Grunnleggende konsepter
The author introduces the FADE framework for fake news detection, emphasizing robustness and generalizability in detecting fake news from unseen events on social media.
Sammendrag
The rapid rise of social media has led to an increase in fake news dissemination, posing threats to individuals and society. Existing methods lack robustness in detecting fake news about future events. The FADE framework addresses this by combining a target predictor with an event-only predictor for debiasing during inference. Experimental results show FADE outperforms existing methods across three real-world datasets.
Key points:
- Social media's growth leads to increased fake news.
- Current methods lack robustness in detecting future event-related fake news.
- FADE combines target and event-only predictors for improved detection.
- Experiments show FADE outperforms existing methods on real-world datasets.
Statistikk
With disturbances up to 30%, FADE's accuracy remains stable, dropping by less than 4% on Twitter16, under 2% on Twitter15, and 1% on PHEME.
The optimal performance is achieved when the bias coefficient (β) is set at 0.1.
Sitater
"Existing fake detection methods exhibit a lack of robustness and cannot generalize to unseen events."
"Our adaptive augmentation strategy generates superior augmented samples compared to other manually designed augmentation."