Dynamics and characteristics of misinformation related to earthquake predictions on Twitter

Dallo, I., Elroy, O., Fallou, L., Komendantova, N. ORCID: https://orcid.org/0000-0003-2568-6179, & Yosipof, A. (2023). Dynamics and characteristics of misinformation related to earthquake predictions on Twitter. Scientific Reports 13 (1) e13391. 10.1038/s41598-023-40399-9.

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Abstract

The spread of misinformation on social media can lead to inappropriate behaviors that can make disasters worse. In our study, we focused on tweets containing misinformation about earthquake predictions and analyzed their dynamics. To this end, we retrieved 82,129 tweets over a period of 2 years (March 2020–March 2022) and hand-labeled 4157 tweets. We used RoBERTa to classify the complete dataset and analyzed the results. We found that (1) there are significantly more not-misinformation than misinformation tweets; (2) earthquake predictions are continuously present on Twitter with peaks after felt events; and (3) prediction misinformation tweets sometimes link or tag official earthquake notifications from credible sources. These insights indicate that official institutions present on social media should continuously address misinformation (even in quiet times when no event occurred), check that their institution is not tagged/linked in misinformation tweets, and provide authoritative sources that can be used to support their arguments against unfounded earthquake predictions.

Item Type:	Article
Research Programs:	Advancing Systems Analysis (ASA) Advancing Systems Analysis (ASA) > Cooperation and Transformative Governance (CAT)
Depositing User:	Luke Kirwan
Date Deposited:	17 Aug 2023 11:31
Last Modified:	17 Aug 2023 11:31
URI:	https://pure.iiasa.ac.at/19013

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