  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 these challenges, yet they struggle to accurately interpret human
emotions and complex content such as 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. Our code and data are available
at https://github.com/claws-lab/MMSoc.git.
