Social media platforms are hubs for multimodal information exchange, encompassing text, images, and videos, making it challenging for machines to comprehend the information or emotions associated with interactions in online spaces. Multimodal Large Language Models (MLLMs) have emerged as a promising solution to address these challenges, yet struggle with accurately interpreting human emotions and complex contents like misinformation. This paper introduces MM-Soc, a comprehensive benchmark designed to evaluate MLLMs' understanding of multimodal social media content. MM-Soc compiles prominent multimodal datasets and incorporates a novel large-scale YouTube tagging dataset, targeting a range of tasks from misinformation detection, hate speech detection, and social context generation. Through our exhaustive evaluation on ten size-variants of four open-source MLLMs, we have identified significant performance disparities, highlighting the need for advancements in models' social understanding capabilities. Our analysis reveals that, in a zero-shot setting, various types of MLLMs generally exhibit difficulties in handling social media tasks. However, MLLMs demonstrate performance improvements post fine-tuning, suggesting potential pathways for improvement.

翻译：社交媒体平台是多模态信息交流的中心，涵盖文本、图像和视频，这使得机器难以理解在线空间中与交互相关的信息或情感。多模态大语言模型（MLLMs）已成为应对这些挑战的有前景解决方案，但在准确解读人类情感及虚假信息等复杂内容方面仍存在困难。本文介绍MM-Soc，一个旨在评估MLLMs对多模态社交媒体内容理解能力的综合性基准。MM-Soc整合了主流多模态数据集，并引入了一个新颖的大规模YouTube标注数据集，针对从虚假信息检测、仇恨言论检测到社交语境生成等一系列任务。通过对四种开源MLLMs的十个规模变体进行详尽评估，我们发现了显著的性能差异，凸显了提升模型社会理解能力的必要性。我们的分析表明，在零样本设置下，各种类型的MLLMs在处理社交媒体任务时普遍存在困难。然而，微调后MLLMs显示出性能提升，这暗示了潜在的改进路径。