Identifying and characterizing superspreaders of low-credibility content on Twitter
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2022
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| _version_ | 1866911768005050368 |
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| author | DeVerna, Matthew R. Aiyappa, Rachith Pacheco, Diogo Bryden, John Menczer, Filippo |
| author_facet | DeVerna, Matthew R. Aiyappa, Rachith Pacheco, Diogo Bryden, John Menczer, Filippo |
| contents | The world's digital information ecosystem continues to struggle with the spread of misinformation. Prior work has suggested that users who consistently disseminate a disproportionate amount of low-credibility content -- so-called superspreaders -- are at the center of this problem. We quantitatively confirm this hypothesis and introduce simple metrics to predict the top superspreaders several months into the future. We then conduct a qualitative review to characterize the most prolific superspreaders and analyze their sharing behaviors. Superspreaders include pundits with large followings, low-credibility media outlets, personal accounts affiliated with those media outlets, and a range of influencers. They are primarily political in nature and use more toxic language than the typical user sharing misinformation. We also find concerning evidence that suggests Twitter may be overlooking prominent superspreaders. We hope this work will further public understanding of bad actors and promote steps to mitigate their negative impacts on healthy digital discourse. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2207_09524 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Identifying and characterizing superspreaders of low-credibility content on Twitter DeVerna, Matthew R. Aiyappa, Rachith Pacheco, Diogo Bryden, John Menczer, Filippo Social and Information Networks Computers and Society Human-Computer Interaction The world's digital information ecosystem continues to struggle with the spread of misinformation. Prior work has suggested that users who consistently disseminate a disproportionate amount of low-credibility content -- so-called superspreaders -- are at the center of this problem. We quantitatively confirm this hypothesis and introduce simple metrics to predict the top superspreaders several months into the future. We then conduct a qualitative review to characterize the most prolific superspreaders and analyze their sharing behaviors. Superspreaders include pundits with large followings, low-credibility media outlets, personal accounts affiliated with those media outlets, and a range of influencers. They are primarily political in nature and use more toxic language than the typical user sharing misinformation. We also find concerning evidence that suggests Twitter may be overlooking prominent superspreaders. We hope this work will further public understanding of bad actors and promote steps to mitigate their negative impacts on healthy digital discourse. |
| title | Identifying and characterizing superspreaders of low-credibility content on Twitter |
| topic | Social and Information Networks Computers and Society Human-Computer Interaction |
| url | https://arxiv.org/abs/2207.09524 |