mlsanityData to 7 Oct 2026

Identifying and Characterizing Active Citizens who Refute Misinformation in Social Media

arXiv 2204.10080 ↗First posted 21 Apr 2022cs.CL cs.SIcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 37 of its 79 references that are in the map. First posted on arXiv 21 Apr 2022.

Builds on

37 of 79 references.

References · 37

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25 more
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Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 2; 2024: 1.

5 papers.

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  3. 2022Improving Graph-Based Text Representations with Character and Word Level N-gramscited by 2
  4. 2022Domain Classification-based Source-specific Term Penalization for Domain Adaptation in Hate-speech Detectioncited by 0
  5. 2023A Large-Scale Comparative Study of Accurate COVID-19 Information versus Misinformationcited by 0