telematics
(IIT),addressline= National Research Council (CNR), city=Pisa, postcode=56124,
country=Italy

[inst3]organization=Institute of Information Science and Technologies
(ISTI),addressline=National Research Council (CNR), city=Pisa, postcode=56124,
country=Italy

##  1 Introduction

Online Social Network (OSN) platforms have been one of the major evolutions of
the Web for at least a decade. Aside from their social implications, OSNs are
impacting many established areas of computer science, which are progressing
also based on the pervasive diffusion of OSN services among users. A chief
example is the area of Social Search. Social Search lies at the intersection
between Information Retrieval (IR) and Social Networks. It is generally
defined as a cooperative process that relies on implicit or explicit
information about social relationships between users to satisfy an information
need. The main focus of this paper is to provide a comprehensive survey of the
main results in this very active field. IR processes are today at the basis of
almost all the most common uses of the Web, whether the user searches for a
restaurant, the solution to a problem, wants to learn something, or asks for
opinions. With the progressive development of tools for creating and sharing
personal content and the increase of explicit or hidden interactions between
users, the process that leverages users’ social information in the standard IR
tasks is becoming of fundamental importance. Furthermore, the interplay
between IR and OSNs is becoming increasingly crucial as OSNs are one of the
leading platforms on which users search for information. Therefore, social
content in OSNs can complement traditional W eb sources in providing fresh and
almost real-time information about events and ongoing discussions [1]. The
reason why people prefer, in many cases, to retrieve information from a social
network rather than a conventional search engine lies in the origin of the
information need. Through social networks, users can obtain tailored
information and different opinions by interacting with the virtual counterpart
of real individuals [2].

Figure 1: The proposed Social Search Taxonomy.

In this survey, we follow the definition and taxonomy proposed in [3] for
Social Search (we refer the reader to Section 2 for related surveys and
definitions). Thus, we consider Social Search as the IR process applied to
social network platforms and content. In presenting the recent advances in
Social Search, as in [3], we divide the broad topic into three categories
based on the type of Social Search system. Specifically, each of the following
three categories can be seen as a particular instance of a Social Search
system, focusing on a specific approach. Figure 1 shows the taxonomy we
propose. The leaves in the taxonomy have different colors based on their role
in the specific task: green if they are components needed to perform the task,
and yellow if they are alternative approaches to accomplish the task. The
three subcategories are the following:

  * •

Social Q&A: through Question&Answering (Q&A) communities, people can ask
questions using more “natural” and flexible forms of natural language (be it
via speech interfaces or typing text concerning more rigid keyword-based
queries), expressing their information need more completely. Alternatively,
the user can refer to existing questions and avoid waiting for someone to
answer. These platforms differ from social networks in the type of
relationships between users, which is implicit here most of the time. By
analyzing them, we have divided this category into three sub-categories:
Expert Finding, Question Retrieval, and Answer Selection. The Expert Finding
problem aims to identify experts in the community to whom forward a newly
posted question to satisfy the user information need in the shortest possible
time. The Question Retrieval problem deals with analyzing the question
archives to find old questions similar to the new ones published. Finally,
Answer Selection deals with the problem of automatically selecting the best
answer in a long thread of questions’ answers and comments, which makes users
unable to choose the correct one. Expert Finding, Question Retrieval, and
Answer Selection are three very widely studied problems, which are therefore
analyzed separately in Sections 4, 5, and 6, respectively. These three topics
should be seen as complementary components of a fully-fledged Social Q&A
system rather than as alternative ways to implement such a system;

  * •

Social Content Search: refers to techniques that facilitate the users’
searches in social networks, where the relationships between users are usually
explicit, unlike community Question&Answering (cQA) sites usually
characterized by implicit relationships. In a Q&A platform, a user may also
have no relationship with other users and still exploit the system by asking
questions and taking advantage of the knowledge provided by “strangers.”
Instead, users usually use social networks to post/search for statuses, get
information on events, stay up to date, and maintain relationships. In these
platforms, users have pre-established relationships. We divide the Social
Content Search literature into two areas representing two alternative
approaches to accomplish the task: the works that do not modify the query and
those that propose terms for query expansion or suggest queries. The
literature on Social Content Search is presented in Section 7;

  * •

Social Collaborative Search: in Social Q&A and Social Content Search, the
collaboration among users in the IR process is mostly implicit. However, in
other cases, the search process involves multiple people who _explicitly_
collaborate to achieve a common purpose. Collaborative Search allows for
unifying knowledge, skills, and experience. We divide this area into three
parts: (i) specific community-oriented solutions (i.e., academic purposes),
(ii) general-purpose solutions, and (iii) chatbot-based solutions. The first
two approaches are alternatives that define the target of the Collaborative
system. The last is a complementary organization of the Collaborative system
that favors building dynamic communities focused on a common goal. The Social
Collaborative Search literature is presented in Section 8.

Note that, to keep the paper self-contained despite the vastity of the
literature, we use the following approach unless otherwise stated. We identify
critical issues addressed in the literature for each leaf node in the
taxonomy. For each such issue, we describe more extensively only one paper to
exemplify the approach, and we briefly mention other papers highlighting only
their key conceptual differences concerning the former. Finally, in section 9,
we highlight critical open challenges, emphasizing that, in most cases, they
could be addressed appropriately through a more structured approach to
modeling social aspects of the users’ behavior as they emerge by analyzing
data available on the considered Online Social platform. These challenges
arise mainly from the nature of social data, its volume, dynamics, and the
need for the user to be more aware of the personal data used.

##  2 Definitions and Related Surveys

The initial concepts regarding Social Search relate to the modern idea of
Social Web Search, which aims to improve the IR processes of search engines
leveraging social information. In the absence of popular social networks, the
first approach was to consider the history of searches as a source of
additional information for future searches via automatic query terms expansion
[4]. According to [5], Social Search should contrast the simple surfing, where
users follow hyperlinks until they find the information. Considering the Web
as modeled by a graph that connects information in a highly complex way, its
huge size presents users with two difficulties: deciding which site to choose
and judging a priori the informative value of the returned results. Following
a Social Search approach, users should first use information from other users’
experiences to start with a site and further exploit it to refine the
information obtained through regular surfing. Subsequently, the definition of
Social Search was transformed by the introduction and popularity of the first
social networks. In [6], Social Search has been defined as “an approach to Web
search that tempts to offer communities of like-minded individuals more
targeted search services, based on the search behavior of their peers,
bringing together ideas from Web search, social networking and
personalization.” Social Search becomes a combination between social
networking and Web search, exploiting the information about the communities
that social networks allow to create. In this way, search engines have become
more sensitive to the needs and preferences of specific communities of users.
Personalizing search results based on like-minded individuals has also been
known as Social Navigation. According to [7], Social Navigation aims at taking
advantage of the human tendency to follow other people with similar interests
and repeat their steps. The authors in [8] combine Social Navigation and
Social Search techniques to take full advantage of the collaboration both can
offer. With the former, it is possible to leverage the past browsing behavior
of users to guide other users to relevant information; the latter aims to use
the search user and users’ community patterns to adapt the result list to the
needs and preferences of a specific community.

Another evolution in the concept of Social Search emerged with the advent of
Web 2.0. Because in Web 2.0 everyone can create content, users have created
annotations for Web pages at a surprising speed. An example of a popular
bookmarking site was Delicious, with millions of registered users. Annotations
represent new information to be exploited to improve IR processes. In [9], the
authors integrate social annotations into the concept of Social Search by
proposing two new algorithms based on PageRank: SocialSimRank and
SocialPageRank. Accordingly, the concept of Social Search has been expanded.
In [10], the authors define Social Search as “an umbrella term used to
describe search acts that make use of social interactions with others. These
interactions may be explicit or implicit, co-located or remote, synchronous or
asynchronous.” Following such a broad definition, in [11], the authors study
the effectiveness of different social network types for personalization: (i)
familiarity-based network, (ii) similarity-based network, and (iii) overall
network. They use the notion of Social Search to describe the search process
over Web 2.0 _social data_ representing different entities and their
interrelations.

