Recent research has offered insights into the extraordinary capabilities of
Large Multimodal Models (LMMs) in various general vision and language tasks.
There is growing interest in how LMMs perform in more specialized domains.
Social media content, inherently multimodal, blends text, images, videos, and
sometimes audio. Understanding social multimedia content remains a challenging
problem for contemporary machine learning frameworks. In this paper, we explore
GPT-4V(ision)'s capabilities for social multimedia analysis. We select five
representative tasks, including sentiment analysis, hate speech detection, fake
news identification, demographic inference, and political ideology detection,
to evaluate GPT-4V. Our investigation begins with a preliminary quantitative
analysis for each task using existing benchmark datasets, followed by a careful
review of the results and a selection of qualitative samples that illustrate
GPT-4V's potential in understanding multimodal social media content. GPT-4V
demonstrates remarkable efficacy in these tasks, showcasing strengths such as
joint understanding of image-text pairs, contextual and cultural awareness, and
extensive commonsense knowledge. Despite the overall impressive capacity of
GPT-4V in the social media domain, there remain notable challenges. GPT-4V
struggles with tasks involving multilingual social multimedia comprehension and
has difficulties in generalizing to the latest trends in social media.
Additionally, it exhibits a tendency to generate erroneous information in the
context of evolving celebrity and politician knowledge, reflecting the known
hallucination problem. The insights gleaned from our findings underscore a
promising future for LMMs in enhancing our comprehension of social media
content and its users through the analysis of multimodal information.

利用现有基准数据集对 GPT-4V 的五项任务进行定量分析，并选择有代表性的样本详细评估其在社交多媒体内容理解方面的潜力，结果显示 GPT-4V 在情感分析、仇恨言论检测、假新闻识别、人口推断和政治意识形态检测等任务上展现出显著的效果，并具备图像 - 文本配对的联合理解、文化和语境意识、以及广泛的常识知识，但在涉及多语言社交多媒体理解和对社交媒体最新趋势的泛化方面仍存在挑战，同时在涉及名人和政治家知识的持续发展背景下，会出现错误信息生成的倾向，这反映了已知的幻觉问题，研究结果表明，大型多模态模型在通过分析多模态信息来增进对社交媒体内容及其用户的理解方面具有巨大的潜力。