Diffusion of real versus misinformation during a crisis event: A big data-driven approach

Misinformation has captured the interest of academia in recent years with several studies looking at the topic broadly with inconsistent results. In this research, we attempt to bridge the gap in the literature by examining the impacts of user-, time-, and content-based characteristics that affect the virality of real versus misinformation during a crisis event. Using a big data-driven approach, we collected over 42 million tweets during Hurricane Harvey and obtained 3589 original verified real or false tweets by cross-checking with fact-checking websites and a relevant federal agency. Our results show that virality is higher for misinformation, novel tweets, and tweets with negative sentiment or lower lexical density. In addition, we reveal the opposite impacts of sentiment on the virality of real news versus misinformation. We also find that tweets on the environment are less likely to go viral than the baseline religious news, while real social news tweets are more likely to go viral than misinformation on social news.

Automated summary

Misinformation has become a focus of academic interest, but studies have yielded inconsistent results. To address this gap, this research examines the factors that impact the virality of real versus misinformation during a crisis event, including user characteristics, time characteristics, and content characteristics. By analyzing over 42 million tweets during Hurricane Harvey, the study found that misinformation, novel tweets, tweets with negative sentiment or lower lexical density tend to go viral. The study also discovered that sentiment has opposite effects on the virality of real news versus misinformation, and that certain types of news are more likely to go viral than others.