Detecting COVID-19 Fake News (RMG)

Fake news can kill. Many people who believed COVID-19 fake news did not get vaccinated, did not wear masks, did not social distance, got unnecessarily infected, and died. Furthermore, accurately detecting fake news and predicting its virality is hard. In our pilot study, we integrated situational theory of problem solving (STOPS, *Kim & Grunig, 2011) and information market theory (*Kim & Gil de Zúñiga, 2021) into a theoretical model of attributes of users (e.g., followers) and messages (personal relationship, emotion-eliciting words, vocabulary, politeness, and uncertainty markers); then we empirically showed that this model detected COVID-19 fake news with 95% accuracy. After downloading millions of available Twitter tweets in English, Chinese, Korean, or French regarding COVID-19 from November 20, 2019 to the present, we identify tweets with links to true versus fake COVID-19 news stories (based on fact-checking sites). Then, we separately use (a) machine learning methods (support vector machine, Pisner & Schnyer, 2020; and deep neural network, Liu, 2017) and (b) statistics (statistical discourse analysis, invented by PI, *Chiu, 2008) to model fake news, before integrating them together (adding successful statistical model components into the machine learning programs). Next, we differentiate distinct online communities (via clustering algorithms, e.g., Gephi software) and model how each tweet spreads (scope and speed) both within and across online communities with another statistical method (multilevel diffusion analysis, invented by PI, Rossman, *Chiu & Mol, 2008). Overall, this project will build foundational knowledge about fake news detection that can be expanded to other languages, crises, and issues.

Funding Source: UGC - Research Matching Grant (RMG)