A few studies in the literature have proposed categorization of Social Search
research. Authors of [12] rely on the simple notion that “Social Search refers
to the use of social media to aid finding information on the Internet,”
identifying five crucial dimensions for understanding Social Search:

  1. 1.

Collaboration - asynchronous vs. synchronous: in an asynchronous scenario, the
data coming from social media are used to improve the Web search without any
interaction between users. Vice versa, the synchronous scenario involves user
interactions during the search process;

  2. 2.

Collaboration - implicit vs. explicit: the tacit collaboration, as for the
asynchronous scenario, is where the search system implicitly integrates social
information obtained from users. When people interact during the
collaboration, we talk about explicit collaboration;

  3. 3.

Search target - finding people vs. finding resources: Social Search is often
seen as searching for people, while Social Search systems offer a means to
also search for resources by considering social information;

  4. 4.

Finding - search vs. discovery: Social Search includes traditional information
retrieval mechanisms for searching by re-ranking search results based on
social information. However, it also offers the possibility to discover
information and resources. For example, on a social bookmarking site, the user
can discover new websites that generally are not returned by the system during
a search process;

  5. 5.

Search results - sense-making vs. content selection: most of the time, people
do not fully understand why they get specific results. Social Search can
improve sense-making and content selection, providing contextual information.

Finally, in [3], the authors propose a taxonomy of social IR defined as “the
process of leveraging social information (both social relationships and the
social content), to perform an IR task with the objective of better satisfying
the users’ information needs.” The taxonomy includes Social Web Search, Social
Search, and Social Recommendation. According to the authors, Social Web Search
deals with techniques that can improve IR processes by using social
information. At the same time, Social Search is “the process of finding
information only with the assistance of social entities, by considering the
interactions or contributions of users.” In this way, Social Search is
associated with platforms specifically designated for managing social data,
such as Facebook or Twitter. The authors further divide Social Search into
three categories: Social Question&Answering (Q&A), Social Content Search, and
Social Collaborative Search.

##  3 Paper Selection Methodology

Before presenting the literature according to the taxonomy of Figure 1, we
briefly describe the methodology used to identify relevant works on Social
Search. Table 1 reports summary data. We started collecting documents
following the Social Search taxonomy proposed in [3]. For each topic (first
column in Table 1), we first retrieved papers from Google Scholar using the
keywords specified in Table 1. We filtered results based on the year of
publication (we choose to consider only the most recent works, from 2018 on),
and we selected the first 20 results returned. We then analyzed these papers
and kept only the most relevant ones according to the topics. We built the
corresponding graph for each chosen work with
ConnectedPapers111https://www.connectedpapers.com, which returns 41 related
papers for the queried work. As done for Google Scholar, we only kept those
from 2018 onwards. The total number of papers collected through this process
is reported in the “CP” (Collected Papers) column in Table 1. These papers
also include articles cited by the most relevant works (according to our
opinion) and those citing each work. The set of collected papers is the
starting point for our detailed analysis. Specifically, after reading those
papers, we kept only a subset of them, whose number is shown in the “SP”
(Selected Papers) column of Table 1.

Further scrutiny of those papers leads us to report a subset of them in the
survey, as indicated in the “RP” (Reported Papers) column of Table 1. The main
criteria we used for filtering those papers are: (i) impact of the work in the
community (by analyzing citation scores); (ii) originality of the proposed
approach concerning the literature; (iii) quality of the publication venue;
(iv) level of innovation on the topic. Moreover, an important factor that
influenced the exclusion or consideration of a paper was the use of social
data: we focused more on works that integrated data relating to social
relationships between users.

Topic |  Keywords |  CP |  SP |  RP
---|---|---|---|---
Expert Finding |  Expert Finding in Community Question&Answering, Expert Representation, Expert Recommendation, Question Routing, User Expertise, Expert Ranking |  240 |  23 |  15
Question Retrieval |  Question Retrieval in Community Question&Answering, Duplicate Question Detection |  190 |  27 |  11
Answer Selection |  Answer Selection in Community Question&Answering, Answer Ranking |  105 |  29 |  5
Content Search |  Social Content Search, Social Platforms, Social Information Retrieval, Content Search in Microblogs, Query Suggestion, Query Expansion, Query Auto-Completion |  240 |  29 |  10
Social Collaborative Search |  Collaborative Search System, Collaborative Web Searching, Collaborative Information Seeking |  208 |  22 |  10
Table 1: Keywords and number of collected (CP), selected (SP) and reported (RP) papers for each topic. |  |  |  |

##  4 Expert Finding in Social Q&A

Community Question&Answering sites host domain-specific communities where
people share their experiences to help other users through point-to-point
interactions. However, the engagement of expert users in the community is
complex, and not receiving any answer can create a sense of frustration in a
user posting a question. A question can fall through unanswered due to several
factors, but one of the main reasons behind this behavior is that the system
does not propose the question to the right expert users. This consequence is
reasonable, considering the large number of questions submitted to these sites
daily. The task of adequately matching user questions and expert users is
commonly called the Expert Finding Task. Previous surveys divide the existing
literature according to the specific methodology used to address the above
problems. In [13], the authors classify the studies into eight categories:
simple models, language models, topic models, network-based methods,
classification methods, expertise probabilistic methods, collaborative
filtering methods, and hybrid methods. Instead, in [14], the authors classify
the studies into matrix factorization-based models, gradient boosting tree-
based models, deep learning-based models, and ranking-based models. We
formalize the problem as follows:

###### Definition 1

Given a question $\bm{q}$ posted by a user $\bm{u}$, a set of $\bm{n}$ experts
$\bm{E=\\{e_{1},e_{2},...,e_{n}\\}}$, and a positive integer $\bm{k\leq n}$
find the best ranked list of $\bm{k}$ experts who are more likely to answer
correctly question $\bm{q}$.

This task involves mainly two phases:

  1. 1.

Expert and Question representation: the representation of experts should
capture their _knowledge_ and _interests_. Usually, past questions and answers
are the information source considered to extract topics addressed by the
experts and their interests. The _reputation score_ is also essential as it
captures the community’s view of the expert quality. Similar to the reputation
score, some studies compute an _authority score_ that involves the
construction of an interaction network on which researchers apply link
analysis techniques. The final representation model should cover expertise,
reputation, users’ activity, and the experts’ likelihood of answering the
question. Analogously, question representation models the topic involved in
the question and, in some cases, the interest and profile of the asker.

  2. 2.

Expert-Question similarity and ranking: a score for each expert-question pair
is computed and used to rank the experts based on some measure of interest,
e.g., the likelihood that the expert will answer the given question.

Unlike these prior works, we classify existing literature according to the two
phases described above, as we think this represents a more comprehensive way
of characterizing the topic. Therefore, the following subsections are devoted
to presenting two required modules in any workable Q&A system.

