CoALFake uses human-LLM co-annotation and domain-aware active learning to improve cross-domain fake news detection with low human effort.
arXiv preprint arXiv:2007.03316 (2020) 20
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
Cold users dominate fake news datasets, and the User Evidence Network approximates their absent behavior data from existing user interactions to enable robust misinformation detection.
citing papers explorer
-
CoALFake: Collaborative Active Learning with Human-LLM Co-Annotation for Cross-Domain Fake News Detection
CoALFake uses human-LLM co-annotation and domain-aware active learning to improve cross-domain fake news detection with low human effort.
-
Real-World Challenges in Fake News Detection: Dealing with Posts by Cold Users
Cold users dominate fake news datasets, and the User Evidence Network approximates their absent behavior data from existing user interactions to enable robust misinformation detection.