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

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.