###  4.1 Expert and Question Representation

The expert and question representation phase aggregates all the user aspects
(i.e., activities and reputation) to capture their expertise and represent
queries, permitting an effective matching between them. The first group of
work focuses on encoding information about the user’s profile, generally
described as features fed into conventional topic modeling or word embedding
tools. In [15] authors propose a method to detect a group of possible
answerers based on their history of questions and answers. The authors use
features from the user’s profile and the community to model the user.
Precisely, they formalize a measure called _TagTrueness (TT)_ that captures
the tendency of a user to answer questions with the tags in her profile:

$TT=\dfrac{\text{Total Number of Accepted Answers of Profile Tag}}{\text{Total
Number of Accepted Answers}}$

Given a question, users belong in the set of possible experts if they have
given at least one accepted answer to a question with the same tags. Each user
is associated with a score computed considering TT, Number of Tags in the
Profile (NPT), Number of Accepted Answers (NAA), the Average Number of
Accepted Answers with Tags in Common between question and profile (AACT), and
a Recent Activity Measure (RAM). The idea is that users who have recently
answered questions with tags in common with the question should have a higher
score than users who have responded to similar questions in the past.. The
score is computed as follows:

$Score=\mbox{TT}\cdot log(\mbox{NPT}+10)\cdot
log(\mbox{NAA}+10)\cdot\mbox{AACT}\cdot\mbox{RAM}$ (1)

In the same group (user’s features modeling), [16] proposes a framework
combining textual information, community feedback, and user interactions. To
capture the semantic relationship between question and user expertise, authors
use word embeddings to represent users’ answers and degrees of expertise. The
proposed approach overcomes the well-known limitations of Latent Dirichlet
Allocation (LDA) [17] for topic modeling. On the other hand, the original
approach of [18] is to define embeddings that favor experts who provide few
but high-quality answers to complex questions rather than experts who provide
solutions that do not satisfy the user or answer only accessible questions.
Finally, [19] accounts that sometimes it is possible to learn from past
answers/questions which topic a user could face with some expertise. For
example, suppose it is possible to deduce from the user information that she
is a researcher, the system could forward the question “How can I write a
scientific paper?” even if she had never answered this topic. To this aim, the
authors define a new approach based on the Recurrent Memory Reasoning Network
(RMRN), composed of different reasoning memory cells that implement attention
mechanisms to focus on various aspects of the question and retrieve relevant
information from the user’s history. Similarly to [16, 19], word embeddings
are also used in [20] to enrich the new question with tags semantically
similar to those already present to include also experts that, considering
only the original tags, would not be considered.

Even if the studies above consider some community-based features, they do not
explicitly consider the social network between answerers and askers. Expert
Finding approaches based primarily on text, such as those mentioned above, aim
to quantify the user’s knowledge about a topic based on the questions/answers
asked/given in the past. On the other hand, network-based approaches aim to
determine the user’s authority within the network by considering the past
activities, independently of the new question. In [21] authors introduce three
new concepts: question hardness, question answerer association, and answerer’s
performance-enhanced authority. These three concepts are based on three
assumptions, respectively: (i) complicated questions require a good knowledge
of the topic and help identify the experts; (ii) an answerer is very familiar
with a given topic if she has answered multiple questions on that topic; (iii)
good answers usually get many votes, so merging information from the network
can help. The framework includes the Knowledge Analyzer component aimed to
estimate an answerer’s knowledge of a topic by combining the output of the
following three components:

  * •

Question similarity finder: it computes the similarity between the new
question and archived questions by treating them as documents and using Query
Likelihood Language (QLL) [22];

  * •

Question answerer association provider: it establishes the relationship
between a question and each answerer using conditional probability;

  * •

Question hardness estimator: it computes the complexity of the question using
features such as Time Response and Number of Answerers. The assumption is that
the delay in receiving the first answer to the question and a low number of
replies are indices of the complexity of the question.

The Authority Analyzer is network-based and consists of two parts: Network
Builder and Authority Estimator. The first deals with building a Competition
Based Expertise Network (CBEN) [23] where each node is an answerer, and there
is a direct link from $a_{i}$ to $a_{j}$ if both users answer the same
question and $a_{j}$ is the best answerer of the question among the ones
available. The weight of the link measures how much $a_{j}$ is better than
$a_{i}$ by considering the number of votes received, and it is computed as
follows:

$v_{i,j}=\sum\limits_{q\in Q}\delta_{i}^{j}(q)Perf(a_{i},q)$

where $\delta_{i}^{j}(q)$ is equal to $1$ if $a_{i}$ and $a_{j}$ are co-
answers of $q$ and $a_{j}$ is the best answer, 0 otherwise. Instead, the
factor $Perf(a_{i},q)$ incorporates into the computation the quality
information of the answer $a_{i}$ concerning the question $q$, and it
considers the number of votes received by $a_{i}$ for $q$ and the maximum
votes received by any answerer for question $q$. The Authority Estimation,
instead, calculates the authorship of the answerers using link analysis
techniques such as AuthorRank [24] and Weighted HITS [25].

Other variations of this approach have been proposed: [26] presents a
Preference-Enhanced Hybrid Expertise Retrieval (PEHER) system that includes a
preferability estimator. The preferability estimator incorporates the
preferences of an answerer $a\in A$ by considering two preferences: _intra-
profile_ and _inter-profile_. The intra-profile ones capture a given user’s
preferences for specific terms. In contrast, inter-profile preferences capture
the features of the particular answerer concerning all other relevant
answerers in the system.

###  4.2 Expert-Question Similarity and Ranking

Depending on the models adopted for representing experts and user queries, we
have different solutions for matching and ranking experts likely to answer a
given question effectively. Most works cited in Section 4.1 also address this
problem. Therefore, we first briefly describe the approach proposed by them.

In [15], after ranking the experts using the score computed by Eq. 1, the
candidates are re-ranked based on their answering behavior by considering if a
user is generally more active during the day or night, during the week, or on
the weekend. Given a question and the corresponding posting time, the experts
on the question topic are re-ranked by putting in the top positions the
experts that usually are active at that specific time of day. Similarly, Kundu
et al. [27] rank the experts by combining their activeness over time with
other features measuring expertise and answering intensity. Mumtaz et al. [16]
rank the experts using a weighted combination of the cosine similarity between
users’ expertise and question representations. In [18], the authors apply the
Learning to Rank methods to the expert ranking task. Specifically, they use
the LambdaMART algorithm to learn a ranking model trained on the pairs
(Questions, Users) modeled with the four categories of features described in
the previous section and labeled based on the historical interaction
information. For a new question, the candidate experts are ranked by the score
predicted by this model. In [21], the knowledge and authority scores described
in the previous section are combined using a Reciprocal Rank Fusion (RRF)
technique [28]. RRF is also used in [26] to generate the expert list.

Other works in the literature focus more specifically on the issue of Expert-
Question Similarity and Ranking, while they do not deal mainly with the
problem considered in Section 4.1. Conceptually similarly to [21, 26], in
[29], the authors develop a network-based framework combining four features:
(i) the similarity between the question’s content and the user profile; (ii)
the similarity between question topics and user topics; (iii) the similarity
between asker and answerer in the network; (iv) the activity level of the
user. Authors compute the similarity of the content through cosine similarity,
incorporating the Term Frequency - Inverse Document Frequency (TF-IDF) to
weigh the words differently. Instead, they first use LDA to extract topics and
then the TF-IDF again to calculate the similarity between topics. For the
similarity between answerer and asker within the social network, they build a
graph with an edge between two user nodes $u_{1}$ and $u_{2}$ if the user
$u_{2}$ has answered a question of user $u_{1}$. Authors compute the proximity
of two nodes by performing Random Walks with Restart (RWR): precisely, given
the graph’s size, they apply the fast RWR algorithm proposed in [30], whose
intuition is to divide the graph into small communities connected by bridge
edges and combine the RWR scores computed on these communities. Finally,
authors use metrics available on the platforms for the answerer’s activity
level, such as the reputation score or the number of awards received. Lastly,
the authors compute the final score by combining these four factors weighted
by parameters learned using the history of the response activity. The study of
[31] also considers the answers’ quality and consistency. The authors propose
a model based on an adaptation of the bibliometric g-index to measure the
consistency of the user in providing high-quality answers. They also compute
another score that measures the user’s reputation (REP-FS) using voter
reputation, up-vote to down-vote ratio, participant-based reputation, and
popular tags. Weighted Exp-PC combines the two scores to compute the final
user’s expertise.

