Bots increase exposure to negative and inflammatory content in online social systems

Societies are complex systems which tend to polarize into sub-groups of individuals with dramatically opposite perspectives. This phenomenon is reflected -- and often amplified -- in online social networks where, however, humans are no more the only players, and co-exist alongside with social bots, i.e., software-controlled accounts. Analyzing large-scale social data collected during the Catalan referendum for independence on October 1, 2017, consisting of nearly 4 millions Twitter posts generated by almost 1 million users, we identify the two polarized groups of Independentists and Constitutionalists and quantify the structural and emotional roles played by social bots. We show that bots act from peripheral areas of the social system to target influential humans of both groups, bombarding Independentists with violent contents, increasing their exposure to negative and inflammatory narratives and exacerbating social conflict online. Our findings stress the importance of developing countermeasures to unmask these forms of automated social manipulation.Comment: 8 pages, 5 figure

Social Media and Information Overload: Survey Results

A UK-based online questionnaire investigating aspects of usage of user-generated media (UGM), such as Facebook, LinkedIn and Twitter, attracted 587 participants. Results show a high degree of engagement with social networking media such as Facebook, and a significant engagement with other media such as professional media, microblogs and blogs. Participants who experience information overload are those who engage less frequently with the media, rather than those who have fewer posts to read. Professional users show different behaviours to social users. Microbloggers complain of information overload to the greatest extent. Two thirds of Twitter-users have felt that they receive too many posts, and over half of Twitter-users have felt the need for a tool to filter out the irrelevant posts. Generally speaking, participants express satisfaction with the media, though a significant minority express a range of concerns including information overload and privacy

Information is not a Virus, and Other Consequences of Human Cognitive Limits

The many decisions people make about what to pay attention to online shape the spread of information in online social networks. Due to the constraints of available time and cognitive resources, the ease of discovery strongly impacts how people allocate their attention to social media content. As a consequence, the position of information in an individual's social feed, as well as explicit social signals about its popularity, determine whether it will be seen, and the likelihood that it will be shared with followers. Accounting for these cognitive limits simplifies mechanics of information diffusion in online social networks and explains puzzling empirical observations: (i) information generally fails to spread in social media and (ii) highly connected people are less likely to re-share information. Studies of information diffusion on different social media platforms reviewed here suggest that the interplay between human cognitive limits and network structure differentiates the spread of information from other social contagions, such as the spread of a virus through a population.Comment: accepted for publication in Future Interne

Twittering the Boko Haram Uprising in Nigeria: Investigating Pragmatic Acts in the Social Media

This paper investigates pragmatic acts in the discourse of tweeters and online feedback comments on the activities of Boko Haram, a terrorist group in Nigeria. The tweets and comments illustrate acts used to express revolutionary feelings and reflect what people say and imply in times of crisis. Tweets about Boko Haram are speech and pragmatic acts that denounce the Nigerian government, reject Western education, and call for support. Tweets and reactions from non-Muslims and nonradical Muslims condemn terrorism and denounce the terrorist group. While some tweets simply offer suggestions on how to curtail the Boko Haram insurgency, others seek the breakup of Nigeria, granting political and religious independence to the north and the southeast of the country

Analysis of Retweeting Behavior Using Topic Models

Igapäevase eluga põimunud virtuaalsed sotsiaalvõrgustikud omavad üha kasvavat rolli sotsiaalsetes ja ärilistes nähtustes. Microblogging teenused nagu Twitter mängivad olulist rolli Interneti infovahetuses, muutes võimalikuks sõnumite leviku minutitega. Käesolevas uurimuses analüüsitakse korduvalt edastatavate sõnumite (retweet) levikut Twitteris. Kasutades Latent Dirichlet Allocation mudelit teemade eristamiseks näitame, et kasutajate ja sõnumites sisalduvate teemade vaheline suhteline kaugus on lühem korduvalt edastatavatel sõnumitel. Kasutades otsustuspuid hindame teemapõhise retweet mudeli täpsust ja kasulikkust. Töö tulemusena näitame, et teemapõhine mudel on tugevama ennustusvõimega võrreldes baseline mudelitega, millest lähtuvalt väidame, et antud lähenemine on sobiv korduvalt edastavate sõnumite ennustamiseks ning edasiseks arenduseks.Social networks are nowadays a constant presence in our lives and increasingly have a role in important social and commercial phenomena. Microblogging services such as Twitter appear to play an important role in the process of information dissemination on the Internet making it possible for messages to spread virally in a matter of minutes. In this research work we study the mechanism of re-broadcasting (called “retweeting”) information on Twitter; specifically we use Latent Dirichlet Allocation to analyze users and messages in terms of the topics that compose their text bodies and by means of ANOVA we are able to show that the topical distance between users and messages is shorter for tweets that are retweeted than for those that are not. Using Decision Tree learning we build several models in order to assess the accuracy and usefulness of our topic-based model of retweeting. Our results show that our topic-based model slightly outperforms a baseline prediction measure, so we conclude that such model is indeed a valid option to consider for predicting retweet behavior with possibilities open for improvement

The Role of Sentiment in Information Propagation on Twitter – An Empirical Analysis of Affective Dimensions in Political Tweets

Twitter is, among other social-media platforms, a service, which is said to have an impact on the public discourse and communication in the society. With the unique feature of “retweeting,” Twitter is an ideal platform for users to spread information. Besides their content and intended use, Twitter messages (“tweets”) often convey pertinent information about their author’s sentiment. In this paper, we examine whether sentiment occurring in politically relevant tweets has an effect on their retweetability (i.e., how often these tweets will be retweeted). Based on a data set of approximately 65,000 tweets, we find a positive relationship between the quantity of words indicating affective dimensions including positive and negative emotions associated with certain political parties or politicians in tweets and their retweet rate. We conclude by discussing the implications of our results

Quantifying the Effect of Sentiment on Information Diffusion in Social Media

Social media have become the main vehicle of information production and consumption online. Millions of users every day log on their Facebook or Twitter accounts to get updates and news, read about their topics of interest, and become exposed to new opportunities and interactions. Although recent studies suggest that the contents users produce will affect the emotions of their readers, we still lack a rigorous understanding of the role and effects of contents sentiment on the dynamics of information diffusion. This work aims at quantifying the effect of sentiment on information diffusion, to understand: (i) whether positive conversations spread faster and/or broader than negative ones (or vice-versa); (ii) what kind of emotions are more typical of popular conversations on social media; and, (iii) what type of sentiment is expressed in conversations characterized by different temporal dynamics. Our findings show that, at the level of contents, negative messages spread faster than positive ones, but positive ones reach larger audiences, suggesting that people are more inclined to share and favorite positive contents, the so-called positive bias. As for the entire conversations, we highlight how different temporal dynamics exhibit different sentiment patterns: for example, positive sentiment builds up for highly-anticipated events, while unexpected events are mainly characterized by negative sentiment. Our contribution is a milestone to understand how the emotions expressed in short texts affect their spreading in online social ecosystems, and may help to craft effective policies and strategies for content generation and diffusion.Comment: 10 pages, 5 figure