Published July 21, 2024 | Version v2

ElectionRumors2022: A Dataset of Election Rumors on Twitter During the 2022 US Midterms

Description

Dataset for forthcoming preprint/paper on rumors in the 2022 U.S. midterm elections. Abstract from this forthcoming paper is attached below.

 

Understanding the spread of online rumors is a pressing societal challenge and an active area of research across domains. In the context of the 2022 U.S. midterm elections, one influential social media platform for sharing information — including rumors that may be false, misleading, or unsubstantiated — was Twitter (now renamed X). To increase understanding of the dynamics of online rumors about elections, we present and analyze a dataset of 1.81 million Twitter posts corresponding to 135 distinct rumors which spread online during the midterm election season (September 5 to December 1, 2022). We describe how this data was collected, compiled, and supplemented, and provide a series of exploratory analyses along with comparisons to a previously-published dataset on 2020 election rumors. We also conduct a mixed-methods analysis of three distinct rumors about the election in Arizona, a particularly prominent focus of 2022 election rumoring. Finally, we provide a set of potential future directions for how this dataset could be used to facilitate future research into online rumors, misinformation, and disinformation.

 
Funding for this work has come from the University of Washington’s Center for an Informed Public, the John S. and James L. Knight Foundation (G-2019-58788), Craig Newmark Philanthropies, the William and Flora Hewlett Foundation, the Election Trust Initiative, the National Science Foundation (grant #1749815 and grant #2120496) and NSF Graduate Research Fellowships under Grant No DGE-2140004, for both Joseph S. Schafer and Kayla Duskin. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation or other funders.
 
Joseph S. Schafer and Kayla Duskin are co-first authors on this paper.

Files

domains_table_to_release.csv

Files (275.8 MB)

Name Size
md5:061f431dc65582646e4e5bc5b7ed0a2d
49.4 MB Preview Download
md5:afe0fd9849c98bc26e8fd294af4ca60b
30.5 kB Preview Download
md5:ae4f963dca610482b7b3ff2e14ea7ba2
57.8 MB Preview Download
md5:6010901bb83c540be3dbbba8fe9258fc
168.6 MB Preview Download