Another aspect to consider when routing new questions to experts is that, in a
cQA site, each user can cover both the asker and answer role simultaneously,
which may vary over time. In [32], the authors represent the user considering
the evolution of her role over time, and they study how much this aspect can
improve the question routing process in a cQA site. To track the evolution of
user roles, they propose a Time-aware Role Model (TRM) based on LDA to model
the latent topical relationship between question content, user, time, and
role.

Moreover, many approaches do not consider the intimacy between the asker and
the answerer. In [33], the authors consider this factor based on the
assumption that an expert has more incentive to answer a question posted by an
asker if she is interested in the topic and if there is an intimacy between
the two. They propose a User Intimacy Model (UIM), i.e., an LDA-style model
that incorporates social interactions in modeling and learning the intimacy
between the entities on topics.

Finally, one further aspect to consider in the Expert Finding task is the
probability that the expert to whom a given question is forwarded will
respond. According to [34], to approach the problem, it is necessary to find a
ranking function that quantifies a user’s expertise and the likelihood that
they will answer the question simultaneously. To this end, the authors exploit
the _Social Identity Theory_ [35, 36] to define a graph joining different
possible experts who could answer the question.

##  5 Question Retrieval in Social Q&A

Users often post questions that already exist in the system and have already
been answered. Posting an already asked question on a cQA site can negatively
affect user engagement by not getting any answers from community experts. It
also causes an unnecessary increase in the amount of data stored by the
system. For this reason, cQA sites provide a service whereby, given a user
question, they return all past questions which may be the same or very similar
to the user’s question. We can formally define the task as the following
retrieval task:

###### Definition 2

Given a query $\bm{q}$, a historical set of already posted and successfully
answered questions $\bm{Q=\\{q_{1},...,q_{m}\\}}$, and a positive integer
$\bm{k}$, return a ranked list of the $\bm{k\leq m}$ historical questions that
are the most similar to $\bm{q}$.

At this point, the user submitting $q$ is presented with the answers of the
$k$ similar past questions and can quickly decide whether the answers satisfy
the information need or whether to post the new question. However, the
previous task is complex: different users with the same information need can
write semantically equivalent questions using completely different sentences
from a syntactical point of view. A significant body of works tries to
overcome this semantic gap problem using NLP techniques and user metadata
information.

As highlighted in Figure 1, the problem consists of two logical phases:
Metadata and Question Representation and Query-Question Similarity and
Ranking. The two phases are conceptually similar to those in the Expert
Finding problem but now refer to a different task. In the Metadata and
Question Representation phase, the proposed approaches address the problem of
representing the questions and which user metadata could improve the
performance of the Question Retrieval service offered by the site. Instead, in
the Query-Question Similarity and Ranking phase, the proposed approaches
develop methodologies to exploit the content of the questions and leverage the
user information available to provide accurate matches between a new query and
existing questions. We dedicate the first subsection to the Metadata and
Question Representation. Instead, we describe the Query-Question Similarity
and Ranking methodologies dividing them into three subsections corresponding
to three approaches that, as shown in Figure 1, are alternatives to address
this problem: (i) Text-based Similarity and Ranking, which only use only
textual data; (ii) Social and Text-based Similarity and Ranking, which also
introduces social aspects by modeling users and interactions; (iii) Domain
Adaptation, where data is adapted or integrated to improve performances.

###  5.1 Metadata and Question Representation

A very popular technique to represent questions and answers relies on Word
Embeddings, which transforms a sentence into a fixed-size dense vector,
somehow capturing the semantics of the sentence. In [37, 38] authors use
GloVe, while in [39, 40, 41] authors use Word2Vec. For example, in [40], after
the first filtering phase in which authors apply text cleaning, tokenization,
stopwords removal, and stemming, each question is represented as a list of
terms. Each word is mapped into a fixed-length vector using a pre-trained
Word2Vec model. Considering that short texts amplify the mismatch problem, the
authors propose expanding the query with other terms close in the semantic
space created by the Word2Vec model.

An interesting study from the question representation point of view is
presented in [42]. Authors try to address five problems when representing
questions: synonymy, polysemy, word order, question length, and data sparsity.
The framework consists of two steps:

  1. 1.

Word and concept learning: they use a Skip-gram model to learn the embeddings
of the words. In this way, the meanings of words are distributed across the
dimension of a semantic space. Furthermore, previous works show that
conceptual information can handle the polysemous problem: in [43], the authors
compute the contextual representation of a word by combining its vector and
the most context-appropriate concept vector that delivers an unambiguous
meaning. For example, considering two queries [python zoo] and [python
string], the embedding of the first “python” is obtained by combining the
intrinsic vector of “python” and the concept vector of “animal category.” In
this way, the model is context-dependent. The problem with the above approach
is that the objective function first locates the right concept, then searches
for a word underneath the chosen concept. In [42], the authors add a
regularization function to overcome the problem, which aims to leverage the
concept information regarding the knowledge base without considering the
context. Considering the context-independent relation could avoid the error
introduced by choosing the wrong concept vector;

  2. 2.

Question and answer embedding learning: to encode concept, syntactic, and word
order information, authors propose the High-level feature embedded
Convolutional Semantic Model (HCSM). The model assumes that if two questions
have similar answers, they could have semantic relations. HCSM is a
convolutional architecture composed of wide convolution (without zero-padding)
and pooling. Authors design three variants: the first variant emphasizes the
relation (i) between concepts, (ii) between words, and (iii) between concepts
and words. The second variant emphasizes only the first two relations, (i) and
(ii). The last variant combines the word-embedding question matrix and concept
embedding question matrix before inputting them to the network.

###  5.2 Text-based Similarity and Ranking

Joty et al. [44] provide a remarkably complete approach to addressing this
problem. Specifically, the authors observe that some questions may have a lot
of answers, some significant and some useless. A long thread of answers for a
question can bore the user who decides not to read it and post a duplicate
question. The authors thus try to accomplish the two tasks by introducing a
third task: checking whether an answer in a long answer thread is good or not.
The three sub-tasks are defined as follows: (i) determine whether the $m$-th
answer $c^{i}_{m}$ in a given thread is good or not for its related question
$q_{i}$ (task $A$); (ii) calculate whether an archived question $q_{i}$ is
relevant to the new query $q$ (task $B$); (iii) rank each answer for $q_{i}$
as relative or non-relative for $q$ (task $C$). These tasks have dependencies:
if an answer $c^{i}_{m}$ is good for the archived question $q_{i}$ and $q_{i}$
is relative to the query $q$, then $c^{i}_{m}$ is a good answer for $q$; if an
answer $c^{i}_{m}$ is relevant to $q$, then the question $q_{i}$ to which it
refers is also inherent to $q$. Therefore, the idea is to jointly exploit the
interactions and dependencies between these tasks to solve the Question
Retrieval and Answer Selection tasks. The framework consists of two phases:

  1. 1.

