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<p>The proliferation of reversal events on social media exacerbates fake news dissemination, undermining public trust and civil order. Such news often resurfaces across different periods, continuing to mislead the public even after debunking, posing a major challenge for detection. Existing studies primarily focus on label-specific correlations but overlook temporal shifts, reducing detection and generalization performance on unseen events. To address this, we introduce ReversalSV, a large-scale Chinese reversal multimodal news dataset covering diverse reversal events. Through macro- and micro-temporal analysis, we reveal unique temporal characteristics of such reversal event multimodal news. Additionally, we propose a <b>B</b>rain-inspired <b>M</b>emory-<b>R</b>eplay <b>M</b>odel (BMRM), which makes the first attempt to simulate human memory mechanisms for detecting fake news in reversal events. Inspired by memory replay theory, BMRM integrates semantic cognition and episodic memory for reversal fake news verification. Extensive experiments demonstrate BMRM’s superiority and establish a new paradigm for real-world multimodal fake news detection while providing a benchmark for evaluating models’ adaptability to evolving information landscapes.</p>

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Teaching models to adapt to world evolution: a large-scale benchmark and framework for reversal multimodal fake news detection

  • 期刊论文

  • 发表日期：

    2026年6月17日

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  • Yue Wu,
  • Siyan Nie,
  • Zhi Zeng,
  • Guodong Li

摘要

The proliferation of reversal events on social media exacerbates fake news dissemination, undermining public trust and civil order. Such news often resurfaces across different periods, continuing to mislead the public even after debunking, posing a major challenge for detection. Existing studies primarily focus on label-specific correlations but overlook temporal shifts, reducing detection and generalization performance on unseen events. To address this, we introduce ReversalSV, a large-scale Chinese reversal multimodal news dataset covering diverse reversal events. Through macro- and micro-temporal analysis, we reveal unique temporal characteristics of such reversal event multimodal news. Additionally, we propose a Brain-inspired Memory-Replay Model (BMRM), which makes the first attempt to simulate human memory mechanisms for detecting fake news in reversal events. Inspired by memory replay theory, BMRM integrates semantic cognition and episodic memory for reversal fake news verification. Extensive experiments demonstrate BMRM’s superiority and establish a new paradigm for real-world multimodal fake news detection while providing a benchmark for evaluating models’ adaptability to evolving information landscapes.