{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T08:12:43Z","timestamp":1778832763401,"version":"3.51.4"},"reference-count":44,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T00:00:00Z","timestamp":1778803200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"University of Amsterdam and a.s.r.","award":["PPS23-3-03434189"],"award-info":[{"award-number":["PPS23-3-03434189"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2026,5,31]]},"abstract":"<jats:p>\n                    In this work, we introduce a multimodal analysis pipeline that leverages large foundation models in vision and language to analyze corporate social media content, with a focus on sustainability-related communication. Addressing the challenges of evolving, multimodal, and often ambiguous corporate messaging on platforms such as\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\mathbb{X}\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    (formerly known as Twitter), we employ an ensemble of large language models (LLMs) to annotate a large corpus of corporate tweets on their topical alignment with the 17 Sustainable Development Goals (SDGs). This approach avoids the need for costly, task-specific annotations and explores the potential of such models as\n                    <jats:italic toggle=\"yes\">ad hoc<\/jats:italic>\n                    annotators for social media data that can efficiently capture both explicit and implicit references to sustainability themes in a scalable manner. Complementing this textual analysis, we utilize vision-language models (VLMs), within a visual understanding framework that uses semantic clusters to uncover patterns in visual sustainability communication. This approach reveals sectoral differences in SDG engagement, temporal trends, and associations between corporate messaging, environmental, social, governance (ESG) risks, and consumer engagement. Our methods, built upon automatic label generation and semantic visual clustering, are broadly applicable to other domains and offer a flexible framework for large-scale social media analysis.\n                  <\/jats:p>","DOI":"10.1145\/3809492","type":"journal-article","created":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T14:37:27Z","timestamp":1776436647000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Analyzing Sustainability Messaging in Large-Scale Corporate Social Media"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0285-1303","authenticated-orcid":false,"given":"Ujjwal","family":"Sharma","sequence":"first","affiliation":[{"name":"University of Amsterdam, Amsterdam, Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1904-8736","authenticated-orcid":false,"given":"Stevan","family":"Rudinac","sequence":"additional","affiliation":[{"name":"University of Amsterdam, Amsterdam, Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4106-9111","authenticated-orcid":false,"given":"Ana","family":"Mi\u0107kovi\u0107","sequence":"additional","affiliation":[{"name":"University of Amsterdam, Amsterdam, Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7768-6539","authenticated-orcid":false,"given":"Willemijn","family":"van Dolen","sequence":"additional","affiliation":[{"name":"University of Amsterdam, Amsterdam, Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4097-4136","authenticated-orcid":false,"given":"Marcel","family":"Worring","sequence":"additional","affiliation":[{"name":"University of Amsterdam, Amsterdam, Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,5,15]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"Marah Abdin Jyoti Aneja Harkirat Behl S\u00e9bastien Bubeck Ronen Eldan Suriya Gunasekar Michael Harrison Russell J. Hewett Mojan Javaheripi Piero Kauffmann et al. 2024. Phi-4 technical report. arXiv:2412.08905. Retrieved from https:\/\/arxiv.org\/abs\/2412.08905"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.52202\/068431-1723"},{"key":"e_1_3_1_4_2","unstructured":"Shuai Bai Keqin Chen Xuejing Liu Jialin Wang Wenbin Ge Sibo Song Kai Dang Peng Wang Shijie Wang Jun Tang et al. 2025. Qwen2.5-vl technical report. arXiv:2502.13923. Retrieved from https:\/\/arxiv.org\/abs\/2502.13923"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1995.tb02031.x"},{"key":"e_1_3_1_6_2","first-page":"1877","article-title":"Language models are few-shot learners","volume":"33","author":"Brown Tom","year":"2020","unstructured":"Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D. Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. In Advances in Neural Information Processing Systems, Vol. 33, 1877\u20131901.