# Characterizing and Predicting Social Correction on Twitter

arXiv: https://arxiv.org/abs/2303.08889 · HTML: https://mlsanity.com/paper/2303.08889/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2023.

## Built on by (most cited first, 7 of 7)

- [NLP for Counterspeech against Hate: A Survey and How-To Guide](https://mlsanity.com/paper/2403.20103/) (2024)
- [Countering Misinformation via Emotional Response Generation](https://mlsanity.com/paper/2311.10587/) (2023)
- [Limited Effectiveness of LLM-based Data Augmentation for COVID-19 Misinformation Stance Detection](https://mlsanity.com/paper/2503.02328/) (2025)
- [Towards Fair Graph Anomaly Detection: Problem, Benchmark Datasets, and Evaluation](https://mlsanity.com/paper/2402.15988/) (2024)
- [Hierarchical Query Classification in E-commerce Search](https://mlsanity.com/paper/2403.06021/) (2024)
- [SPOT: An Annotated French Corpus and Benchmark for Detecting Critical Interventions in Online Conversations](https://mlsanity.com/paper/2511.07405/) (2025)
- [Angry but Accurate: Detecting and Profiling the Counter-Misinformation Ecosystem on Twitter](https://mlsanity.com/paper/2607.02900/) (2026)

## Abstract (arXiv)

Online misinformation has been a serious threat to public health and society. Social media users are known to reply to misinformation posts with counter-misinformation messages, which have been shown to be effective in curbing the spread of misinformation. This is called social correction. However, the characteristics of tweets that attract social correction versus those that do not remain unknown. To close the gap, we focus on answering the following two research questions: (1) ``Given a tweet, will it be countered by other users?'', and (2) ``If yes, what will be the magnitude of countering it?''. This exploration will help develop mechanisms to guide users' misinformation correction efforts and to measure disparity across users who get corrected. In this work, we first create a novel dataset with 690,047 pairs of misinformation tweets and counter-misinformation replies. Then, stratified analysis of tweet linguistic and engagement features as well as tweet posters' user attributes are conducted to illustrate the factors that are significant in determining whether a tweet will get countered. Finally, predictive classifiers are created to predict the likelihood of a misinformation tweet to get countered and the degree to which that tweet will be countered. The code and data is accessible on https://github.com/claws-lab/social-correction-twitter.