# Angry but Accurate: Detecting and Profiling the Counter-Misinformation Ecosystem on Twitter

arXiv: https://arxiv.org/abs/2607.02900 · HTML: https://mlsanity.com/paper/2607.02900/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 32 of its 65 references that are in the map.
- First posted on arXiv 3 Jul 2026.

## Builds on (most cited first, 30 of 32)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [Combining Fact Extraction and Verification with Neural Semantic Matching Networks](https://mlsanity.com/paper/1811.07039/) (2018)
- [COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic](https://mlsanity.com/paper/2106.03794/) (2021)
- [The Rise of Social Bots](https://mlsanity.com/paper/1407.5225/) (2014)
- [Combating Misinformation in the Age of LLMs: Opportunities and Challenges](https://mlsanity.com/paper/2311.05656/) (2023)
- [CoAID: COVID-19 Healthcare Misinformation Dataset](https://mlsanity.com/paper/2006.00885/) (2020)
- [A Survey on Stance Detection for Mis- and Disinformation Identification](https://mlsanity.com/paper/2103.00242/) (2021)
- [FakeCovid -- A Multilingual Cross-domain Fact Check News Dataset for COVID-19](https://mlsanity.com/paper/2006.11343/) (2020)
- [Scalable and Generalizable Social Bot Detection through Data Selection](https://mlsanity.com/paper/1911.09179/) (2019)
- [BotOrNot: A System to Evaluate Social Bots](https://mlsanity.com/paper/1602.00975/) (2016)
- [CoVERT: A Corpus of Fact-checked Biomedical COVID-19 Tweets](https://mlsanity.com/paper/2204.12164/) (2022)
- [FACT-GPT: Fact-Checking Augmentation via Claim Matching with LLMs](https://mlsanity.com/paper/2402.05904/) (2024)
- [Botometer 101: Social bot practicum for computational social scientists](https://mlsanity.com/paper/2201.01608/) (2022)
- [The Role of the Crowd in Countering Misinformation: A Case Study of the COVID-19 Infodemic](https://mlsanity.com/paper/2011.05773/) (2020)
- [Automated Claim Matching with Large Language Models: Empowering Fact-Checkers in the Fight Against Misinformation](https://mlsanity.com/paper/2310.09223/) (2023)
- [Leveraging Label Correlations in a Multi-label Setting: A Case Study in Emotion](https://mlsanity.com/paper/2210.15842/) (2022)
- [Community-Based Fact-Checking on Twitter's Birdwatch Platform](https://mlsanity.com/paper/2104.07175/) (2021)
- [Moral Emotions Shape the Virality of COVID-19 Misinformation on Social Media](https://mlsanity.com/paper/2202.03590/) (2022)
- [Combining Similarity Features and Deep Representation Learning for Stance Detection in the Context of Checking Fake News](https://mlsanity.com/paper/1811.00706/) (2018)
- [Social Bots and Social Media Manipulation in 2020: The Year in Review](https://mlsanity.com/paper/2102.08436/) (2021)
- [Characterizing and Predicting Social Correction on Twitter](https://mlsanity.com/paper/2303.08889/) (2023)
- [Limited Effectiveness of LLM-based Data Augmentation for COVID-19 Misinformation Stance Detection](https://mlsanity.com/paper/2503.02328/) (2025)
- [Taking a Stance on Fake News: Towards Automatic Disinformation Assessment via Deep Bidirectional Transformer Language Models for Stance Detection](https://mlsanity.com/paper/1911.11951/) (2019)
- [The COVMis-Stance dataset: Stance Detection on Twitter for COVID-19 Misinformation](https://mlsanity.com/paper/2204.02000/) (2022)
- [Fighting Fire with Fire: Adversarial Prompting to Generate a Misinformation Detection Dataset](https://mlsanity.com/paper/2401.04481/) (2024)
- [Comparing Toxicity Across Social Media Platforms for COVID-19 Discourse](https://mlsanity.com/paper/2302.14270/) (2023)

## Abstract (arXiv)

On social media, many users actively push back against false claims. Understanding who pushes back and how they do so matters, as this corrective activity is central to how misinformation is contested. We study this counter-misinformation ecosystem at scale: applying a domain-specific NLI model from our prior work to a large corpus of COVID-19 tweets, we classify 264,737 posts as supporting or opposing false claims and compare 23 user- and text-level features across the two groups. Contrary to the dominant assumption that negative emotion is a signature of falsehood, we find that misinformation-opposing posts are more emotionally negative than misinformation-supporting posts, with higher levels of anger, disgust, and sadness. These differences are modest in magnitude but consistent in direction across the negative emotions. We also find that posts opposing misinformation tend to come from more established users, i.e., older accounts, more followers, and higher listed counts.