Mauro Coletto

Science vs conspiracy: collective narratives in the age of misinformation.

The large availability of user provided contents on online social media facilitates people aggregation around shared beliefs, interests, worldviews and narratives. In spite of the enthusiastic rhetoric about the so called collective intelligence unsubstantiated rumors and conspiracy theories—e.g., chemtrails, reptilians or the Illuminati—are pervasive in online social networks (OSN). In this work we study, on a sample of 1.2 million of individuals, how information related to very distinct narratives—i.e. main stream scientific and conspiracy news—are consumed and shape communities on Facebook. Our results show that polarized communities emerge around distinct types of contents and usual consumers of conspiracy news result to be more focused and self-contained on their specific contents. To test potential biases induced by the continued exposure to unsubstantiated rumors on users’ content selection, we conclude our analysis measuring how users respond to 4,709 troll information—i.e. parodistic and sarcastic imitation of conspiracy theories. We find that 77.92% of likes and 80.86% of comments are from users usually interacting with conspiracy stories.

Polarized User and Topic Tracking in Twitter

Digital traces of conversations in micro-blogging platforms and OSNs provide information about user opinion with a high degree of resolution. These information sources can be exploited to understand and monitor collective behaviours. In this work, we focus on polarisation classes, i.e., those topics that require the user to side exclusively with one position. The proposed method provides an iterative classification of users and keywords: first, polarised users are identified, then polarised keywords are discovered by monitoring the activities of previously classified users. This method thus allows tracking users and topics over time. We report several experiments conducted on two Twitter datasets during political election time-frames. We measure the user classification accuracy on a golden set of users, and analyse the relevance of the extracted keywords for the ongoing political discussion.

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.

Perception of social phenomena through the multidimensional analysis of online social networks

Abstract We propose an analytical framework aimed at investigating different views of the discussions regarding polarized topics which occur in Online Social Networks (OSNs). The framework supports the analysis along multiple dimensions, i.e., time, space and sentiment of the opposite views about a controversial topic emerging in an OSN. To assess its usefulness in mining insights about social phenomena, we apply it to two different Twitter case studies: the discussions about the refugee crisis and the United Kingdom European Union membership referendum. These complex and contended topics are very important issues for EU citizens and stimulated a multitude of Twitter users to take side and actively participate in the discussions. Our framework allows to monitor in a scalable way the raw stream of relevant tweets and to automatically enrich them with location information (user and mentioned locations), and sentiment polarity (positive vs. negative). The analyses we conducted show how the framework captures the differences in positive and negative user sentiment over time and space. The resulting knowledge can support the understanding of complex dynamics by identifying variations in the perception of specific events and locations.

Do Violent People Smile: Social Media Analysis of their Profile Pictures

The popularity of online social platforms has also determined the emergence of violent and abusive behaviors reflecting real life issues into the digital arena. Cyberbullying, Internet banging, pedopornography, sexting are examples of these behaviors, as witnessed in the social media environments. Several studies have shown how to approximately detect those behaviors by analyzing the social interactions and in particular the content of the exchanged messages. The features considered in the models basically include detection of o ensive language through NLP techniques and vocabularies, social network structural measures and, if available, user context information. Our goal is to investigate those users who adopt offensive language and hate speech in Twitter by analyzing their profile pictures. Results show that violent people smile less and they are dominating by anger, fear and sadness.

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.

Automatic controversy detection in social media: A content-independent motif-based approach

Abstract Online social networks are becoming the primary medium by which people get informed, as they provide a forum for expressing ideas, contributing to public debates, and participating in opinion-formation processes. Among the topics discussed in Social Media, some lead to controversy. Identifying controversial topics is useful for exploring the space of public discourse and understanding the issues of current interest. Thus, 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, especially in short texts, 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 analysis of user discussions and their evolution over time. Network motifs can be easily extracted both from user interactions and from the underlying social network, and they are conceptually simple to define and very efficient to compute. We assess the predictive power of motifs on a manually labeled twitter dataset. In fact, a supervised model exploiting motif patterns can achieve 85% accuracy, with an improvement of 7% compared to baseline structural, propagation-based and temporal network features. Finally, thanks to the locality of motif patterns, we show that it is possible to monitor the evolution of controversy in a conversation over time thus discovering changes in user opinion.