Multifaceted online coordinated behavior in the 2020 US presidential election
Abstract Organized attempts to manipulate public opinion during election run-ups have dominated online debates in the last few years. Such attempts require numerous accounts to act in coordination to exert influence. Yet, the ways in which coordinated behavior surfaces during major online political...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-04-01
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| Series: | EPJ Data Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1140/epjds/s13688-024-00467-0 |
| _version_ | 1797199638230466560 |
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| author | Serena Tardelli Leonardo Nizzoli Marco Avvenuti Stefano Cresci Maurizio Tesconi |
| author_facet | Serena Tardelli Leonardo Nizzoli Marco Avvenuti Stefano Cresci Maurizio Tesconi |
| author_sort | Serena Tardelli |
| collection | DOAJ |
| description | Abstract Organized attempts to manipulate public opinion during election run-ups have dominated online debates in the last few years. Such attempts require numerous accounts to act in coordination to exert influence. Yet, the ways in which coordinated behavior surfaces during major online political debates is still largely unclear. This study sheds light on coordinated behaviors that took place on Twitter (now X) during the 2020 US Presidential Election. Utilizing state-of-the-art network science methods, we detect and characterize the coordinated communities that participated in the debate. Our approach goes beyond previous analyses by proposing a multifaceted characterization of the coordinated communities that allows obtaining nuanced results. In particular, we uncover three main categories of coordinated users: (i) moderate groups genuinely interested in the electoral debate, (ii) conspiratorial groups that spread false information and divisive narratives, and (iii) foreign influence networks that either sought to tamper with the debate or that exploited it to publicize their own agendas. We also reveal a large use of automation by far-right foreign influence and conspiratorial communities. Conversely, left-leaning supporters were overall less coordinated and engaged primarily in harmless, factual communication. Our results also showed that Twitter was effective at thwarting the activity of some coordinated groups, while it failed on some other equally suspicious ones. Overall, this study advances the understanding of online human interactions and contributes new knowledge to mitigate cyber social threats. |
| first_indexed | 2024-04-24T07:18:56Z |
| format | Article |
| id | doaj.art-dbfb948bf8d644d3b294c27dd7a2c667 |
| institution | Directory Open Access Journal |
| issn | 2193-1127 |
| language | English |
| last_indexed | 2024-04-24T07:18:56Z |
| publishDate | 2024-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EPJ Data Science |
| spelling | doaj.art-dbfb948bf8d644d3b294c27dd7a2c6672024-04-21T11:12:06ZengSpringerOpenEPJ Data Science2193-11272024-04-0113112710.1140/epjds/s13688-024-00467-0Multifaceted online coordinated behavior in the 2020 US presidential electionSerena Tardelli0Leonardo Nizzoli1Marco Avvenuti2Stefano Cresci3Maurizio Tesconi4Institute of Informatics and Telematics, National Research CouncilInstitute of Informatics and Telematics, National Research CouncilDepartment of Information Engineering, University of PisaInstitute of Informatics and Telematics, National Research CouncilInstitute of Informatics and Telematics, National Research CouncilAbstract Organized attempts to manipulate public opinion during election run-ups have dominated online debates in the last few years. Such attempts require numerous accounts to act in coordination to exert influence. Yet, the ways in which coordinated behavior surfaces during major online political debates is still largely unclear. This study sheds light on coordinated behaviors that took place on Twitter (now X) during the 2020 US Presidential Election. Utilizing state-of-the-art network science methods, we detect and characterize the coordinated communities that participated in the debate. Our approach goes beyond previous analyses by proposing a multifaceted characterization of the coordinated communities that allows obtaining nuanced results. In particular, we uncover three main categories of coordinated users: (i) moderate groups genuinely interested in the electoral debate, (ii) conspiratorial groups that spread false information and divisive narratives, and (iii) foreign influence networks that either sought to tamper with the debate or that exploited it to publicize their own agendas. We also reveal a large use of automation by far-right foreign influence and conspiratorial communities. Conversely, left-leaning supporters were overall less coordinated and engaged primarily in harmless, factual communication. Our results also showed that Twitter was effective at thwarting the activity of some coordinated groups, while it failed on some other equally suspicious ones. Overall, this study advances the understanding of online human interactions and contributes new knowledge to mitigate cyber social threats.https://doi.org/10.1140/epjds/s13688-024-00467-0Coordinated behaviorDisinformationSocial network |
| spellingShingle | Serena Tardelli Leonardo Nizzoli Marco Avvenuti Stefano Cresci Maurizio Tesconi Multifaceted online coordinated behavior in the 2020 US presidential election EPJ Data Science Coordinated behavior Disinformation Social network |
| title | Multifaceted online coordinated behavior in the 2020 US presidential election |
| title_full | Multifaceted online coordinated behavior in the 2020 US presidential election |
| title_fullStr | Multifaceted online coordinated behavior in the 2020 US presidential election |
| title_full_unstemmed | Multifaceted online coordinated behavior in the 2020 US presidential election |
| title_short | Multifaceted online coordinated behavior in the 2020 US presidential election |
| title_sort | multifaceted online coordinated behavior in the 2020 us presidential election |
| topic | Coordinated behavior Disinformation Social network |
| url | https://doi.org/10.1140/epjds/s13688-024-00467-0 |
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