Echoes of Unrest: A Multimodal NLP Framework for Early Warning of Fake News and Violence-Driven Mob Activity

动荡的回响：用于虚假新闻与暴力煽动性群体活动预警的多模态自然语言处理框架

Abstract: Rapid growth in social media has transformed global communication by enabling fast information exchange, but it has also accelerated the spread of misinformation. Fake news, manipulated content, and provocative narratives are increasingly linked to social unrest, political instability, and mob violence. Incidents in South Asia and elsewhere demonstrate how false information disseminated via platforms such as Facebook and WhatsApp can trigger real-world harm, often spreading faster than fact-checking efforts can respond.

摘要： 社交媒体的飞速发展通过实现快速信息交换改变了全球通信方式，但也加速了虚假信息的传播。虚假新闻、被操纵的内容以及煽动性叙事正日益与社会动荡、政治不稳定和群体暴力事件挂钩。南亚及其他地区的事件表明，通过 Facebook 和 WhatsApp 等平台传播的虚假信息如何引发现实世界的危害，且其传播速度往往超过了事实核查的响应速度。

To address this challenge, this chapter presents a multilingual, multimodal Natural Language Processing (NLP) framework for early detection of misinformation and violence-prone dynamics. A fused dataset of 138,256 Bangla and English samples was created by combining multiple benchmark datasets. The framework integrates XLM-RoBERTa for multilingual text representation, CLIP for visual embedding, and a multi-head attention mechanism for multimodal fusion, enhanced with auxiliary features such as sarcasm and geospatial metadata.

为了应对这一挑战，本章提出了一个多语言、多模态的自然语言处理（NLP）框架，用于早期检测虚假信息和暴力倾向动态。研究人员通过整合多个基准数据集，创建了一个包含 138,256 条孟加拉语和英语样本的融合数据集。该框架集成了用于多语言文本表示的 XLM-RoBERTa、用于视觉嵌入的 CLIP，以及用于多模态融合的多头注意力机制，并辅以讽刺检测和地理空间元数据等辅助特征。

Experiments on a stratified 30% subset achieved 98% test accuracy with strong precision and recall. The outcomes show the efficacy of multimodal approaches in early misinformation detection and highlight the added value of geospatial signals for anticipating real-world escalation.

在分层抽取的 30% 子集上进行的实验达到了 98% 的测试准确率，并表现出强大的精确率和召回率。研究结果证明了多模态方法在早期虚假信息检测中的有效性，并突显了地理空间信号在预测现实世界局势升级方面的附加价值。

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Paper Details:

• Authors: Md. Maruf Bangabashi, Tahmid Hasan, Golam Mahmud, Md. Mostafijur Rahman, Md. Toufiqur Rahman, Jahanur Biswas
• Submission Date: 2 Jul 2026
• Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
• DOI: 10.48550/arXiv.2607.02734

论文详情：

• 作者： Md. Maruf Bangabashi, Tahmid Hasan, Golam Mahmud, Md. Mostafijur Rahman, Md. Toufiqur Rahman, Jahanur Biswas
• 提交日期： 2026年7月2日
• 学科分类： 计算与语言 (cs.CL)；人工智能 (cs.AI)
• DOI： 10.48550/arXiv.2607.02734