Quali-quanti visual methods and political bots
Computational social science research on automated social media accounts, colloquially dubbed “bots”, has tended to rely on binary verification methods to detect bot operations on social media. Typically focused on textual data from Twitter (now rebranded as "X"), these inference-based me...
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| Format: | Article |
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DIGSUM
2024-03-01
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| Series: | Journal of Digital Social Research |
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| Online Access: | https://publicera.kb.se/jdsr/article/view/23185 |
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| author | Janna Joceli Omena Thais Lobo Giulia Tucci Elias Bitencourt Emillie de Keulenaar Francisco W. Kerche Jason Chao Marius Liedtke Mengying Li Maria Luiza Paschoal Ilya Lavrov |
| author_facet | Janna Joceli Omena Thais Lobo Giulia Tucci Elias Bitencourt Emillie de Keulenaar Francisco W. Kerche Jason Chao Marius Liedtke Mengying Li Maria Luiza Paschoal Ilya Lavrov |
| author_sort | Janna Joceli Omena |
| collection | DOAJ |
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Computational social science research on automated social media accounts, colloquially dubbed “bots”, has tended to rely on binary verification methods to detect bot operations on social media. Typically focused on textual data from Twitter (now rebranded as "X"), these inference-based methods are prone to finding false positives and failing to understand the subtler ways in which bots operate over time, through visual content and in particular contexts. This research brings methodological contributions to such studies, focusing on what it calls “bolsobots” in Brazilian social media. Named after former Brazilian President Jair Bolsonaro, the bolsobots refer to the extensive and skilful usage of partial or fully automated accounts by marketing teams, hackers, activists or campaign supporters. These accounts leverage online political culture to sway public opinion for or against public policies, opposition figures, or Bolsonaro himself. Drawing on empirical case studies, this paper implements quali-quanti visual methods to operationalise specific techniques for interpreting bot-associated image collections and textual content across Instagram, TikTok and Twitter/X. To unveil the modus operandi of bolsobots, we map the networks of users they follow (“following networks”), explore the visual-textual content they post, and observe the strategies they deploy to adapt to platform content moderation. Such analyses tackle methodological challenges inherent in bot studies by employing three key strategies: 1) designing context-sensitive queries and curating datasets with platforms’ interfaces and search engines to mitigate the limitations of bot scoring detectors, 2) engaging qualitatively with data visualisations to understand the vernaculars of bots, and 3) adopting a non-binary analysis framework that contextualises bots within their socio-technical environments. By acknowledging the intricate interplay between bots, user and platform cultures, this paper contributes to method innovation on bot studies and emerging quali-quanti visual methods literature.
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| format | Article |
| id | doaj-art-63bd96744f704a6da9b8abcb20e4180e |
| institution | OA Journals |
| issn | 2003-1998 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | DIGSUM |
| record_format | Article |
| series | Journal of Digital Social Research |
| spelling | doaj-art-63bd96744f704a6da9b8abcb20e4180e2025-08-20T02:18:43ZengDIGSUMJournal of Digital Social Research2003-19982024-03-016110.33621/jdsr.v6i1.215Quali-quanti visual methods and political botsJanna Joceli Omena0Thais Lobo1Giulia Tucci2Elias Bitencourt3Emillie de Keulenaar4Francisco W. Kerche5Jason Chao6Marius Liedtke7Mengying Li8Maria Luiza Paschoal9Ilya Lavrov10University of Warwick, United Kingdom; Universidade Nova de Lisboa, PortugalKing’s College London, United KingdomFederal University of Rio de Janeiro, Brazil.Universidade do Estado da Bahia, BrazilUniversity of Groningen, NetherlandsUniversity of São Paulo, BrazilUniversity of São Paulo, BrazilUniversity of Salzburg, AustriaFudan University, ChinaUniversity of Trento, ItalyUniversity of Groningen, Netherlands; University College Dublin, Ireland Computational social science research on automated social media accounts, colloquially dubbed “bots”, has tended to rely on binary verification methods to detect bot operations on social media. Typically focused on textual data from Twitter (now rebranded as "X"), these inference-based methods are prone to finding false positives and failing to understand the subtler ways in which bots operate over time, through visual content and in particular contexts. This research brings methodological contributions to such studies, focusing on what it calls “bolsobots” in Brazilian social media. Named after former Brazilian President Jair Bolsonaro, the bolsobots refer to the extensive and skilful usage of partial or fully automated accounts by marketing teams, hackers, activists or campaign supporters. These accounts leverage online political culture to sway public opinion for or against public policies, opposition figures, or Bolsonaro himself. Drawing on empirical case studies, this paper implements quali-quanti visual methods to operationalise specific techniques for interpreting bot-associated image collections and textual content across Instagram, TikTok and Twitter/X. To unveil the modus operandi of bolsobots, we map the networks of users they follow (“following networks”), explore the visual-textual content they post, and observe the strategies they deploy to adapt to platform content moderation. Such analyses tackle methodological challenges inherent in bot studies by employing three key strategies: 1) designing context-sensitive queries and curating datasets with platforms’ interfaces and search engines to mitigate the limitations of bot scoring detectors, 2) engaging qualitatively with data visualisations to understand the vernaculars of bots, and 3) adopting a non-binary analysis framework that contextualises bots within their socio-technical environments. By acknowledging the intricate interplay between bots, user and platform cultures, this paper contributes to method innovation on bot studies and emerging quali-quanti visual methods literature. https://publicera.kb.se/jdsr/article/view/23185Digital methodspolitical botscoordinated inauthentic behaviourcross-platformquali-quanti methodsvisual methodologies |
| spellingShingle | Janna Joceli Omena Thais Lobo Giulia Tucci Elias Bitencourt Emillie de Keulenaar Francisco W. Kerche Jason Chao Marius Liedtke Mengying Li Maria Luiza Paschoal Ilya Lavrov Quali-quanti visual methods and political bots Journal of Digital Social Research Digital methods political bots coordinated inauthentic behaviour cross-platform quali-quanti methods visual methodologies |
| title | Quali-quanti visual methods and political bots |
| title_full | Quali-quanti visual methods and political bots |
| title_fullStr | Quali-quanti visual methods and political bots |
| title_full_unstemmed | Quali-quanti visual methods and political bots |
| title_short | Quali-quanti visual methods and political bots |
| title_sort | quali quanti visual methods and political bots |
| topic | Digital methods political bots coordinated inauthentic behaviour cross-platform quali-quanti methods visual methodologies |
| url | https://publicera.kb.se/jdsr/article/view/23185 |
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