{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T22:51:09Z","timestamp":1783983069632,"version":"3.55.0"},"reference-count":106,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T00:00:00Z","timestamp":1744848000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2025,6,30]]},"abstract":"<jats:p>\n            Recent research has shed light on the capabilities of Large Multimodal Models (LMMs) across various general vision and language tasks. The performance of LMMs in specialized domains, such as social media, which integrates text, images, videos, and sometimes audio, remains an area of active interest. Effective analysis of such content requires models to interpret the complex interactions between different communication modalities and their influence on the conveyed message. This article explores GPT-4V(ision)\u2019s performance in social multimedia analysis. We evaluate GPT-4V across five representative tasks: sentiment analysis, hate speech detection, fake news identification, demographic inference, and political ideology detection. Our approach includes a preliminary quantitative analysis for each task using existing benchmark datasets, followed by a review of the results and a selection of qualitative samples to demonstrate GPT-4V\u2019s performance in multimodal social media content analysis. GPT-4V shows effectiveness in these tasks, exhibiting capabilities like joint image\u2013text understanding, contextual and cultural awareness, and commonsense knowledge application. However, challenges persist, including struggles with multilingual social multimedia comprehension and difficulty in adapting to the latest social media trends. It also sometimes generates incorrect information about evolving knowledge of celebrities and politicians. This preliminary study aims to inform further research across disciplines, particularly in computational social science and social media studies. The findings highlight the potential of LMMs to enhance our understanding of social media content and its users through multimodal analysis. All images and prompts used in this study will be available at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/VIStA-H\/GPT-4V%5fSocial%5fMedia\">https:\/\/github.com\/VIStA-H\/GPT-4V_Social_Media<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3709005","type":"journal-article","created":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T15:56:07Z","timestamp":1734623767000},"page":"1-54","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["GPT-4V(ision) as A Social Media Analysis Engine"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3876-0094","authenticated-orcid":false,"given":"Hanjia","family":"Lyu","sequence":"first","affiliation":[{"name":"University of Rochester, Rochester, New York, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0081-4106","authenticated-orcid":false,"given":"Jinfa","family":"Huang","sequence":"additional","affiliation":[{"name":"University of Rochester, Rochester, New York, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6959-165X","authenticated-orcid":false,"given":"Daoan","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Rochester, Rochester, New York, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1386-859X","authenticated-orcid":false,"given":"Yongsheng","family":"Yu","sequence":"additional","affiliation":[{"name":"University of Rochester, Rochester, New York, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9255-8164","authenticated-orcid":false,"given":"Xinyi","family":"Mou","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6439-2857","authenticated-orcid":false,"given":"Jinsheng","family":"Pan","sequence":"additional","affiliation":[{"name":"University of Rochester, Rochester, New York, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5808-0889","authenticated-orcid":false,"given":"Zhengyuan","family":"Yang","sequence":"additional","affiliation":[{"name":"University of Rochester, Rochester, New York, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3789-8507","authenticated-orcid":false,"given":"Zhongyu","family":"Wei","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4516-9729","authenticated-orcid":false,"given":"Jiebo","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Rochester, Rochester, New York, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,4,17]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-66840-2_109"},{"key":"e_1_3_1_3_2","first-page":"635","volume-title":"Proceedings of the 11th International Conference on Language Resources and Evaluation (LREC \u201918)","author":"Attia Mohammed","year":"2018","unstructured":"Mohammed Attia, Younes Samih, Ali Elkahky, and Laura Kallmeyer. 2018. 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Performance of multimodal GPT-4V on USMLE with image: Potential for imaging diagnostic support with explanations. medRxiv. 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Lost in translation: When GPT-4V(ision) can\u2019t see eye to eye with text. A vision-language-consistency analysis of VLLMs and beyond. arXiv:2310.12520. Retrieved from https:\/\/arxiv.org\/abs\/2310.12520"},{"key":"e_1_3_1_103_2","unstructured":"Xinlu Zhang Yujie Lu Weizhi Wang An Yan Jun Yan Lianke Qin Heng Wang Xifeng Yan William Yang Wang and Linda Ruth Petzold. 2023. GPT-4V(ision) as a generalist evaluator for vision-language tasks. arXiv:2311.01361. Retrieved from https:\/\/arxiv.org\/abs\/2311.01361"},{"key":"e_1_3_1_104_2","doi-asserted-by":"publisher","DOI":"10.1109\/BigData52589.2021.9671533"},{"key":"e_1_3_1_105_2","doi-asserted-by":"publisher","DOI":"10.23919\/JSC.2021.0010"},{"key":"e_1_3_1_106_2","unstructured":"Peilin Zhou Meng Cao You-Liang Huang Qichen Ye Peiyan Zhang Junling Liu Yueqi Xie Yining Hua and Jaeboum Kim. 2023. Exploring recommendation capabilities of GPT-4V (ision): A preliminary case study. arXiv:2311.04199. 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