arXiv cs.AI· Manar Alsaid·· 3 天前AI 评分22
研究评估 AI 模型在预设社交噪声构念下的虚假信息检测表现
Assessing the Performance of AI Models in Misinformation Detection Using Predefined Social Noise Constructs
AI 导读
一项研究评估了六种文本模型和一个图神经网络（GNN）在预设社交噪声构念下缓解虚假信息的效果，数据集为 2024 年 3 月至 2025 年 3 月从 X 平台采集的 50 万条乌克兰话题推文。
正文 · 原文
Abstract:Social media platforms generate vast volumes of user-generated content that often obscure meaningful discourse, a phenomenon conceptualized as social noise. While existing misinformation detection methods primarily focus on factual verification, they largely overlook the role of social noise in amplifying misinformation. This study assesses the performance of six text-based models and a graph neural network (GNN) in mitigating the effects of misinformation using predefined social noise constructs. The dataset consists of 500,000 tweets collected from the X platform using the Ukraine hashtag during the Russia-Ukraine conflict from March 2024 to March 2025. Advanced machine learning models achieve strong performance, with accuracies ranging from 79.94% to 97.17%. RoBERTa demonstrates the highest performance, with an accuracy of 97.17%, Cohen's kappa of 0.9660, MCC of 0.9660, and AUC of 0.9977. The GNN achieves an accuracy of 64.52% and an AUC of 0.899, demonstrating performance above random-chance classification by exploiting the relational structure between tweets. All text-based models achieve kappa values greater than 0.75, indicating substantial overall agreement. Explainable AI analysis using LIME reveals that 89.6% of predictive weight is attributed to contextual features rather than isolated keywords. The findings highlight that integrating social noise into AI pipelines enhances misinformation detection and supports more effective mitigation strategies in large-scale social media environments.
| Comments: | 22 pages, 12 tables, 2 figures and one Appendix |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.17469 [cs.LG] |
| (or arXiv:2610.17469v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.17469 arXiv-issued DOI via DataCite (pending registration) |
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| Journal reference: | ACM Transactions on Asian and Low-Resource Language Information Processing, 2026 |
Submission history
From: Manar Alsaid [view email]
[v1]
Mon, 12 Oct 2026 22:31:47 UTC (1,554 KB)
来源：arXiv cs.AI · arxiv.org