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"e_1_3_1_8_2","unstructured":"Xi Chen Xiao Wang Soravit Changpinyo A. J. Piergiovanni Piotr Padlewski Daniel Salz Sebastian Goodman Adam Grycner Basil Mustafa Lucas Beyer et al. 2022. PaLI: A jointly-scaled multilingual language-image model. arXiv:2209.06794. Retrieved from https:\/\/arxiv.org\/abs\/2209.06794"},{"key":"e_1_3_1_9_2","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1609\/icwsm.v5i1.14126","article-title":"Political polarization on Twitter","volume":"5","author":"Conover Michael","year":"2011","unstructured":"Michael Conover, Jacob Ratkiewicz, Matthew Francisco, Bruno Gon\u00e7alves, Filippo Menczer, and Alessandro Flammini. 2011. Political polarization on Twitter. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 5, 89\u201396.","journal-title":"Proceedings of the International AAAI Conference on Web and Social Media"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/2964284.2964312"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/2185520.2185597"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462990"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3463001"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","unstructured":"Gabriel Ilharco Mitchell Wortsman Ross Wightman Cade Gordon Nicholas Carlini Rohan Taori Achal Dave Vaishaal Shankar Hongseok Namkoong John Miller et al. 2021. OpenCLIP. Zenodo. DOI: 10.5281\/zenodo.5143773","DOI":"10.5281\/zenodo.5143773"},{"key":"e_1_3_1_15_2","first-page":"4904","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Jia Chao","year":"2021","unstructured":"Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. 2021. Scaling up visual and vision-language representation learning with noisy text supervision. In Proceedings of the International Conference on Machine Learning. PMLR, 4904\u20134916."},{"key":"e_1_3_1_16_2","unstructured":"Evan Klinger and David Starkweather. 2021. phash.org: Home of phash the open source perceptual hash library. Retrieved from https:\/\/phash.org\/"},{"key":"e_1_3_1_17_2","first-page":"19730","volume-title":"Proceedings of International Conference on Machine Learning","author":"Li Junnan","year":"2023","unstructured":"Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. 2023. BLIP-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In Proceedings of International Conference on Machine Learning. PMLR, 19730\u201319742."},{"key":"e_1_3_1_18_2","unstructured":"Junyou Li Qin Zhang Yangbin Yu Q. I. A. N. G. Fu and Deheng Ye. 2024. More Agents Is All You Need. Transactions on Machine Learning Research. Retrieved from https:\/\/openreview.net\/forum?id=bgzUSZ8aeg"},{"key":"e_1_3_1_19_2","first-page":"34892","article-title":"Visual instruction tuning","volume":"36","author":"Liu Haotian","year":"2023","unstructured":"Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. 2023. Visual instruction tuning. In Advances in Neural Information Processing Systems, Vol. 36, 34892\u201334916.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_20_2","doi-asserted-by":"crossref","unstructured":"Gowreesh Mago Pascal Mettes and Stevan Rudinac. 2025. Looking beyond the obvious: A survey on abstract concept recognition for video understanding. arXiv:2508.20765. Retrieved from https:\/\/arxiv.org\/abs\/2508.20765","DOI":"10.1007\/s11263-026-02784-5"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177730491"},{"key":"e_1_3_1_22_2","unstructured":"Morgan Stanley Capital International (MSCI) and Standard & Poor\u2019s. 2024. Global Industry Classification Standard. Retrieved November 6 2024 from https:\/\/www.msci.com\/oursolutions\/indexes\/gics"},{"key":"e_1_3_1_23_2","unstructured":"United Nations. 2015. United Nations Transforming our World: The 2030 Agenda for Sustainable Development. Technical Report A\/res\/70\/1. Retrieved from https:\/\/sdgs.un.org\/sites\/default\/files\/publications\/21252030%20Agenda%20for%20Sustainable%20Development%20web.pdf"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1609\/icwsm.v5i1.14137"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/2531602.2531613"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/2631775.2631799"},{"key":"e_1_3_1_27_2","first-page":"8748","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Radford Alec","year":"2021","unstructured":"Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021. Learning transferable visual models from natural language supervision. In Proceedings of the International Conference on Machine Learning. PMLR, 8748\u20138763."},{"key":"e_1_3_1_28_2","first-page":"8821","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Ramesh Aditya","year":"2021","unstructured":"Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. 2021. Zero-shot text-to-image generation. In Proceedings of the International Conference on Machine Learning. PMLR, 8821\u20138831."