Analysis of Polarized Communities in Online Social Networks
Creators: name: Coletto, Mauro
Publisher: name: IMT Alti Studi Lucca
Year: 2016
Subjects: QA75 Electronic computers. Computer science

description: Increasingly, people around the globe use Social Media (SM)
 - e.g. Facebook, Twitter, Tumblr, Flickr, Youtube - to publish
 multimedia content (posting), to share it (retweeting, reblogging
 or resharing), to reinforce it or not (liking, disliking,
 favoriting) and to discuss (through messages and comments)
 in order to be in contact with other users and to get informed
 about topics of interest. The world population is ? 7:4 billion
 people, among them ? 2:3 billion (31%) are active social
 media users (GlobalWeb Index data, Jan 2016). In fact, these
 virtual contexts answer the human need of aggregation that
 nowadays is translated into digital bonds among peers all
 over the world, in addition to the traditional face-to-face
 relationships. Online Social Networks (OSNs), then, provide
 a space for user aggregation in groups, expressing opinions,
 accessing information, contributing to public debates, and
 participating in the formation of belief systems.
 In this context, communities are built around different topics
 of interaction and polarized sub-groups often emerge by clustering
 different opinions and points of view. Such polarized
 sub-groups can be tracked and monitored over time in an automatic
 way and the analysis of their interactions is interesting
 to shed light on the human social behavior. Even though many
 studies have been devoted to understand different aspects of
 the social network structure and its function, such as, community
 structure (For10), information spreading (BRMA12),
 information seeking (KLPM10), link prediction (LNK07), etc.,
 much less work is available on analyzing online discussions,
 user opinion and public debates.
 In this doctoral dissertation we analyze the concept of polarization by looking at interactions among users in different Online
 Social Networks. Polarization is a social process whereby a
 social group is divided into sub-communities discussing different
 topics and having different opinions, goals and viewpoints,
 often conflicting and contrasting (Sun02; Ise86). We are interested
 in studying how and to what extend it is possible to extract
 information about polarized communities by automatically
 processing the data about interactions created in Online Social
 Networks. We present the state of the art and we propose a
 novel detecting method which allows to identify polarized
 groups, track them and monitor the topic evolution in the
 discussion among users of an OSN over time by classifing the
 keywords used in the messages exchanged. We show that
 it improves the state of the art and we describe case studies
 conducted particularly on Twitter (CLOP16; CGGL17).
 The benefits in understanding user opinions are detailed in
 the first chapters. Moreover, we use the proposed methodology
 and alternatives in different application contexts: misinformation
 (BCD+14a; BCD+14b; BCD+15), politics (CLOP16;
 CLOP15; CLO+15), social behaviors (CALS16a; CALS16b),
 and migrations (CLM+16).
 A further application of opinion mining is the task of predicting
 user behavior. We discuss the limitations and the
 challenges related to this research area by looking at the context
 of political elections and by digging into a case study of
 electoral prediction. We believe that the analysis of polarized
 communities is OSNs can be used to predict collective social
 behavior, but major improvements in the field can be achieved
 by integrating several sources of information, such as traditional
 surveys, multiple Online Social Networks, demographic
 data, historical information, events, cyber-physical data.
 Therefore, polarization is integrated in a framework of analysis
 with other dimensions (time, location) to explore social
 phenomena from a social media perspective. In particular, we look at the possibility to understand European perception of
 the political refugees' crises by mining OSN data.
 The concept of polarization is related to that of controversy. Controversy
 describes the interaction among two or more opponent
 polarized communities that discuss together, often with heated
 tones. For some highly controversial topics (e.g., politics,
 religion, ethics) even though users prefer to get informed
 though polarized content originated in the communities they
 belong to, they like to share their affiliations, believes, ideals,
 convictions with external users in order to persuade them in
 joining their belief system or supporting, criticizing an event,
 a group, a party or a specific person. Highly polarization does
 not always imply controversy and vice versa. We describe the
 recent literature about controversy detection and we propose a
 machine learning approach which takes into account features
 related to the social network and to conversational interaction
 patterns. The model is able to identify controversy in a
 conversation without any feature related to the content of the
 interaction. The features are deeply analyzed and the accuracy
 of the model is discussed.
 We finally explore two opposite situations. The first is the
 formation of echo chambers, where a user gets informed and
 gives opinions in a self-contained group, whose members
 share a similar point of view. By analyzing communities
 in Facebook which consume news from scientific pages and
 from pages focused on conspiracy theories we confirm the
 hypothesis of cognitive closure of the users, weakening the
 idea of Social Media as a space for democratic collective intelligence.
 The second is the presence of deviant communities.
 Those are communities that emerge around what are usually
 referred to as deviant behaviors (CM15), conducts that are commonly
 considered inappropriate because they violate society's
 norms or moral standards. An example of deviant behavior
 is the pornography consumption, that is the focus of our examination looking at content dissemination in Online Social
 Networks. Deviant communities are commonly considered
 segregated but we show that instead their content might
 spread far away in the Online Social Network. We analyze
 both situations with real case studies using Facebook, Flickr,
 and Tumblr data.
 Our work is an initial study of opinion polarization on Online
 Social Networks with some in-depth analyses of specific topical
 user communities. It brings novel contributions in: i)
 characterizing communities through the perspective of user
 polarization; ii) proposing a novel method to classify polarized
 users and topic evolution over time; iii) understanding user
 behavior from a social media perspective; iv) integrating polarization
 with other variables (time, space) with the purpose of
 analyzing a social phenomenon; v) defining controversy and
 how to detect it regardless of the content; vi) describing how
 people aggregate and share information in various contexts.
 Different topical communities and several OSNs are described
 in the dissertation, providing a general overview of the investigation
 field and proposing contributions to the discussion
 and solutions. Our research questions are part of a broader
 research area which is called Computational Social Science.
 This new discipline - which is the frame of our thesis - is a
 new approach to social studies by mean of novel large-scale
 computational tools, merging Social Science with Computer
 Science and Machine Learning.