Citation

Hunt K, Agarwal P, Zhuang J. Risk Anal. 2020; ePub(ePub): ePub.

Copyright

(Copyright © 2020, Society for Risk Analysis, Publisher John Wiley and Sons)

DOI

10.1111/risa.13634

PMID

33190276

Abstract

Social media has been increasingly utilized to spread breaking news and risk communications during disasters of all magnitudes. Unfortunately, due to the unmoderated nature of social media platforms such as Twitter, rumors and misinformation are able to propagate widely. Given this, a surfeit of research has studied false rumor diffusion on Twitter, especially during natural disasters. Within this domain, studies have also focused on the misinformation control efforts from government organizations and other major agencies. A prodigious gap in research exists in studying the monitoring of misinformation on social media platforms in times of disasters and other crisis events. Such studies would offer organizations and agencies new tools and ideologies to monitor misinformation on platforms such as Twitter, and make informed decisions on whether or not to use their resources in order to debunk. In this work, we fill the research gap by developing a machine learning framework to predict the veracity of tweets that are spread during crisis events. The tweets are tracked based on the veracity of their content as either true, false, or neutral. We conduct four separate studies, and the results suggest that our framework is capable of tracking multiple cases of misinformation simultaneously, with F1 scores exceeding 87%. In the case of tracking a single case of misinformation, our framework reaches an F1 score of 83%. We collect and drive the algorithms with 15,952 misinformation‐related tweets from the Boston Marathon bombing (2013), Manchester Arena bombing (2017), Hurricane Harvey (2017), Hurricane Irma (2017), and the Hawaii ballistic missile false alert (2018). This article provides novel insights on how to efficiently monitor misinformation that is spread during disasters.

Language: en

Keywords

social media; Emergency management; machine learning; fake news; risk communications