},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1410"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/1963405.1963503"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2013.2261481"},{"key":"e_1_3_1_32_2","doi-asserted-by":"crossref","first-page":"1736","DOI":"10.1145\/3511808.3557279","volume-title":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","author":"Saeed Mohammed","year":"2022","unstructured":"Mohammed Saeed, Nicolas Traub, Maelle Nicolas, Gianluca Demartini, and Paolo Papotti. 2022. Crowdsourced fact-checking at Twitter: How does the crowd compare with experts? In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 1736\u20131746."},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591810"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1002\/j.1538-7305.1948.tb01338.x"},{"key":"e_1_3_1_35_2","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1007\/978-3-031-56435-2_8","volume-title":"Proceedings of the International Conference on Multimedia Modeling","author":"Sharma Ujjwal","year":"2024","unstructured":"Ujjwal Sharma, Stevan Rudinac, Joris Demmers, Willemijn van Dolen, and Marcel Worring. 2024. and GreenScreen: A multimodal dataset for detecting corporate greenwashing in the wild. In Proceedings of the International Conference on Multimedia Modeling. Springer, 96\u2013109."},{"key":"e_1_3_1_36_2","first-page":"8234","volume-title":"Proceedings of the Findings of the Association for Computational Linguistics (EMNLP \u201923)","author":"Si Chenglei","year":"2023","unstructured":"Chenglei Si, Weijia Shi, Chen Zhao, Luke Zettlemoyer, and Jordan Boyd-Graber. 2023. Getting MoRE out of mixture of language model reasoning experts. In Proceedings of the Findings of the Association for Computational Linguistics (EMNLP \u201923), 8234\u20138249."},{"key":"e_1_3_1_37_2","unstructured":"Geoffrey Supran and Cameron Hickey. 2022. Three Shades of Green(Washing): Content Analysis of Social Media Discourse by European Oil Car and Airline Companies. Retrieved from https:\/\/ati.io\/three-shades-of-greenwashing\/"},{"issue":"8","key":"e_1_3_1_38_2","doi-asserted-by":"crossref","first-page":"084019","DOI":"10.1088\/1748-9326\/aa815f","article-title":"Assessing ExxonMobil\u2019s climate change communications (1977\u20132014)","volume":"12","author":"Supran Geoffrey","year":"2017","unstructured":"Geoffrey Supran and Naomi Oreskes. 2017. Assessing ExxonMobil\u2019s climate change communications (1977\u20132014). Environmental Research Letters 12, 8 (2017), 084019.","journal-title":"Environmental Research Letters"},{"key":"e_1_3_1_39_2","unstructured":"Sustainalytics. 2024. ESG Risk Ratings. Retrieved May 6 2025 from https:\/\/www.sustainalytics.com\/"},{"key":"e_1_3_1_40_2","unstructured":"Mistral AI Team. 2024. Mistral NeMo. Retrieved May 6 2025 from https:\/\/mistral.ai\/news\/mistral-nemo"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1609\/icwsm.v11i1.14871"},{"issue":"1","key":"e_1_3_1_42_2","doi-asserted-by":"crossref","first-page":"101045","DOI":"10.1016\/j.bar.2021.101045","article-title":"Stock market reactions to adverse ESG disclosure via media channels","volume":"54","author":"Wong Jin Boon","year":"2022","unstructured":"Jin Boon Wong and Qin Zhang. 2022. Stock market reactions to adverse ESG disclosure via media channels. The British Accounting Review 54, 1 (2022), 101045.","journal-title":"The British Accounting Review"},{"key":"e_1_3_1_43_2","unstructured":"An Yang Baosong Yang Beichen Zhang Binyuan Hui Bo Zheng Bowen Yu Chengyuan Li Dayiheng Liu Fei Huang Haoran Wei et al. 2024. Qwen2. 5 technical report. arXiv:2412.15115. Retrieved from https:\/\/arxiv.org\/abs\/2412.15115"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-96530-3"},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1145\/2978656"}],"container-title":["ACM Transactions on Multimedia Computing, Communications, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3809492","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T07:51:51Z","timestamp":1778831511000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3809492"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,15]]},"references-count":44,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,5,31]]}},"alternative-id":["10.1145\/3809492"],"URL":"https:\/\/doi.org\/10.1145\/3809492","relation":{},"ISSN":["1551-6857","1551-6865"],"issn-type":[{"value":"1551-6857","type":"print"},{"value":"1551-6865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,15]]},"assertion":[{"value":"2025-05-12","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-19","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-05-15","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}