What is going on? An evidence-frame framework for analyzing online rumors about election integrity

Pervasive falsehoods that erode trust in election processes are of increasing concern to democracies around the world. Misleading claims like these are often understood as simply ''getting the facts wrong''. Using a grounded, interpretative, mixed-method approach to study Twitter activity during the 2022 U.S. Midterm Election in Arizona, our work paints a more nuanced picture. We adapt Klein's data-frame theory of collective sensemaking to online rumors, demonstrating how misleading claims about election administration take shape online through interactions between (often factual) evidence and frames. We introduce a methodological approach for analyzing rumors through this evidence-frame lens and provide insights into the dynamics of online rumoring around claims of ''rigged elections''. Our work highlights how rumors are as much about political framing as they are about faulty facts, and locates the crux of the problem of misinformation in the interactions with and between evidence and distorted political frames.

Authors

Institutions

Publication Details

Journal
Proceedings of the ACM on Human-Computer Interaction
Published
2025-10-16
DOI
https://doi.org/10.1145/3757522
Citations
2
Primary Topic
Misinformation and Its Impacts
Type
article
Field-Weighted Citation Impact
8.40
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

What is going on? An evidence-frame framework for analyzing online rumors about election integrity

Kate Starbird, Stephen Prochaska, Ben Yamron
2 citations
Proceedings of the ACM on Human-Computer Interaction
Misinformation and Its Impacts
8.40
article

What is going on? An evidence-frame framework for analyzing online rumors about election integrity

Kate Starbird, Stephen Prochaska, Ben Yamron
article en
2 citations

Abstract

Pervasive falsehoods that erode trust in election processes are of increasing concern to democracies around the world. Misleading claims like these are often understood as simply ''getting the facts wrong''. Using a grounded, interpretative, mixed-method approach to study Twitter activity during the 2022 U.S. Midterm Election in Arizona, our work paints a more nuanced picture. We adapt Klein's data-frame theory of collective sensemaking to online rumors, demonstrating how misleading claims about election administration take shape online through interactions between (often factual) evidence and frames. We introduce a methodological approach for analyzing rumors through this evidence-frame lens and provide insights into the dynamics of online rumoring around claims of ''rigged elections''. Our work highlights how rumors are as much about political framing as they are about faulty facts, and locates the crux of the problem of misinformation in the interactions with and between evidence and distorted political frames.

Proceedings of the ACM on Human-Computer InteractionVol. 9(7)
Wested (US), University of Washington (US)
Openalex Percentile: Top 3%
Misinformation and Its Impacts
8.40
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.