A Motif-based Approach for Identifying Controversy

Among the topics discussed in Social Media, some lead to controversy. A number of recent studies have focused on the problem of identifying controversy in social media mostly based on the analysis of textual content or rely on global network structure. Such approaches have strong limitations due to the difficulty of understanding natural language, and of investigating the global network structure. In this work we show that it is possible to detect controversy in social media by exploiting network motifs, i.e., local patterns of user interaction. The proposed approach allows for a language-independent and fine- grained and efficient-to-compute analysis of user discussions and their evolution over time. The supervised model exploiting motif patterns can achieve 85% accuracy, with an improvement of 7% compared to baseline structural, propagation-based and temporal network features

Misinformation in the loop: the emergence of narratives in Online Social Networks

The interlink between information and belief formation and revision is a fundamental aspect of social dynamics. The growth of knowledge fostered by a hyper-connected world together with the unprecedented acceleration of scientific progress has exposed individuals, governments and countries to an increasing level of complexity to explain reality and its phenomena. Despite the enthusiastic rhetoric about the so called collective intelligence, conspiracy theories and other unsubstantiated claims find on the Web a natural medium for their diffusion. Cases in which these kinds of false information are used in political debates are far from unimaginable. In this work, we study the behavior of users supporting different (and opposite) worldviews – i.e. scientific and conspiracist thinking – that commented the posts of the Facebook page of a large italian political party that advocates direct democracy and e-Participation. We find that users supporting different narratives consume political information in a similar way. Moreover, by analyzing the composition of users active on the page in terms of commenting activity, we notice that almost one fifth of them is represented by polarized consumers of conspiracy stories, and those are able to generate almost one third of total comments to the posts of the pag

Electoral Predictions with Twitter: A Machine-Learning approach

Several studies have shown how to approximately predict public opinion, such as in political elections, by analyzing user activities in blogging platforms and on-line social networks. The task is challenging for several reasons. Sample bias and automatic understanding of textual content are two of several non trivial issues. In this work we study how Twitter can provide some interesting insights concerning the primary elections of an Italian political party. State-of-the-art approaches rely on indicators based on tweet and user volumes, often including sentiment analysis. We investigate how to exploit and improve those indicators in order to reduce the bias of the Twitter users sample. We propose novel indicators and a novel content-based method. Furthermore, we study how a machine learning approach can learn correction factors for those indicators. Experimental results on Twitter data support the validity of the proposed methods and their improvement over the state of the art.Several studies have shown how to approximately predict public opinion, such as in political elections, by analyzing user activities in blogging platforms and on-line social networks. The task is challenging for several reasons. Sample bias and automatic understanding of textual content are two of several non trivial issues. In this work we study how Twitter can provide some interesting insights concerning the primary elections of an Italian political party. State-of-the-art approaches rely on indicators based on tweet and user volumes, often including sentiment analysis. We investigate how to exploit and improve those indicators in order to reduce the bias of the Twitter users sample. We propose novel indicators and a novel content-based method. Furthermore, we study how a machine learning approach can learn correction factors for those indicators. Experimental results on Twitter data support the validity of the proposed methods and their improvement over the state of the art

A Kernel of Truth: Determining Rumor Veracity on Twitter by Diffusion Pattern Alone

Recent work in the domain of misinformation detection has leveraged rich signals in the text and user identities associated with content on social media. But text can be strategically manipulated and accounts reopened under different aliases, suggesting that these approaches are inherently brittle. In this work, we investigate an alternative modality that is naturally robust: the pattern in which information propagates. Can the veracity of an unverified rumor spreading online be discerned solely on the basis of its pattern of diffusion through the social network? Using graph kernels to extract complex topological information from Twitter cascade structures, we train accurate predictive models that are blind to language, user identities, and time, demonstrating for the first time that such "sanitized" diffusion patterns are highly informative of veracity. Our results indicate that, with proper aggregation, the collective sharing pattern of the crowd may reveal powerful signals of rumor truth or falsehood, even in the early stages of propagation.Comment: Published at The Web Conference (WWW) 202

Analysis of Polarized Communities in Online Social Networks

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

Adult content consumption in online social networks

Users in online social networks naturally organize themselves into overlapping and interlinked communities that are formed around common identity or shared topical interests. Some communities gather people around specific deviant behaviors, conducts that are commonly considered inappropriate with respect to the society's norms or moral standards such as drug use, eating disorders, and pornographic content consumption. From a network analysis perspective, the set of interactions between members of these communities form deviant networks that map how the deviant content is shared and consumed. It is commonly believed that deviant networks are small and isolated from the mainstream social media life; accordingly, most research studies have considered them in isolation. We focus on adult content consumption networks, which is one deviant network with a significant presence in online social media and in the Web in general. We investigate two large online social networks and discuss the following insights. Deviant networks are limited in size, tightly connected and structured in subgroups. Nevertheless, content originated in deviant networks spreads widely across the whole social graph possibly touching a large number of unintentionally exposed users, such that the average local perception is that neighboring users share more deviant content. Finally, we investigate how content production and consumption vary with age and show that the consumption rate is very similar between male and female users up to the age of 25. We conclude that deviant communities are deeply rooted into the relational fabric of a social network, and that a deeper understanding of how their activity impacts on every other user is required

On the behaviour of deviant communities in online social networks

On-line social networks are complex ensembles of interlinked communities that interact on different topics. Some communities are characterized by what are usually referred to as deviant behaviors, conducts that are commonly considered inappropriate with respect to the society's norms or moral standards. Eating disorders, drug use, and adult content consumption are just a few examples. We refer to such communities as deviant networks. It is commonly believed that such deviant networks are niche, isolated social groups, whose activity is well separated from the mainstream socialmedia life. According to this assumption, research studies have mostly considered them in isolation. In this work we focused on adult content consumption networks, which are present in many on-line social media and in the Web in general. We found that few small and densely connected communities are responsible for most of the content production. Differently from previous work, we studied how such communities interact with the whole social network. We found that the produced content flows to the rest of the network mostly directly or through bridge-communities, reaching at least 450 times more users. We also show that a large fraction of the users can be inadvertently exposed to such content through indirect content resharing. We also discuss a demographic analysis of the producers and consumers networks. Finally, we show that it is easily possible to identify a few core users to radically uproot the diffusion process. We aim at setting the basis to study deviant communities in context

Sentiment-enhanced multidimensional analysis of online social networks: Perception of the mediterranean refugees crisis

We propose an analytical framework able to investigate discussions about polarized topics in online social networks from many different angles. The framework supports the analysis of social networks along several dimensions: time, space and sentiment. We show that the proposed analytical framework and the methodology can be used to mine knowledge about the perception of complex social phenomena. We selected the refugee crisis discussions over Twitter as a case study. This difficult and controversial topic is an increasingly important issue for the EU. The raw stream of tweets is enriched with space information (user and mentioned locations), and sentiment (positive vs. negative) w.r.t. refugees. Our study shows differences in positive and negative sentiment in EU countries, in particular in UK, and by matching events, locations and perception, it underlines opinion dynamics and common prejudices regarding the refugees

After the Death of Childhood (Korean edition)

<p>The number of pages, posts, likes, comments, likers, and commenters for conspiracy and science news.</p