Evolving to the Future: Unseen Event Adaptive Fake News Detection on Social Media

• URL: http://arxiv.org/abs/2403.00037v2
• Date: Mon, 11 Nov 2024 18:27:18 GMT
• Title: Evolving to the Future: Unseen Event Adaptive Fake News Detection on Social Media
• Authors: Jiajun Zhang, Zhixun Li, Qiang Liu, Shu Wu, Liang Wang,
• Abstract summary: We introduce textbfFuture textbfADaptive textbfEvent-based Fake news Detection (FADE) framework. Specifically, we train a target predictor through an adaptive augmentation strategy and graph contrastive learning to obtain higher-quality features. We further mitigate event bias by subtracting the event-only predictor's output from the target predictor's output to obtain the final prediction.
• Score: 27.236656042545796
• License:
• Abstract: With the rapid development of social media, the wide dissemination of fake news on social media is increasingly threatening both individuals and society. One of the unique challenges for fake news detection on social media is how to detect fake news on future events. Recently, numerous fake news detection models that utilize textual information and the propagation structure of posts have been proposed. Unfortunately, most of the existing approaches can hardly handle this challenge since they rely heavily on event-specific features for prediction and cannot generalize to unseen events. To address this, we introduce \textbf{F}uture \textbf{AD}aptive \textbf{E}vent-based Fake news Detection (FADE) framework. Specifically, we train a target predictor through an adaptive augmentation strategy and graph contrastive learning to obtain higher-quality features and make more accurate overall predictions. Simultaneously, we independently train an event-only predictor to obtain biased predictions. We further mitigate event bias by subtracting the event-only predictor's output from the target predictor's output to obtain the final prediction. Encouraging results from experiments designed to emulate real-world social media conditions validate the effectiveness of our method in comparison to existing state-of-the-art approaches.

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