Interactively Learning Social Media Representations Improves News Source Factuality Detection
Published in Findings of IJCNLP-AACL 2023, 2023
We develop an interactive framework to detect the factuality of news sources. We take advantage of human insight, where humans interact on sub-graphs, providing us information about user similarity. This interaction is simple for humans and very quick. It also doesn’t require humans to be aware of the factuality of news content. We then combine this interaction with a graph deep learning framework, showing how these interactions lead to significant performance improvements. Our novel contribution is where humans should interact (for maximum impact), what they should interact on, and how we should incorporate the interactions.