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Third International Workshop on Multimodal Content Analysis for Social Good

dc.contributor.authorNaseem, Usmanen
dc.contributor.authorThapa, Surendrabikramen
dc.contributor.authorLee, Royen
dc.contributor.authorNasim, Mehwishen
dc.date.accessioned2025-08-12T13:05:13Zen
dc.date.available2025-08-12T13:05:13Zen
dc.date.issued2025-05-08en
dc.date.updated2025-08-01T07:50:01Zen
dc.description.abstractThe third edition of the International Workshop on Multimedia Content Analysis for Social Good (MM4SG 2025) was held alongside the prestigious Web Conference 2025. This workshop aimed to tackle the critical challenge of analyzing and moderating multimodal content across digital platforms. In today’s era, where diverse forms of multimodal data—including memes, text-embedded images, and fabricated content—can rapidly shape public opinion and influence societal narratives, the demand for sophisticated and ethical content moderation strategies has become increasingly urgent. MM4SG 2025 provided a unique forum for interdisciplinary collaboration, bringing together researchers and practitioners from natural language processing, machine learning, computational social science, and ethics to address these pressing concerns. This paper highlights the key themes, discussions, and contributions of the third edition of the MM4SG workshop, with a particular focus on the intersection of computational linguistics and multimodal content analysis. It also explores future directions for the workshop, including expanding its scope and impact in subsequent editions.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1145/3701716.3718378en
dc.identifier.urihttps://hdl.handle.net/10919/137461en
dc.language.isoenen
dc.publisherACMen
dc.rightsIn Copyright (InC)en
dc.rights.holderThe author(s)en
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.titleThird International Workshop on Multimodal Content Analysis for Social Gooden
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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