A Deep Neural Network (DNN) is used to solve the three tasks separately. The
subtask-specific hidden layer activations are used as the embedded feature
representations for the second step, where a Conditional Random Field (CRF)
model performs joint learning with global inference exploiting the
dependencies between the three subtasks. As shown in Figure 2(a), the input
for the first step is a tuple $(q,q_{i},c^{i}_{m})$ composed of the new query
$q$, a historical question $q_{i}$, and the $m$-th answer $c^{i}_{m}$ of the
question $q_{i}$. These elements are converted into fixed-length vectors
through syntactic and semantic embeddings and passed to a feed-forward Neural
Network intending to learn task-specific embeddings for the three tasks
separately. Considering task $A$, shown in the lower part of Figure 2(a), the
vectors of answer $c^{i}_{m}$ and question $q_{i}$ are concatenated and passed
to the first hidden layer of the DNN. The output is passed to a task-specific
hidden layer that combines these signals with a pairwise similarity between
$c^{i}_{m}$ and $q_{i}$. The merging between the final hidden-layer output and
the pairwise similarity gives the final task-specific embedding. The last
layer outputs the prediction variable $y^{a}_{i,m}$. Similarly, the authors
compute the embeddings of tasks $B$ and $C$;

  2. 2.

The authors build a large undirected graph consisting of six sub-graphs:
$G_{a},G_{b},G_{c}$ for the three tasks and $G_{ab},G_{ac}$, $G_{bc}$ for the
three possible interactions between the different tasks. The _intra-subtask_
edges are created for the variable of the same sub-task, i.e., $y^{b}_{i}$ and
$y^{b}_{j}$. Instead, regarding _across-subtask_ edges, authors exploit three
different types of connection: (i) null or no connection, (ii) 1:1 connection
between tasks $A$ and $C$, i.e., $y^{a}_{i,m}$ and $y^{c}_{i,m}$, and (iii)
M:1 connection to $B$, where all the nodes of subtasks $A$ and $C$ are
connected to the thread-level $B$ node. Each node $u$ is associated with its
embedding vector $x_{u}$ and its output variable $y_{u}$. Each edge that links
the two nodes $(u,v)$ is associated with an input-feature vector
$\mu(x_{u},x_{v})$ composed of node-level features, which are (i) comment
features and (ii) meta-features. Dependencies between the output variables are
modeled by learning node and edge factor functions that jointly optimize a
global performance criterion using the CRF model [45].

The results of the experiments show that the DNN alone achieves good results.
Still, the CRF model allows a global inference on a graph structure,
considering the dependencies between the different tasks.

Figure 2: Graphical representation of the cQA framework in [44]. On the left
(a), we have three feed-forward neural networks to learn task-specific
embeddings for the three cQA subtasks. On the right (b), a global conditional
random field (CRF) models intra- and inter-subtask dependencies.

Other approaches provide less rich but noticeable solutions for the Similarity
and Raking task. The authors of [46] propose an unsupervised framework to
compute the similarity between two questions, called Reduced Attentive
Matching Network (RAMN). It is based on an attention autoencoder to
semantically represent the question, pre-trained using a large amount of
unlabeled data, and an attention mechanism to focus on the different parts of
the sequence of terms. A different approach to capture the similarity between
questions is the one designed in [47], where authors use a Convolutional
Neural Network (CNN) whose input is a set of six matrices deriving from
different measurements of similarity between question pairs. Four matrices are
vector distances, and two are interaction measurements. These matrices are the
input of a Deep CNN with a last fully connected layer which outputs the
probability of the question pair being duplicate or related.

Finally, [38] uses a Siamese NN to address the problem. Two questions, $q_{1}$
and $q_{2}$, are passed as input to two CNNs to capture their most meaningful
features, $\hat{q_{1}}$ and $\hat{q_{2}}$. Two additional operations are then
performed on $\hat{q_{1}}$ and $\hat{q_{2}}$: (i) matrix multiplication to
compute the similarity and (ii) matrix reduction to compute the closeness
between the two questions. The result of these two operations is combined with
$\hat{q_{1}}$ and $\hat{q_{2}}$ and given in input to a Multi Perceptron Layer
(MLP) that outputs the duplicate and non-duplicate label.

###  5.3 Social and Text-based Similarity and Ranking

In [39], the authors propose a framework that simultaneously learns the
content of questions, their category, and the information in the users’ social
network to represent the intent of the question richly. The framework is
represented in Figure 3. The assumptions under this approach are two: (i) a
user tends to ask questions similar to friends and colleagues because it is
likely that they have common interests, and (ii) questions in the same
category are more similar than those belonging to different categories.
Considering the above observations, the authors build a heterogeneous graph
composed of three nodes: users, questions, and categories. A question node is
connected to the category node it belongs to and the user who posted it. This
graph is used to sample paths that include different types of nodes applying
deep Random Walks inspired by [48]. Questions are represented using pre-
trained word embeddings and LSTM. User embeddings are instead learned from
matrices of social relations $M\in R^{(m\times m)}$ where entry $s(i,j)=1$ if
users $i$-th and $j$-th are friends. At inference time, given a new query $q$
along with asker information, the proposed approach learns, through this
heterogeneous network, the latent representation of the queried question.
Specifically, it learns the heterogeneous network of graph $G$ and then embeds
the asker $u$ and the queried question $q$ to return the top-$n$ similar
questions submitted in the past.

Figure 3: The overview of heterogeneous network ranking learning. (a) The
heterogeneous CQA network is constructed by integrating questions content as
well as corresponding categories and social network relations. (b) A deep
random walker is walking on the heterogeneous CQA networks to sample the data
paths. (c) The questions, askers and categories are encoded into fixed feature
vectors and used to train the model for scoring the questions [39].

Finally, in [49] authors address the problem of detecting _misflagged_
duplicate questions, i.e., questions marked erroneously as duplicates. It is a
complementary approach to the one described in [39], and authors use social
information related to users (such as their reputation and the scores of their
answers) by defining a set of features that help identify errors in marking
duplicates.

###  5.4 Domain Adaptation

Finding duplicate questions is a costly process, which is alleviated when
experts manually mark duplicate questions. However, some cQA platforms do not
offer this functionality, making the Duplicate Question Retrieval process more
intricate. One solution is to use Domain Adaptation from another forum. The
authors in [37] propose an approach that uses Adversarial Domain Adaptation to
help cQA sites that do not have data on duplicate questions. Authors study
when the transfer domain can work and what domains’ properties are important
for successfully transferring the knowledge from the source domain. The model
consists of a question encoder, a similarity function, and a domain adaptation
classifier. The encoder maps the sequence of tokens representing a question
into a dense vector. The cosine similarity function predicts if two question
vectors $v_{1}$ and $v_{2}$ are duplicates. Finally, the domain adaptation
classifier predicts whether a given question is part of the source or target
domain. The encoder is trained not only to perform well with source data but
also to be incapable of distinguishing between question pairs from the source
vs. target domain. The adversarial component reduces the difference between
the target and source distributions. For the domain adaptation component, the
authors choose the Wasserstein method [50], which diminishes the Wasserstein
distance between the source and target distributions. The experiments are
performed using three datasets: StackExchange, SprintFAQ, and Quora. The first
two datasets are focused on technical domains, while the Quora dataset covers
many different topics. Results show that Domain Adaptation works well when the
source and target domain are similar (i.e., with StackExchange and SprintFAQ
datasets).

Finally, Domain Adaptation is also used to exploit questions written in
different languages concerning the target query. These methods include
translating the original question written in language L1 to the target
language L2 and then using a monolingual Question Retrieval model. A relevant
example of this approach is presented in [51].

##  6 Answer Selection in Social Q&A

Answer Selection is an essential process in cQA as it allows to automatically
identify the most valuable answers for an incoming question and propose them
to the user to increase her engagement and satisfy faster her information
need. The two main problems for this task are that: (i) answers are usually
noisy, and (ii) it is likely that they address different facets of the
associated question, not all relevant to the current information need. The
problem can be formalized as follow:

###### Definition 3

Given a new query $\bm{q}$ and a similar historical question $\bm{\hat{q}}$
along with its thread of $\bm{n}$ answers $\bm{A=\\{a_{1},...,a_{n}\\}}$, the
Answer Selection problem asks for selecting the best answer in $\bm{A}$
relevant for $\bm{q}$.

