Blended Bots: Infiltration through Identity Deception on Social Media
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916280071618560 |
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| author | Phillips, Samantha C. Ng, Lynnette Hui Xian Carley, Kathleen M. |
| author_facet | Phillips, Samantha C. Ng, Lynnette Hui Xian Carley, Kathleen M. |
| contents | Bots are automated social media users that can be used to amplify (mis)information and sow harmful discourse. In order to effectively influence users, bots can be generated to reproduce human user behavior. Indeed, people tend to trust information coming from users with profiles that fit roles they expect to exist, such as users with gender role stereotypes. In this work, we examine differences in the types of identities in profiles of human and bot accounts with a focus on combinations of identities that represent gender role stereotypes. We find that some types of identities differentiate between human and bot profiles, confirming this approach can be a useful in distinguishing between human and bot accounts on social media. However, contrary to our expectations, we reveal that gender bias is expressed more in human accounts than bots overall. Despite having less gender bias overall, we provide examples of identities with strong associations with gender identities in bot profiles, such as those related to technology, finance, sports, and horoscopes. Finally, we discuss implications for designing constructive social media bot detection training materials. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_05246 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Blended Bots: Infiltration through Identity Deception on Social Media Phillips, Samantha C. Ng, Lynnette Hui Xian Carley, Kathleen M. Human-Computer Interaction Social and Information Networks Bots are automated social media users that can be used to amplify (mis)information and sow harmful discourse. In order to effectively influence users, bots can be generated to reproduce human user behavior. Indeed, people tend to trust information coming from users with profiles that fit roles they expect to exist, such as users with gender role stereotypes. In this work, we examine differences in the types of identities in profiles of human and bot accounts with a focus on combinations of identities that represent gender role stereotypes. We find that some types of identities differentiate between human and bot profiles, confirming this approach can be a useful in distinguishing between human and bot accounts on social media. However, contrary to our expectations, we reveal that gender bias is expressed more in human accounts than bots overall. Despite having less gender bias overall, we provide examples of identities with strong associations with gender identities in bot profiles, such as those related to technology, finance, sports, and horoscopes. Finally, we discuss implications for designing constructive social media bot detection training materials. |
| title | Blended Bots: Infiltration through Identity Deception on Social Media |
| topic | Human-Computer Interaction Social and Information Networks |
| url | https://arxiv.org/abs/2406.05246 |