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      Is Open Access

      Bots are less central than verified accounts during contentious political events

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          Significance

          Online networks carry benefits and risks with high-stakes consequences during contentious political events: They can be tools for organization and awareness, or tools for disinformation and conflict. We combine social media and web-tracking data to measure differences on the visibility of news sources during two events that involved massive political mobilizations in two different countries and time periods. We contextualize the role of social media as an entry point to news, and we cast doubts on the impact that bot activity had on the coverage of those mobilizations. We show that verified, blue-badge accounts were significantly more visible and central. Our findings provide evidence to evaluate the role of social media in facilitating information campaigns and eroding traditional gatekeeping roles.

          Abstract

          Information manipulation is widespread in today’s media environment. Online networks have disrupted the gatekeeping role of traditional media by allowing various actors to influence the public agenda; they have also allowed automated accounts (or bots) to blend with human activity in the flow of information. Here, we assess the impact that bots had on the dissemination of content during two contentious political events that evolved in real time on social media. We focus on events of heightened political tension because they are particularly susceptible to information campaigns designed to mislead or exacerbate conflict. We compare the visibility of bots with human accounts, verified accounts, and mainstream news outlets. Our analyses combine millions of posts from a popular microblogging platform with web-tracking data collected from two different countries and timeframes. We employ tools from network science, natural language processing, and machine learning to analyze the diffusion structure, the content of the messages diffused, and the actors behind those messages as the political events unfolded. We show that verified accounts are significantly more visible than unverified bots in the coverage of the events but also that bots attract more attention than human accounts. Our findings highlight that social media and the web are very different news ecosystems in terms of prevalent news sources and that both humans and bots contribute to generate discrepancy in news visibility with their activity.

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          Most cited references33

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          The spread of true and false news online

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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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              Fake news on Twitter during the 2016 U.S. presidential election

              The spread of fake news on social media became a public concern in the United States after the 2016 presidential election. We examined exposure to and sharing of fake news by registered voters on Twitter and found that engagement with fake news sources was extremely concentrated. Only 1% of individuals accounted for 80% of fake news source exposures, and 0.1% accounted for nearly 80% of fake news sources shared. Individuals most likely to engage with fake news sources were conservative leaning, older, and highly engaged with political news. A cluster of fake news sources shared overlapping audiences on the extreme right, but for people across the political spectrum, most political news exposure still came from mainstream media outlets.
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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
                16 March 2021
                08 March 2021
                08 March 2021
                : 118
                : 11
                : e2013443118
                Affiliations
                [1] aAnnenberg School for Communication, University of Pennsylvania , Philadelphia, PA 19104;
                [2] bCenter for Information and Communication Technology, Fondazione Bruno Kessler , 38123 Trento, Italy
                Author notes
                1To whom correspondence may be addressed. Email: sgonzalezbailon@ 123456asc.upenn.edu or mdedomenico@ 123456fbk.eu .

                Edited by Nicole Ellison, University of Michigan, Ann Arbor, MI, and accepted by Editorial Board Member Margaret Levi January 30, 2021 (received for review June 27, 2020)

                Author contributions: S.G.B. and M.D.D. designed research, performed research, contributed analytic tools, analyzed data, and wrote the paper.

                Author information
                https://orcid.org/0000-0002-8372-798X
                https://orcid.org/0000-0001-5158-8594
                Article
                202013443
                10.1073/pnas.2013443118
                7980437
                33836572
                e5ae3e79-8321-4787-9f34-e1eb8ccde284
                Copyright © 2021 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: 8
                Funding
                Funded by: National Science Foundation (NSF) 100000001
                Award ID: 1729412; 2017655
                Award Recipient : Sandra Gonzalez-Bailon
                Categories
                411
                432
                Social Sciences
                Social Sciences
                Physical Sciences
                Computer Sciences

                social media,computational social science,online networks,information diffusion,political mobilization

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