Repeat Spreaders and Election Delegitimization
This paper introduces and presents a first analysis of a uniquely curated dataset of misinformation, disinformation, and rumors spreading on Twitter about the 2020 U.S. election. Previous research on misinformation—an umbrella term for false and misleading content—has largely focused either on broad...
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| Format: | Article |
| Language: | English |
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HOPE
2023-12-01
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| Series: | Journal of Quantitative Description: Digital Media |
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| Online Access: | https://journalqd.org/article/view/3137 |
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| author | Ian Kennedy Morgan Wack Andrew Beers Joseph S. Schafer Isabella Garcia-Camargo Emma S. Spiro Kate Starbird |
| author_facet | Ian Kennedy Morgan Wack Andrew Beers Joseph S. Schafer Isabella Garcia-Camargo Emma S. Spiro Kate Starbird |
| author_sort | Ian Kennedy |
| collection | DOAJ |
| description | This paper introduces and presents a first analysis of a uniquely curated dataset of misinformation, disinformation, and rumors spreading on Twitter about the 2020 U.S. election. Previous research on misinformation—an umbrella term for false and misleading content—has largely focused either on broad categories, using a finite set of keywords to cover a complex topic, or on a few, focused case studies, with increased precision but limited scope. Our approach, by comparison, leverages real-time reports collected from September through November 2020 to develop a comprehensive dataset of tweets connected to 456 distinct misinformation stories from the 2020 U.S. election (our ElectionMisinfo2020 dataset), 307 of which sowed doubt in the legitimacy of the election. By relying on real-time incidents and streaming data, we generate a curated dataset that not only provides more granularity than a large collection based on a finite number of search terms, but also an improved opportunity for generalization compared to a small set of case studies. Though the emphasis is on misleading content, not all of the tweets linked to a misinformation story are false: some are questions, opinions, corrections, or factual content that nonetheless contributes to misperceptions. Along with a detailed description of the data, this paper provides an analysis of a critical subset of election-delegitimizing misinformation in terms of size, content, temporal diffusion, and partisanship. We label key ideological clusters of accounts within interaction networks, describe common misinformation narratives, and identify those accounts which repeatedly spread misinformation.
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| format | Article |
| id | doaj-art-8d054e4b366e492496aceb06eb3efd7e |
| institution | DOAJ |
| issn | 2673-8813 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | HOPE |
| record_format | Article |
| series | Journal of Quantitative Description: Digital Media |
| spelling | doaj-art-8d054e4b366e492496aceb06eb3efd7e2025-08-20T03:04:49ZengHOPEJournal of Quantitative Description: Digital Media2673-88132023-12-01210.51685/jqd.2022.013Repeat Spreaders and Election DelegitimizationIan Kennedy0Morgan Wack1Andrew Beers2Joseph S. Schafer3Isabella Garcia-Camargo4Emma S. Spiro5Kate Starbird6University of WashingtonUniversity of WashingtonUniversity of WashingtonUniversity of WashingtonKrebs Stamos Group, USAUniversity of WashingtonUniversity of WashingtonThis paper introduces and presents a first analysis of a uniquely curated dataset of misinformation, disinformation, and rumors spreading on Twitter about the 2020 U.S. election. Previous research on misinformation—an umbrella term for false and misleading content—has largely focused either on broad categories, using a finite set of keywords to cover a complex topic, or on a few, focused case studies, with increased precision but limited scope. Our approach, by comparison, leverages real-time reports collected from September through November 2020 to develop a comprehensive dataset of tweets connected to 456 distinct misinformation stories from the 2020 U.S. election (our ElectionMisinfo2020 dataset), 307 of which sowed doubt in the legitimacy of the election. By relying on real-time incidents and streaming data, we generate a curated dataset that not only provides more granularity than a large collection based on a finite number of search terms, but also an improved opportunity for generalization compared to a small set of case studies. Though the emphasis is on misleading content, not all of the tweets linked to a misinformation story are false: some are questions, opinions, corrections, or factual content that nonetheless contributes to misperceptions. Along with a detailed description of the data, this paper provides an analysis of a critical subset of election-delegitimizing misinformation in terms of size, content, temporal diffusion, and partisanship. We label key ideological clusters of accounts within interaction networks, describe common misinformation narratives, and identify those accounts which repeatedly spread misinformation. https://journalqd.org/article/view/3137misinformationdisinformationtwitter |
| spellingShingle | Ian Kennedy Morgan Wack Andrew Beers Joseph S. Schafer Isabella Garcia-Camargo Emma S. Spiro Kate Starbird Repeat Spreaders and Election Delegitimization Journal of Quantitative Description: Digital Media misinformation disinformation |
| title | Repeat Spreaders and Election Delegitimization |
| title_full | Repeat Spreaders and Election Delegitimization |
| title_fullStr | Repeat Spreaders and Election Delegitimization |
| title_full_unstemmed | Repeat Spreaders and Election Delegitimization |
| title_short | Repeat Spreaders and Election Delegitimization |
| title_sort | repeat spreaders and election delegitimization |
| topic | misinformation disinformation |
| url | https://journalqd.org/article/view/3137 |
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