Identifying and Characterizing Active Citizens who Refute Misinformation in Social Media
This work addresses the challenge of misinformation analysis in computational social science by enabling automated detection of users who counter misinformation, though it is incremental as it applies existing methods to new data and platforms.
The paper tackles the problem of automatically identifying and characterizing active citizens who refute misinformation on social media, by developing a new dataset for Weibo and evaluating supervised models on it and an existing Twitter dataset, achieving results that include an analysis of language differences between user categories.
The phenomenon of misinformation spreading in social media has developed a new form of active citizens who focus on tackling the problem by refuting posts that might contain misinformation. Automatically identifying and characterizing the behavior of such active citizens in social media is an important task in computational social science for complementing studies in misinformation analysis. In this paper, we study this task across different social media platforms (i.e., Twitter and Weibo) and languages (i.e., English and Chinese) for the first time. To this end, (1) we develop and make publicly available a new dataset of Weibo users mapped into one of the two categories (i.e., misinformation posters or active citizens); (2) we evaluate a battery of supervised models on our new Weibo dataset and an existing Twitter dataset which we repurpose for the task; and (3) we present an extensive analysis of the differences in language use between the two user categories.