Angry but Accurate: Detecting and Profiling the Counter-Misinformation Ecosystem on Twitter
arXiv:2607.02900v2 Announce Type: replace-cross Abstract: 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- an
Record details
Published: 10 August 2026
Source: arXiv cs.CY
Category: Research
Topics: Misinformation
Retrieved: 10 August 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
The GenAI Catch-22: Use of Generative Artificial Intelligence in Norwegian Newsrooms During the 2025 Parliamentary Election
arXiv cs.CY · 12 August 2026
Turning OSINV inside the machine: Open-source investigations and information disorder
HKS Misinformation Review · 12 August 2026
Multi-Agent Forensic Reasoning for Generalizable Deepfake Video Detection
arXiv · 7 August 2026
Is Misinformation More Open? A Study of robots.txt Gatekeeping on the Web
arXiv cs.CY · 13 August 2026
Public support for misinformation interventions depends on perceived fairness, effectiveness, and intrusiveness
arXiv cs.CY · 13 August 2026
From Cheap Fakes to Pure Synthesis: Addressing the New Era of T2V Fake News Videos
arXiv · 7 August 2026
How to cite this record
ethics.ai (10 August 2026), “Angry but Accurate: Detecting and Profiling the Counter-Misinformation Ecosystem on Twitter,” evidence record 17772, https://ethics.ai/record/17772 (originally published by arXiv cs.CY).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.