This is Part 5 of a 12-part series based on the research paper “Human-Machine Social Systems.” Use the table of links below to navigate to the next part. Table of Links Abstract and Introduction Human-machine interactions Collective outcomes Box 1: Competition in high-frequency trading markets Box 2: Contagion on Twitter Box 3: Cooperation and coordination on Wikipedia Box 4: Cooperation and contagion on Reddit Discussion Implications for research Implications for design Implications for policy Conclusion, Acknowledgments, References, and Competing interests Box 2: Contagion on Twitter Social bots on the micro-blogging platform Twitter (re-branded as X in 2022) are covert automated accounts designed to impersonate humans to boost followers, disseminate information, and promote products. Bots and bot detection methods have co-evolved, resulting in increasingly more sophisticated imitation or detection strategies [177,178,179,180,39,181,182], but detection is inherently limited due to the overlap between covert autonomous bots, managed user accounts, hacked accounts, cyborgs, sock-puppets, and coordinated botnets [24,183,22,184]. Estimates suggest that 9-15% of Twitter users are bots185,182, with bot activity typically increasing around controversial political events [186]. Twitter social bots, who do not follow social instincts but neither succumb to fatigue, engage less in social interactions via replies and mentions than humans but produce more content [187]. The bots mainly retweet – a passive strategy to indicate support and gain followers – but are less successful in attracting friends and followers than humans [182]. Overall, they are less connected and bot-bot (2%), bot-human (19%), and human-bot (3%) interactions are considerably less common than human-human interactions (76%) 1[86]. Despite their rudimentary social behavior and weak network integration, Twitter bots significantly influence political communication, public opinion, elections, and markets. They play an important role in misinformation dissemination in relation to political events [188,189,190,191,192,193,194], COVID-19[195,196], and stock market investment [197]. Bots can affect human interaction networks by encouraging followings and conversations [198] and amplify low-credibility content early on by targeting influential humans through replies and mentions [192]. Bots’ large numbers enhance their visibility and influence to trigger deep information cascades [199]. Bots equally link to true and false news from low-credibility sites, but people prefer false content, making humans ultimately responsible for the spread of false news [200]. Twitter bots significantly contribute to negative sentiment and conflict escalation. Acting from the periphery, they target central human users to exert indirect influence. They amplify existing negative sentiment and selectively promote inflammatory content, often targeting only one of the factions[186]. Their success stems from exploiting human tendencies to connect with similar others and engage with messages that reinforce their beliefs [201]. Consequently, bots increase ideological polarization and negatively affect democratic discourse on social media, as seen in the 2016 US presidential election[202], the 2016 UK Brexit Referendum[201], and the 2017 Catalan independence referendum[186]. In sum, Twitter’s covert social bots are considered harmful, prompting the platform to cull them[203,204]. Their strength lies in indirect action: they skew the platform’s recommendation system to bias content popularity [137] and exploit human behavioral weaknesses like attention seeking, confirmation bias, moral outrage, and ideological homophily. Authors:
(1) Milena Tsvetkova, Department of Methodology, London School of Economics and Political Science, London, United Kingdom;
(2) Taha Yasseri, School of Sociology, University College Dublin, Dublin, Ireland and Geary Institute for Public Policy, University College Dublin, Dublin, Ireland;
(3) Niccolo Pescetelli, Collective Intelligence Lab, New Jersey Institute of Technology, Newark, New Jersey, USA;
