Identifying and Characterizing Active Citizens who Refute Misinformation in Social Media

TL;DR

This study introduces a new dataset and models for identifying active citizens who refute misinformation on social media platforms like Twitter and Weibo, across English and Chinese languages.

Contribution

It provides the first cross-platform, multilingual analysis of active citizens refuting misinformation, including a new dataset and evaluation of supervised models.

Findings

Supervised models can effectively distinguish active citizens from misinformation posters.

Language use varies significantly between user categories across platforms.

The new dataset enables better understanding of misinformation refutation behaviors.

Abstract

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.