{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:56:06Z","timestamp":1773802566855,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"19","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>With the explosive growth of multimodal data streams on social media, the timely detection of emerging social events has become increasingly important. \nAs a result, Multimodal Social Event Detection in open-world settings is receiving growing attention.\nHowever, most existing methods face two major limitations:\n(1) They overlook the dynamic nature of open-world social media data and fail to design dedicated incremental learning frameworks.\n(2) They ignore the impact of noise in streaming data, leading to performance degradation over long-term detection.\nTo overcome these limitations, we propose SeInEvent (**S**tructural **E**ntropy Guided **In**cremental Learning for Open-World Multimodal Social **Event** Detection).\nOur innovations are as follows:\n**First**, considering data dynamics, we design a self-supervised alternating incremental contrastive learning mechanism.\nThrough knowledge distillation, historical event clusters were reviewed and consolidated, and contrastive learning was combined to absorb knowledge of unknown events, ultimately achieving incremental learning without labels.\n**Second**, addressing the impact of noise, we propose a Pointwise Structural Entropy-based noise filter, which quantifies each sample\u2019s informational contribution to the event clustering structure. \nIt enables automatic removal of noisy data and supports robust long-term detection.\nExtensive experiments on two public datasets demonstrate that SeInEvent achieves superior performance.<\/jats:p>","DOI":"10.1609\/aaai.v40i19.38647","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:45:44Z","timestamp":1773794744000},"page":"16119-16127","source":"Crossref","is-referenced-by-count":0,"title":["Structural Entropy Guided Incremental Learning for Open-World Multimodal Social Event Detection"],"prefix":"10.1609","volume":"40","author":[{"given":"Zhiwei","family":"Yang","sequence":"first","affiliation":[]},{"given":"Haimei","family":"Qin","sequence":"additional","affiliation":[]},{"given":"Xiaoyan","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Hao","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Li","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Zhiqin","family":"Yang","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/38647\/42609","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/38647\/42609","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:45:44Z","timestamp":1773794744000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/38647"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i19.38647","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}