MM-Soc: Benchmarking Multimodal Large Language Models in Social Media Platforms

arXiv:2402.14154v1 Announce Type: cross
Abstract: 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. …

abstract arxiv benchmarking cs.cl cs.cv cs.cy emotions images information interactions language language models large language large language models machines making media mllms multimodal platforms soc social social media social media platforms solution spaces text the information type videos