融合深度学习与模糊逻辑，检测TikTok视频中的虚假信息
A New Hybrid Intelligent Approach for Multimodal Detection of Suspected Disinformation on TikTok
- 用多模态特征分析提取文本、音频、视频信息
- 结合人体语言等行为线索，判断内容可疑度
- 生成详细报告，适合平台内容审核与研究使用
在多媒体内容快速传播的背景下，识别TikTok等社交平台上的虚假信息面临巨大挑战。本文提出一种混合框架，结合深度学习的计算能力与模糊逻辑的可解释性，用于检测TikTok视频中的疑似虚假信息。方法包含两个核心模块：多模态特征分析器，从文本、音频和视频中提取并评估数据；基于模糊逻辑的多模态虚假信息检测器。二者协同工作，依据人体动作、语调模式和文本连贯性等人类行为线索，评估内容传播虚假信息的可能性。实验分为两类：一类聚焦特定情境下的虚假信息识别，另一类测试模型在更广泛主题上的可扩展性。对每条视频，系统生成高质量、结构完整、详尽的分析报告，全面呈现虚假信息行为特征。
原文摘要 · Abstract (English)
In the context of the rapid dissemination of multimedia content, identifying disinformation on social media platforms such as TikTok represents a significant challenge. This study introduces a hybrid framework that combines the computational power of deep learning with the interpretability of fuzzy logic to detect suspected disinformation in TikTok videos. The methodology is comprised of two core components: a multimodal feature analyser that extracts and evaluates data from text, audio, and video; and a multimodal disinformation detector based on fuzzy logic. These systems operate in conjunction to evaluate the suspicion of spreading disinformation, drawing on human behavioural cues such as body language, speech patterns, and text coherence. Two experiments were conducted: one focusing on context-specific disinformation and the other on the scalability of the model across broader topics. For each video evaluated, high-quality, comprehensive, well-structured reports are generated, providing a detailed view of the disinformation behaviours.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。