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Register a new userThe Anatomy of Brexit Debate on Facebook
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Added by
Walter Quattrociocchi
Publication date
2016
fields
Informatics Engineering
and research's language is
English
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Nowadays users get informed and shape their opinion through social media. However, the disintermediated access to contents does not guarantee quality of information. Selective exposure and confirmation bias, indeed, have been shown to play a pivotal role in content consumption and information spreading. Users tend to select information adhering (and reinforcing) their worldview and to ignore dissenting information. This pattern elicits the formation of polarized groups -- i.e., echo chambers -- where the interaction with like-minded people might even reinforce polarization. In this work we address news consumption around Brexit in UK on Facebook. In particular, we perform a massive analysis on more than 1 Million users interacting with Brexit related posts from the main news providers between January and July 2016. We show that consumption patterns elicit the emergence of two distinct communities of news outlets. Furthermore, to better characterize inner group dynamics, we introduce a new technique which combines automatic topic extraction and sentiment analysis. We compare how the same topics are presented on posts and the related emotional response on comments finding significant differences in both echo chambers and that polarization influences the perception of topics. Our results provide important insights about the determinants of polarization and evolution of core narratives on online debating.
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Vaccine hesitancy has been recognized as a major global health threat. Having access to any type of information in social media has been suggested as a potential powerful influence factor to hesitancy. Recent studies in other fields than vaccination show that access to a wide amount of content through the Internet without intermediaries resolved into major segregation of the users in polarized groups. Users select the information adhering to theirs system of beliefs and tend to ignore dissenting information. In this paper we assess whether there is polarization in Social Media use in the field of vaccination. We perform a thorough quantitative analysis on Facebook analyzing 2.6M users interacting with 298.018 posts over a time span of seven years and 5 months. We used community detection algorithms to automatically detect the emergent communities from the users activity and to quantify the cohesiveness over time of the communities. Our findings show that content consumption about vaccines is dominated by the echo-chamber effect and that polarization increased over years. Communities emerge from the users consumption habits, i.e. the majority of users only consumes information in favor or against vaccines, not both. The existence of echo-chambers may explain why social-media campaigns providing accurate information may have limited reach, may be effective only in sub-groups and might even foment further polarization of opinions. The introduction of dissenting information into a sub-group is disregarded and can have a backfire effect, further reinforcing the existing opinions within the sub-group.
On social media algorithms for content promotion, accounting for users preferences, might limit the exposure to unsolicited contents. In this work, we study how the same contents (videos) are consumed on different platforms -- i.e. Facebook and YouTube -- over a sample of $12M$ of users. Our findings show that the same content lead to the formation of echo chambers, irrespective of the online social network and thus of the algorithm for content promotion. Finally, we show that the users commenting patterns are accurate early predictors for the formation of echo-chambers.
Online Social Networks represent a novel opportunity for political campaigns, revolutionising the paradigm of political communication. Nevertheless, many studies uncovered the presence of d/misinformation campaigns or of malicious activities by genuine or automated users, putting at severe risk the credibility of online platforms. This phenomenon is particularly evident during crucial political events, as political elections. In the present paper, we provide a comprehensive description of the structure of the networks of interactions among users and bots during the UK elections of 2019. In particular, we focus on the polarised discussion about Brexit on Twitter analysing a data set made of more than 10 million tweets posted for over a month. We found that the presence of automated accounts fostered the debate particularly in the days before the UK national elections, in which we find a steep increase of bots in the discussion; in the days after the election day, their incidence returned to values similar to the ones observed few weeks before the elections. On the other hand, we found that the number of suspended users (i.e. accounts that were removed by the platform for some violation of the Twitter policy) remained constant until the election day, after which it reached significantly higher values. Remarkably, after the TV debate between Boris Johnson and Jeremy Corbyn, we observed the injection of a large number of novel bots whose behaviour is markedly different from that of pre-existing ones. Finally, we explored the bots stance, finding that their activity is spread across the whole political spectrum, although in different proportions, and we studied the different usage of hashtags by automated accounts and suspended users, thus targeting the formation of common narratives in different sides of the debate.
Recent studies, targeting Facebook, showed the tendency of users to interact with information adhering to their preferred narrative and to ignore dissenting information. Primarily driven by confirmation bias, users tend to join polarized clusters where they cooperate to reinforce a like-minded system of beliefs, thus facilitating fake news and misinformation cascades. To gain a deeper understanding of these phenomena, in this work we analyze the lexicons used by the communities of users emerging on Facebook around verified and unverified contents. We show how the lexical approach provides important insights about the kind of information processed by the two communities of users and about their overall sentiment. Furthermore, by focusing on comment threads, we observe a strong positive correlation between the lexical convergence of co-commenters and their number of interactions, which in turns suggests that such a trend could be a proxy for the emergence of collective identities and polarization in opinion dynamics.
The advent of WWW changed the way we can produce and access information. Recent studies showed that users tend to select information that is consistent with their system of beliefs, forming polarized groups of like-minded people around shared narratives where dissenting information is ignored. In this environment, users cooperate to frame and reinforce their shared narrative making any attempt at debunking inefficient. Such a configuration occurs even in the consumption of news online, and considering that 63% of users access news directly form social media, one hypothesis is that more polarization allows for further spreading of misinformation. Along this path, we focus on the polarization of users around news outlets on Facebook in different European countries (Italy, France, Spain and Germany). First, we compare the pages posting behavior and the users interacting patterns across countries and observe different posting, liking and commenting rates. Second, we explore the tendency of users to interact with different pages (i.e., selective exposure) and the emergence of polarized communities generated around specific pages. Then, we introduce a new metric -- i.e., polarization rank -- to measure polarization of communities for each country. We find that Italy is the most polarized country, followed by France, Germany and lastly Spain. Finally, we present a variation of the Bounded Confidence Model to simulate the emergence of these communities by considering the users engagement and trust on the news. Our findings suggest that trust in information broadcaster plays a pivotal role against polarization of users online.
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