Several state-of-the-art works cast the Answer Selection problem to a
classification or a ranking task and face it by considering mainly the text of
the candidate’s question and answer pairs. Notable examples of this approach,
identified as answer-based methods in the taxonomy presented in Figure 1, are
[52, 53], where semantic self-attention and co-attention mechanisms are
exploited to focus on the interaction between questions, answers, and their
contexts. In the following, instead, we concentrate our attention on studies
that build a graph to model questions, answers, and user expertise, trying to
exploit hidden relations between all the entities involved in the selection
process.

###  6.1 Graph-based

The work in [54] proposes a framework that jointly addresses the _redundancy_
, _heterogeneity_ , and _multi-modality_ of questions and answers of the CQA
platforms. Text and images are first converted into feature vectors using word
embeddings and the ResNet deep CNN model [55], respectively, to consider both
textual and visual content. An attention mechanism is then applied to reduce
redundancy by constructing a question-answer attentive interaction matrix that
focuses on useful word-pair interactions. Moreover, the authors build a
heterogeneous network in which the vertices are questions, answers, users, and
tags. Instead, the edges represent the relations among these entities. A meta-
path heterogeneous network embedding algorithm is applied to leverage the
social information, which allows the discovery of special neighbors of nodes
through specific meta-path schemes. For example, considering the schema
“answer-user-answer-question-tag,” the last vertex “tag” could be considered a
special neighbor of the first answer node in the schema. The aim is to learn
representations $\overrightarrow{u}_{q}$ and $\overrightarrow{u}_{a}$ of
questions and answers that consider indirect relations among question, answer,
user and tags. Finally, based on the idea that the similarity between word-
pair can contribute differently to the final score in a different context, the
authors incorporate context information in the similarity computation.

Liu et al. [56], observe that modeling user expertise only through historical
answers as in [52, 53] works well only if sufficient data is available. They
thus investigate the impact of static and dynamic social influence on the
answer selection problem. They claim that a form of static influence is
applied indirectly by other experts or users a person follows in the
community: users with a passion for a field will follow experts in that field
and read their posts/answers to improve their knowledge of the field. Instead,
the dynamic influence refers to dynamic personal interests: users can have
different interests, but only a few are activated when they face a new
question. Considering all the user interests as equally important can thus
negatively affect expertise modeling. Relations in cQA sites are modeled as a
directed graph where the nodes are users, and there is an edge from $u_{1}$ to
$u_{2}$ if $u_{1}$ follows $u_{2}$. The framework is illustrated in Figure 4.
Authors compute social influence in two phases. During the first phase, the
user embedding is the concatenation of the user interest feature vector
$x_{u}$ (obtained using the word embedding of explicit user topics) and a
randomly initialized latent vector $p_{u}$. The second phase updates the
embedding with $k$ aggregation steps by incorporating, at each stage, the
information of the user’s neighbors nodes. Moreover, the authors model user
interest dynamics through a context-based dynamic representation. The
embeddings of topics representing user interests are stacked, thus composing a
topic embedding matrix. They learn a hidden representation of each topic
through a dense layer. Assuming that the answer given by the user is a
personal expression containing information about the topics activated at that
moment, the authors compute the weights for each topic using the specific
answer by a multi-layer perceptron attention mechanism. Personal interests,
instead, consider the weighted sum of the interesting topics and the global
user interest obtained by applying the mean-pooling over all topic
representations. The representation $u$ of user context is finally obtained by
concatenating social influence and personal interest information. Answer
selection is computed by a matching layer that takes a triple $(q,a,u)$ as
input and outputs the matching score for ranking the candidate answers.

Figure 4: Overview of the proposed SCAD model. It encodes the user expertise
by jointly considering the dynamic personal interest and the static social
influence. The aggregation process of the social influence takes K = 2 as an
example [56].

Similar to [56], the authors of [57] also remark that users have many
interests, but only a few are relevant for a specific question. They address
the answer selection process by learning a Graph Neural Network (GNN) from a
graph modeling the relations among questions, answers, and terms, weighted
with TF-IDF values to capture their different importance for answers and
questions. They introduce a novel GTAN (Graph-based Tri-Attention Network)
model used to encode answer correlation into their representations. Based on
the GNN-representation, target-aware answerer representation, answer-specific
question representation, and context-aware answer representation are computed
through attention mechanisms. These three representations are used to measure
the final score for the answers. The approach outperforms the one used in
[53], demonstrating the possibility of explicitly encoding answer correlations
by GNN modeling and learning answer-specific question representations and
target-aware respondent representations.

##  7 Social Content Search

Social Content Search refers to the process of retrieving relevant content on
social platforms by exploiting information such as clicks, social annotations,
social interactions, and personal interests. We broadly divide this body of
works into two classes presented in Sections 7.1 and 7.2, respectively. As
shown in Figure 1, the two classes are alternatives to face the Social Content
Search task. Approaches in the first class do not modify the issued query and
use social information to determine the relevance of the content that possibly
answers the query. Instead, approaches in the second class manipulate the
issued query, exploiting social information sometimes.

###  7.1 Social Content Search without Query Modification

Most of the time, Content Search uses social information to define specific
features in a more complex representation of users, which also involves
representations of the content they have generated. In this sense, social
information should be defined as any information about the interaction between
users or between users and the content they provide (e.g., likes, retweets)
available on the social platform.

The work in [58] is a relevant example in this class. Content modeling always
remains essential for retrieval, and, as discussed above, LDA is widely used
in this area. The LDA approach has problems with short texts and sparse data.
Sparsity is observed mainly on social platforms like Twitter, given the limits
imposed on the length of posts. To overcome the problem, in [58], the authors
propose a new pooling scheme for Twitter topic modeling that groups tweets
based on authors who belong to the same community. The scheme is called
Community Pooling and makes use of a social graph. The nodes in this graph are
users, and an edge connects two users based on their retweet behavior. Authors
extract communities using the Louvain method for community detection [59]. The
authors associate each community with a document containing all author’s
tweets who belong to the community. This way, the number of documents is
small, but the documents have enough words for the LDA algorithm to work well.
The method is compared with baselines that group tweets by author, hashtag, or
thread of replies under a tweet. The methodology works well for the document
retrieval task, where, given a query, the model returns the most similar
tweets based on their LDA topic similarity.

Xu et al. [60] use a conceptually similar approach, tailored to platforms
allowing for annotation of documents. Specifically, each document is
associated with a “document” vector, which is the outcome of an LDA on the
document to extract its topic distribution, and a “social” vector, which
represents the distribution of topics for users who annotated the document.
Authors of [61] focus on Twitter and design a system whereby a range of
information related to tweets is used for a complex ranking process to define
the relevance of tweets. Specifically, features considered include (i) static
features (e.g., the creation time, influence of author); (ii) real-time
features (e.g., number of social actions received); (iii) query features
(e.g., query terms and number of clicks for query results); (iv)
personalization features (e.g., the follower-followee graph). It also includes
specific information related to the platform’s social features, used to
identify relevant tweets given a specific query.

Similarly, [62] focuses on Facebook instead of Twitter. The goal is to define
a proper embedding of both the user issuing the query and the possible
documents to be provided. In addition to textual features, the embedding
includes social features extracted from the interaction between users and
between users and content considered for the query.

