[提交于 2026年7月2日]

标题：动荡的回响：一个用于假新闻与暴力驱动群体活动早期预警的多模态NLP框架

查看论文《动荡的回响：一个用于假新闻与暴力驱动群体活动早期预警的多模态NLP框架》的PDF，作者Md. Maruf Bangabashi 及其他5位作者

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摘要：社交媒体的迅速发展通过实现快速信息交换改变了全球通信，但也加速了虚假信息的传播。假新闻、被操纵的内容和煽动性叙事越来越多地与社会动荡、政治不稳定和群体暴力相关联。南亚和其他地区的事件表明，通过Facebook和WhatsApp等平台传播的虚假信息可能引发真实世界的伤害，往往比事实核查工作反应更快。为了解决这一挑战，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易引发暴力的动态。通过结合多个基准数据集，创建了一个包含138,256个孟加拉语和英语样本的融合数据集。该框架集成了XLM-RoBERTa用于多语言文本表示，CLIP用于视觉嵌入，以及多头注意力机制用于多模态融合，并辅以讽刺和地理空间元数据等辅助特征。在分层抽样的30%子集上的实验达到了98%的测试准确率，并具有较高的精确率和召回率。结果表明，多模态方法在早期虚假信息检测中具有有效性，并突显了地理空间信号在预测真实世界事态升级中的附加价值。

提交历史

来自：Md. Maruf Bangabashi 查看电子邮件

[v1] 2026年7月2日 星期四 20:05:40 UTC (5,274 KB)

[Submitted on 2 Jul 2026]

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

View a PDF of the paper titled Echoes of Unrest: A Multimodal NLP Framework for Early Warning of Fake News and Violence-Driven Mob Activity, by Md. Maruf Bangabashi and 5 other authors

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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. 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. 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.

Submission history

From: Md. Maruf Bangabashi [

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[v1]

Thu, 2 Jul 2026 20:05:40 UTC (5,274 KB)