Title: ThumbnailTruth: A Multi-Modal LLM Approach for Detecting Misleading YouTube Thumbnails Across Diverse Cultural Settings Show Chinese title

Title: 缩略图真实性：一种多模态大语言模型方法，用于在多种文化背景下检测具有误导性的YouTube缩略图

Abstract: Misleading video thumbnails on platforms like YouTube are a pervasive problem, undermining user trust and platform integrity. This paper proposes a novel multi-modal detection pipeline that uses Large Language Models (LLMs) to flag misleading thumbnails. We first construct a comprehensive dataset of 2,843 videos from eight countries, including 1,359 misleading thumbnail videos that collectively amassed over 7.6 billion views -- providing a unique cross-cultural perspective on this global issue. Our detection pipeline integrates video-to-text descriptions, thumbnail images, and subtitle transcripts to holistically analyze content and flag misleading thumbnails. Through extensive experimentation and prompt engineering, we evaluate the performance of state-of-the-art LLMs, including GPT-4o, GPT-4o Mini, Claude 3.5 Sonnet, and Gemini-1.5 Flash. Our findings show the effectiveness of LLMs in identifying misleading thumbnails, with Claude 3.5 Sonnet consistently showing strong performance, achieving an accuracy of 93.8\%, precision over 92\%, and recall exceeding 94\% in certain scenarios. We discuss the implications of our findings for content moderation, user experience, and the ethical considerations of deploying such systems at scale. Our findings pave the way for more transparent, trustworthy video platforms and stronger content integrity for audiences worldwide. Show Chinese abstract

Abstract: 误导性视频缩略图在YouTube等平台上是一个普遍的问题，这损害了用户信任和平台的完整性。 本文提出了一种新颖的多模态检测流程，利用大型语言模型（LLMs）来标记误导性缩略图。 我们首先构建了一个涵盖来自八个不同国家的2,843个视频的全面数据集，其中包括1,359个具有误导性缩略图的视频，这些视频总共获得了超过76亿次观看——为这一全球性问题提供了独特的跨文化视角。 我们的检测流程整合了视频到文本的描述、缩略图图像和字幕转录文本，以全面分析内容并标记误导性缩略图。 通过广泛的实验和提示工程，我们评估了最先进的LLMs的表现，包括GPT-4o、GPT-4o Mini、Claude 3.5 Sonnet和Gemini-1.5 Flash。 我们的研究结果表明LLMs在识别误导性缩略图方面的有效性，其中Claude 3.5 Sonnet表现稳定，某些情况下准确率达到93.8%，精确度超过92%，召回率超过94%。 我们讨论了这些发现对内容审核、用户体验以及部署此类系统时的伦理考虑的影响。 我们的研究结果为更加透明、可信的视频平台以及全球观众更强的内容完整性铺平了道路。