###  7.2 Query Expansion and Suggestion

To improve the performance of the retrieval process and increase the number of
relevant documents returned as output, one of the most common techniques is
Query Expansion (QE), which adds new terms to the original query. Formally:

###### Definition 4

Given a query $\bm{q={t_{1},...,t_{n}}}$ composed of $\bm{n}$ terms and a
positive integer $\bm{k}$, the Query Expansion (QE) approach adds $\bm{k}$
terms to $\bm{q}$ that are likely to occur in relevant documents thus
formulating an enriched query $\bm{q*={t_{1},...,t_{n},t_{n+1},...,t_{n+k}}}$

In literature, many works perform QE by relying on document content. Instead,
the expansion of terms also should include user interests and the context in a
social context. An interesting study is the one of Bouadjenek et al. [63],
where the authors propose an approach for selecting terms for query expansion
that is based on (i) the semantic similarity between a query word and a
candidate term, (ii) the social closeness between the query and the user (how
interesting a query term can be for the user), and (iii) a strategy for
expanding user queries online. The approach consists of three phases: (i)
select candidate terms that are similar and related to the query based on
their co-occurrences with resources and users; (ii) profile the issuer user
based on interests and activities; (iii) expand the query considering both the
semantic similarity and the user’s profile. The method is based on
Folksonomies, whose definition is the following:

###### Definition 5

Let $\bm{U,T,R}$ be the set of Users, Tags, and Resources, respectively. A
folksonomy $\bm{\mathscr{F}(U,T,R)}$ is a subset of the cartesian product
$\bm{U\times T\times R}$ such that every triple $\bm{(u,t,r)\in\mathscr{F}}$
is a bookmark, representing a user $\bm{U}$ who used a tag $\bm{T}$ to
annotate a resource $\bm{R}$.

The approach consists of two parts. The offline part deals with building the
social graph and converting it into a tag graph of a folksonomy $\mathscr{F}$,
where two tags are connected if they are semantically correlated. In addition,
the offline part is also responsible for building and updating user profiles.
The online part performs query expansion based on the tag graph and user
profile. There are two approaches to constructing the tag graph, starting from
the folksonomy:

  * •

Extracting semantics from resources: semantically related tags are expected to
occur on the same resources. The semantic relationship is computed through
similarities that require a reduction in the dimensionality of the tripartite
graph $\mathscr{F}$ in a bipartite graph. Specifically, the authors use a
function that performs a projective aggregation on the entire folksonomy
$\mathscr{F}$, resulting in a bipartite Tag-Resource graph. Then, a $T_{R}$
tag graph is extracted in which two tag nodes are connected by a weighted edge
according to the Jaccard, Dice, or Overlap similarity metrics;

  * •

Extracting semantics from users: the same users use related tags to annotate
resources. Similar to the previous case, the authors perform a projective
aggregation on the resource dimension by creating a Tag-User bipartite graph.
Hence, they obtain a $T_{U}$ tags graph using the same metrics used to obtain
$T_{R}$.

Choosing only one method can lead to a loss of information, so the authors
combine the two graphs to get a single $T_{UR}$ tag graph that incorporates
all the information. The edge between two tags is weighted using the Weighted
Borda Fuse (WBF) technique. As explained below, the authors use the graph to
extract terms semantically related to a given query term. As a further
refinement, the user’s interests should be considered to return a result
personalized for each user. In the context of folksonomies, a user annotates a
resource using tags that summarize her understanding of that resource. These
tags can thus be considered a good summary of users’ interests. Therefore,
each user is represented as a set of tags, and their weight is computed by
adapting the TF-IDF measure based on the user’s profile. In the online part,
the authors consider the similarity between a query term $t_{i}$ and a
potential candidate term $t_{j}$ for query expansion based on both tag graph
and user interests. The first step is to extract the user profile, given a
query and its issuer. Then, the authors extract all adjacent tags for each
query term using the $T_{UR}$ graph. For each candidate tag $t_{j}$, they
compute a ranking metric that combines (i) the similarity of a query term
$t_{i}$ and the candidate term $t_{j}$ given by the graph, and (ii) the
measure of user interest for tag $t_{j}$ calculated considering the similarity
between $t_{j}$ and all the tags in the user profile. Finally, they order the
terms of the candidates according to this ranking measure.
Instead, in [64], the authors integrate social scores of users into the
similarity between query and document, which are based on two criteria:
positivity of a user and feedback.

An alternative to QE is Query Suggestion (QS), which is more powerful
concerning synonymy and polysemy problems. The QS process offers the user
alternative queries, allowing her to choose which one to explore. In [65], the
authors propose an improved Query Suggestion system for Linkedin by
simultaneously modeling structured personalized user features and unstructured
text data in a sequence-to-sequence model (Seq2Seq). Differently, Chen et al.
[66] designed a Hierarchical Neural Query Suggestion system that combines a
session-level and a user-level neural network to model a user’s short- and
long-term search histories. Finally, in [67], the authors suggest a novel
training approach that considers user feedback as additional machine
translation.

Auto-completion is another search engine feature that provides possible
completions of the query as the user is typing. An interesting study is the
one of Jaech et al. [68], where the authors show how an adaptive language
model based on a recurrent neural network can be used to generate personalized
completions.

##  8 Social Collaborative Search

Although the information search process is usually an individual action, it
may involve the collaboration of several subjects. In this context, when two
or more people collaborate to satisfy an information need, we speak about
Collaborative Search. According to Golovchinsky et al. [69], the Collaborative
Search can be studied along four dimensions: (i) the intent, which is explicit
or implicit; (ii) the mediation, applied through user interfaces or
algorithms; (iii) the concurrency, that can be synchronous or asynchronous;
(iv) the location, which can be remote or co-located. Afterward, Morris et al.
[70] suggested two additional dimensions: the role (symmetric or asymmetric)
and the medium (Desktop or emerging devices). Given the focus of this study,
we present the literature according to the following categories: (i) solutions
oriented towards specific communities, (ii) general-purpose solutions, and
(iii) solutions based on chatbots. The three categories are alternative
approaches, as illustrated in Figure 1.

###  8.1 Solutions Oriented Towards Specific Communities

With the specific aim of helping the academic world, in [71], the authors
propose SciNoon, a system capable of facilitating the exploratory search
process performed by students and researchers in their daily work. Unlike
traditional search, exploratory search requires a significant effort, mainly
to estimate the relevance of the results returned, where a direct
collaboration can help by making assessment faster and more precise. To this
end, SciNoon provides collaborators with a shared workspace of collected
articles and a chatbot that allows reporting every activity of team members.
It integrates Google Scholar suggestions for related papers and queries. It
also extracts from retrieved papers keywords related to the user search
intent. The information is organized in the workspace according to a graph-
based data model: the nodes are articles, authors, searches, and associated
results; the edges express the relationship among them. The system provides a
content-based recommendation tool that allows users to expand an article node
based on citing and cited relationships.

SearchX [72] is a collaborative search system designed explicitly for large-
scale Collaborative Search (CSE) research enabling users to implement and run
their CSE experiments. According to [73, 74], we can categorize the features
of a collaborative system along three lines: division of labor, sharing of
knowledge, and awareness. SearchX provides these features with a group chat
for direct communication, a shared workspace, and color-coding.

###  8.2 General-purpose Solutions

People often perform collaborative search tasks during daily activities, e.g.,
choosing a restaurant or a product to buy. In such a context, group members
often have to reach a consensus, which is challenging when the criteria of
each member are conflicting. There is a need for communication between the
members and awareness of each other’s preferences. To this end, in [75], the
authors propose a component that allows users to reach consensus through the
awareness of preferences, integrating it into a decision-making system. The
component, called Collaborative Dynamic Queries (CDQ), acts as a moderator
with three roles:

  1. 1.

It provides each member with awareness of the preferences of others without
the need for communication;

  2. 2.

