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      Is Open Access

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

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          Significance

          Social media can deeply influence reality perception, affecting millions of people’s voting behavior. Hence, maneuvering opinion dynamics by disseminating forged content over online ecosystems is an effective pathway for social hacking. We propose a framework for discovering such a potentially dangerous behavior promoted by automatic users, also called “bots,” in online social networks. We provide evidence that social bots target mainly human influencers but generate semantic content depending on the polarized stance of their targets. During the 2017 Catalan referendum, used as a case study, social bots generated and promoted violent content aimed at Independentists, ultimately exacerbating social conflict online. Our results open challenges for detecting and controlling the influence of such content on society.

          Abstract

          Societies are complex systems, which tend to polarize into subgroups of individuals with dramatically opposite perspectives. This phenomenon is reflected—and often amplified—in online social networks, where, however, humans are no longer the only players and coexist alongside with social bots—that is, 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.

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          Most cited references39

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          The spread of behavior in an online social network experiment.

          How do social networks affect the spread of behavior? A popular hypothesis states that networks with many clustered ties and a high degree of separation will be less effective for behavioral diffusion than networks in which locally redundant ties are rewired to provide shortcuts across the social space. A competing hypothesis argues that when behaviors require social reinforcement, a network with more clustering may be more advantageous, even if the network as a whole has a larger diameter. I investigated the effects of network structure on diffusion by studying the spread of health behavior through artificially structured online communities. Individual adoption was much more likely when participants received social reinforcement from multiple neighbors in the social network. The behavior spread farther and faster across clustered-lattice networks than across corresponding random networks.
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            Experimental evidence of massive-scale emotional contagion through social networks.

            Emotional states can be transferred to others via emotional contagion, leading people to experience the same emotions without their awareness. Emotional contagion is well established in laboratory experiments, with people transferring positive and negative emotions to others. Data from a large real-world social network, collected over a 20-y period suggests that longer-lasting moods (e.g., depression, happiness) can be transferred through networks [Fowler JH, Christakis NA (2008) BMJ 337:a2338], although the results are controversial. In an experiment with people who use Facebook, we test whether emotional contagion occurs outside of in-person interaction between individuals by reducing the amount of emotional content in the News Feed. When positive expressions were reduced, people produced fewer positive posts and more negative posts; when negative expressions were reduced, the opposite pattern occurred. These results indicate that emotions expressed by others on Facebook influence our own emotions, constituting experimental evidence for massive-scale contagion via social networks. This work also suggests that, in contrast to prevailing assumptions, in-person interaction and nonverbal cues are not strictly necessary for emotional contagion, and that the observation of others' positive experiences constitutes a positive experience for people.
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              A 61-million-person experiment in social influence and political mobilization.

              Human behaviour is thought to spread through face-to-face social networks, but it is difficult to identify social influence effects in observational studies, and it is unknown whether online social networks operate in the same way. Here we report results from a randomized controlled trial of political mobilization messages delivered to 61 million Facebook users during the 2010 US congressional elections. The results show that the messages directly influenced political self-expression, information seeking and real-world voting behaviour of millions of people. Furthermore, the messages not only influenced the users who received them but also the users' friends, and friends of friends. The effect of social transmission on real-world voting was greater than the direct effect of the messages themselves, and nearly all the transmission occurred between 'close friends' who were more likely to have a face-to-face relationship. These results suggest that strong ties are instrumental for spreading both online and real-world behaviour in human social networks.
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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc. Natl. Acad. Sci. U.S.A
                pnas
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                4 December 2018
                20 November 2018
                20 November 2018
                : 115
                : 49
                : 12435-12440
                Affiliations
                [1] aCenter for Information and Communication Technology, Fondazione Bruno Kessler , 38123 Trento, Italy;
                [2] bUSC Information Sciences Institute, University of Southern California , Marina del Rey, CA 90292
                Author notes
                1To whom correspondence may be addressed. Email: emiliofe@ 123456usc.edu or mdedomenico@ 123456fbk.eu .

                Edited by Jon Kleinberg, Cornell University, Ithaca, NY, and approved October 19, 2018 (received for review February 27, 2018)

                Author contributions: M.S., E.F., and M.D.D. designed research; M.S., E.F., and M.D.D. performed research; M.S., E.F., and M.D.D. contributed new reagents/analytic tools; M.S. and M.D.D. analyzed data; and M.S., E.F., and M.D.D. wrote the paper.

                Author information
                http://orcid.org/0000-0002-1942-2831
                http://orcid.org/0000-0001-5158-8594
                Article
                201803470
                10.1073/pnas.1803470115
                6298098
                30459270
                4552935c-aa4f-48d0-bc69-d211c42f1a4e
                Copyright © 2018 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

                History
                Page count
                Pages: 6
                Funding
                Funded by: DOD | USAF | AFMC | Air Force Office of Scientific Research (AFOSR) 100000181
                Award ID: FA9550-17-1-0327
                Award Recipient : Emilio Ferrara
                Categories
                Social Sciences
                Social Sciences
                Physical Sciences
                Computer Sciences

                computational social science,complex networks,machine learning,sociotechnical systems,human behavior

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