(4) Tobias Werner, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany. This paper is available on arxiv under CC BY 4.0 DEED license. This is Part 5 of a 12-part series based on the research paper “Human-Machine Social Systems.” Use the table of links below to navigate to the next part. This is Part 5 of a 12-part series based on the research paper “Human-Machine Social Systems.” Use the table of links below to navigate to the next part. “ Human-Machine Social Systems .” Human-Machine Social Systems Table of Links Abstract and Introduction Abstract and Introduction Human-machine interactions Human-machine interactions Collective outcomes Collective outcomes Box 1: Competition in high-frequency trading markets Box 1: Competition in high-frequency trading markets Box 2: Contagion on Twitter Box 2: Contagion on Twitter Box 3: Cooperation and coordination on Wikipedia Box 3: Cooperation and coordination on Wikipedia Box 4: Cooperation and contagion on Reddit Box 4: Cooperation and contagion on Reddit Discussion Discussion Implications for research Implications for research Implications for design Implications for design Implications for policy Implications for policy Conclusion, Acknowledgments, References, and Competing interests Conclusion, Acknowledgments, References, and Competing interests Box 2: Contagion on Twitter Social bots on the micro-blogging platform Twitter (re-branded as X in 2022) are covert automated accounts designed to impersonate humans to boost followers, disseminate information, and promote products. Bots and bot detection methods have co-evolved, resulting in increasingly more sophisticated imitation or detection strategies [177,178,179,180,39,181,182], but detection is inherently limited due to the overlap between covert autonomous bots, managed user accounts, hacked accounts, cyborgs, sock-puppets, and coordinated botnets [24,183,22,184]. Estimates suggest that 9-15% of Twitter users are bots185,182, with bot activity typically increasing around controversial political events [186]. Twitter social bots, who do not follow social instincts but neither succumb to fatigue, engage less in social interactions via replies and mentions than humans but produce more content [187]. The bots mainly retweet – a passive strategy to indicate support and gain followers – but are less successful in attracting friends and followers than humans [182]. Overall, they are less connected and bot-bot (2%), bot-human (19%), and human-bot (3%) interactions are considerably less common than human-human interactions (76%) 1[86]. Despite their rudimentary social behavior and weak network integration, Twitter bots significantly influence political communication, public opinion, elections, and markets. They play an important role in misinformation dissemination in relation to political events [188,189,190,191,192,193,194], COVID-19[195,196], and stock market investment [197]. Bots can affect human interaction networks by encouraging followings and conversations [198] and amplify low-credibility content early on by targeting influential humans through replies and mentions [192]. Bots’ large numbers enhance their visibility and influence to trigger deep information cascades [199]. Bots equally link to true and false news from low-credibility sites, but people prefer false content, making humans ultimately responsible for the spread of false news [200]. Twitter bots significantly contribute to negative sentiment and conflict escalation. Acting from the periphery, they target central human users to exert indirect influence. They amplify existing negative sentiment and selectively promote inflammatory content, often targeting only one of the factions[186]. Their success stems from exploiting human tendencies to connect with similar others and engage with messages that reinforce their beliefs [201]. Consequently, bots increase ideological polarization and negatively affect democratic discourse on social media, as seen in the 2016 US presidential election[202], the 2016 UK Brexit Referendum[201], and the 2017 Catalan independence referendum[186]. In sum, Twitter’s covert social bots are considered harmful, prompting the platform to cull them[203,204]. Their strength lies in indirect action: they skew the platform’s recommendation system to bias content popularity [137] and exploit human behavioral weaknesses like attention seeking, confirmation bias, moral outrage, and ideological homophily. Authors: (1) Milena Tsvetkova, Department of Methodology, London School of Economics and Political Science, London, United Kingdom; (2) Taha Yasseri, School of Sociology, University College Dublin, Dublin, Ireland and Geary Institute for Public Policy, University College Dublin, Dublin, Ireland; (3) Niccolo Pescetelli, Collective Intelligence Lab, New Jersey Institute of Technology, Newark, New Jersey, USA; (4) Tobias Werner, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany. Authors: Authors: (1) Milena Tsvetkova, Department of Methodology, London School of Economics and Political Science, London, United Kingdom; (2) Taha Yasseri, School of Sociology, University College Dublin, Dublin, Ireland and Geary Institute for Public Policy, University College Dublin, Dublin, Ireland; (3) Niccolo Pescetelli, Collective Intelligence Lab, New Jersey Institute of Technology, Newark, New Jersey, USA; (4) Tobias Werner, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

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