It shows candidates who match both individual and group preferences, allowing
the identification of ideal candidates;

  3. 3.

In case of no agreement, the moderator identifies the sources of disagreement
and suggests relaxing the preferences.

The design encourages mutual awareness among members. In particular, each
member has a color associated. These colors will appear both under each filter
and next to a candidate. The former allows to view the preferences of each
member, the latter to show who may or may not agree on the choice of that
candidate as the final solution. The authors conduct two user studies and show
the moderator’s effectiveness in maintaining awareness in the group and
facilitating effective and efficient communication by reducing the effort it
usually requires.

Furuie et al. [76], instead, designed a system for personal smartphones that
allows comparing web pages shared among users of the same group. This function
is automatically invoked differently based on the orientation of the terminal.
In addition to the traditional search process involving keywords, entity-based
search has been introduced over the years where named entities replace
keywords. In [77], the authors propose Querytogether, a multi-device search
tool in which entities such as keywords, documents, and authors can be used to
compose a query or can be shared with other members.

Considering the asynchronous scenario, looking at the search process and
colleagues’ results can fill the gap of a user without background; at the same
time, her search process and results can help others discover new aspects. In
[78], building on previous work [79, 80], the authors propose LogCanvas, a
prototype of a search tool supporting asynchronous remote collaborative web
search. LogCanvas is explicitly designed for search history visualization and,
differently from other platforms, aims to reconstruct the semantic
relationship among the users’ search activities. The search process of a
session is represented by a knowledge graph consisting of all queries and
essential related concepts and their relationships using Yahoo’s Fast Entity
Linking toolkit (yahooFEL) [81]. Moreover, LogCanvas groups queries and
entities according to the topic by applying a Wikipedia-based categorization
method, i.e., TagTheWeb [82].

###  8.3 Solutions Based on Chatbots

In [83], motivated by a study revealing that users use messaging channels
external to the collaborative platform to accomplish their search tasks, two
different chatbots are incorporated into SLACK: the first bot explicitly
requests information, and the second one instead deduces this information
through conversations. The study shows that chatbots improve collaborative
search thanks to a greater awareness of team members’ activities and a greater
simplicity in communicating and sharing information and content.

SECC [84] uses a social engine that allows users to communicate across
multiple channels. However, few people may have the same information need,
leading to a cold start problem as the number of people is unsuitable for
carrying out a collaborative search. To overcome the problem, the same authors
in [85] propose to insert an auxiliary intelligent robot in a collaborative
search platform, capable of supporting the communication between the system
and a search engine. In this way, if there is only one user in the chat
channel, she can continue to search by interacting with the search engine
through the mediation of the chatbot. To this goal, the authors apply a
Machine Reading Comprehension (MRC) technique to support the conversation
between users and the chatbot. The system consists of a Search engine, a
Cluster engine, and an Interactive engine. The Search engine provides all the
functionalities of search engines, such as query expansion/suggestion and web
page ranking. The Cluster engine groups users dynamically based on the
keywords of the query they submit. Finally, the Interactive engine is composed
of the Social engine and InfoBOT. The former provides communication channels
to different user groups to allow collaborative research. The latter collects
the conversations, detects the new query, consults the search engine to
collect the related documents, and modifies the query by incorporating the
past discussions. Finally, it passes the documents and the modified query to
the MRC model that returns a response.

##  9 Open Challenges

This section identifies some open challenges that characterize Social Search
tasks. We focus primarily on those where adequate social data processing can
improve the effectiveness or efficiency of the Social Search system, as this
is the area where, overall, we have identified the main gaps and subjects for
future research:

  * •

Modelling Users and their Relations
The social aspect of Social Search platforms plays a pivotal role. From the
studies analyzed in this survey, social features are poorly exploited in some
fields (e.g., Question Retrieval). In other areas, such as the Expert Finding
task, the social dimension is widely adopted, for instance, by constructing
networks to calculate users’ authority. However, embedding social information
is often not sophisticated and is implemented through a simple combination of
features made available by social platforms. One challenge is to model users
and explicit/implicit interactions within platforms in a more structured way,
leveraging advanced methods proposed, for example, in the social network
analysis literature.

  * •

Cross-Linking
Often, the poor use of data about users and their interactions may derive from
the lack of availability of such information. It is known that different
social platforms offer different and complementary services, thus collecting
content and information that reflect diverse aspects of the user and her
social relationships. A way to improve Social Search processes is to exploit
several social sources through cross-linking mechanisms of user profiles.
Therefore, integrating cross-linking mechanisms would allow for a deeper
behavioral and social knowledge of users.

  * •

Explainability
One way to make the user more aware of the data used and processed could be to
explain why the system provides a specific result or suggestion. A user-
friendly explanation of why the user received a particular outcome could make
her more aware of the underlying mechanisms. Moreover, this would increase the
trust and make the users more inclined to share their data and favor the
system’s personalization. The extensive use of black-box models such as deep
neural networks in the proposal analyzed in this work further intensifies the
need for explainability methods. In this respect, social information may be
used to expose different levels of information to different social groups
based on the strength of social ties between users.

  * •

Scalability
The pervasive use of social platforms has led to an exponential growth of
content created and shared by users. Often, the methodologies proposed in
state-of-the-art are computationally expensive when applied to large-scale
data. Therefore, there is a need to design methodologies that can cope with
large data, guaranteeing a good trade-off between effectiveness and
efficiency. From this standpoint, social information may be used to build
effective filters limiting the amount of data searched to retrieve the few
relevant results presented to the users based on their social relationships.

  * •

Dynamicity
Users are dynamic entities that continually evolve, changing their interests,
behaviors, information, and social interactions. A Social Search system should
address the dynamicity of data to offer an effective personalized service
focused on the user. Researchers can achieve it by implementing algorithms
that can continuously update the indexes with the most recent data while
seeking a trade-off between computational costs and the accuracy of the
results. Social information may also be used to predict the near-term
evolution of social links (e.g., via advanced link prediction algorithms) and
configure search processes accordingly.

  * •

Bias Mitigation
Social Search systems must learn as much as possible about users’ interests
and behaviors to provide tailored results that can fulfill the user’s
expectations. However, the mechanism by which a system provides results based
on the user information could introduce a bias creating a situation in which
users are presented with homogeneous results from a limited set of similar
sources. In practice, however, proposing to users diversified results slightly
differing from their profile could bring additional value to the service
provided by expanding user vision and knowledge of a concept. The introduction
and evaluation of diversification methodologies aimed to mitigate social
systems’ bias have been only partially investigated and deserve further
attention.

  * •

System Evaluation
While offline evaluation is a fast and easy way to test systems,
unfortunately, it may not reflect reality and is often characterized by
biases. On the other hand, online evaluation allows directly evaluating the
degree of user engagement and satisfaction. Unfortunately, it remains
impractical or reserved for the owners of social platforms. Most of the
studies considered in this survey are based on offline evaluation methods.
Still, the research community should put an additional effort into improving
the assessment methodologies by providing standard benchmark datasets and
using the same evaluation metrics chosen based on the specific tasks
considered.

  * •

Cold Start Problem
The cold start problem represents one of the limitations that has always
existed regarding social platforms. It occurs when there is insufficient data
about a specific user to provide a good service (e.g., for new users). The
availability of rich information about the communities in the system, combined
with the scarce information about the user, could mitigate the problem.

  * •

Multimodality
Given the heterogeneity of the information present within the social
platforms, it would be convenient to exploit Deep Multimodal Fusion
methodologies to take full advantage of the data available. In particular,
with the fusion of graphics (e.g., images shared by the user), textual, and
relational information, the user profiling could be complete and represent
reality as faithfully as possible.

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