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Inf. Syst."],"published-print":{"date-parts":[[2025,1,31]]},"abstract":"<jats:p>\n            Social Event Detection (SED) aims to identify significant events from social streams, and has a wide application ranging from public opinion analysis to risk management. In recent years, Graph Neural Network (GNN) based solutions have achieved state-of-the-art performance. However, GNN-based methods often struggle with missing and noisy edges between messages, affecting the quality of learned message embedding. Moreover, these methods statically initialize node embedding before training, which, in turn, limits the ability to learn from message texts and relations simultaneously. In this article, we approach social event detection from a new perspective based on Pre-trained Language Models (PLMs), and present\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\mathrm{RPLM}_{SED}\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            (\n            <jats:bold>R<\/jats:bold>\n            elational prompt-based\n            <jats:bold>P<\/jats:bold>\n            re-trained\n            <jats:bold>L<\/jats:bold>\n            anguage\n            <jats:bold>M<\/jats:bold>\n            odels for\n            <jats:italic>S<\/jats:italic>\n            ocial\n            <jats:italic>E<\/jats:italic>\n            vent\n            <jats:italic>D<\/jats:italic>\n            etection). We first propose a new pairwise message modeling strategy to construct social messages into message pairs with multi-relational sequences. Secondly, a new multi-relational prompt-based pairwise message learning mechanism is proposed to learn more comprehensive message representation from message pairs with multi-relational prompts using PLMs. Thirdly, we design a new clustering constraint to optimize the encoding process by enhancing intra-cluster compactness and inter-cluster dispersion, making the message representation more distinguishable. We evaluate the\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\mathrm{RPLM}_{SED}\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            on three real-world datasets, demonstrating that the\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\mathrm{RPLM}_{SED}\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            model achieves state-of-the-art performance in offline, online, low-resource, and long-tail distribution scenarios for social event detection tasks.\n          <\/jats:p>","DOI":"10.1145\/3695869","type":"journal-article","created":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T14:22:10Z","timestamp":1726237330000},"page":"1-43","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Relational Prompt-Based Pre-Trained Language Models for Social Event Detection"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-1410-4096","authenticated-orcid":false,"given":"Pu","family":"Li","sequence":"first","affiliation":[{"name":"Kunming University of Science and Technology, Kunming, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2314-7867","authenticated-orcid":false,"given":"Xiaoyan","family":"Yu","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0458-5977","authenticated-orcid":false,"given":"Hao","family":"Peng","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6411-4734","authenticated-orcid":false,"given":"Yantuan","family":"Xian","sequence":"additional","affiliation":[{"name":"Kunming University of Science and Technology, Kunming, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0700-5152","authenticated-orcid":false,"given":"Linqin","family":"Wang","sequence":"additional","affiliation":[{"name":"Kunming University of Science and Technology, Kunming, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4562-2279","authenticated-orcid":false,"given":"Li","family":"Sun","sequence":"additional","affiliation":[{"name":"North China Electric Power University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7215-0040","authenticated-orcid":false,"given":"Jingyun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3491-5968","authenticated-orcid":false,"given":"Philip S.","family":"Yu","sequence":"additional","affiliation":[{"name":"University of Illinois at Chicago, Chicago, Illinois, USA"}]}],"member":"320","published-online":{"date-parts":[[2024,11,26]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1137\/1.9781611972825.54","volume-title":"Proceedings of the 2012 SIAM International Conference on Data Mining","author":"Aggarwal Charu C.","year":"2012","unstructured":"Charu C. 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A french corpus for event detection on twitter. In Proceedings of the 12th Language Resources and Evaluation Conference, 6220\u20136227."},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1145\/2505515.2505695"},{"key":"e_1_3_2_53_2","first-page":"1","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Mikolov Tomas","year":"2013","unstructured":"Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. In Proceedings of the International Conference on Learning Representations, 1\u201312."},{"key":"e_1_3_2_54_2","first-page":"2712","volume-title":"Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP \u201920)","author":"Mohiuddin Muhammad Tasnim","year":"2020","unstructured":"Muhammad Tasnim Mohiuddin, M. Saiful Bari, and Shafiq Joty. 2020. LNMap: Departures from isomorphic assumption in bilingual lexicon induction through non-linear mapping in latent space. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP \u201920). Association for Computational Linguistics, 2712\u20132723."},{"key":"e_1_3_2_55_2","first-page":"77","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop","author":"Morabia Keval","year":"2019","unstructured":"Keval Morabia, Neti Lalita Bhanu Murthy, Aruna Malapati, and Surender Samant. 2019. SEDTWik: Segmentation-based event detection from tweets using Wikipedia. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop. Association for Computational Linguistics, 77\u201385."},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfds.2017.11.002"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-017-0476-8"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.5555\/3367471.3367491"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186005"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447585"},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3144993"},{"key":"e_1_3_2_62_2","first-page":"1","volume-title":"Proceedings of the 2024 SIAM International Conference on Data Mining (SDM),","author":"Peng Kun","year":"2024","unstructured":"Kun Peng, Lei Jiang, Hao Peng, Rui Liu, Zhengtao Yu, Jiaqian Ren, Zhifeng Hao, and Philip S. Yu. 2024. Prompt based tri-channel graph convolution neural network for aspect sentiment triplet extraction. In Proceedings of the 2024 SIAM International Conference on Data Mining (SDM), 1\u20139."},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1162"},{"key":"e_1_3_2_64_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1250"},{"key":"e_1_3_2_65_2","unstructured":"Alec Radford Karthik Narasimhan Tim Salimans and Ilya Sutskever. 2018. Improving language understanding by generative pre-training. (2018) 1\u201312."},{"key":"e_1_3_2_66_2","first-page":"1696","volume-title":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","author":"Ren Jiaqian","year":"2022","unstructured":"Jiaqian Ren, Lei Jiang, Hao Peng, Yuwei Cao, Jia Wu, Philip S. Yu, and Lifang He. 2022. From known to unknown: quality-aware self-improving graph neural network for open set social event detection. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 1696\u20131705."},{"key":"e_1_3_2_67_2","first-page":"331","volume-title":"Proceedings of the IEEE International Conference on Web Services (ICWS \u201922)","author":"Ren Jiaqian","year":"2022","unstructured":"Jiaqian Ren, Lei Jiang, Hao Peng, Zhiwei Liu, Jia Wu, and Philip S. Yu. 2022. Evidential temporal-aware graph-based social event detection via dempster-shafer theory. In Proceedings of the IEEE International Conference on Web Services (ICWS \u201922). IEEE, 331\u2013336."},{"key":"e_1_3_2_68_2","doi-asserted-by":"publisher","DOI":"10.1145\/3689948"},{"issue":"6","key":"e_1_3_2_69_2","first-page":"2701","article-title":"Uncertainty-guided Boundary Learning for Imbalanced Social Event Detection","volume":"36","author":"Ren Jiaqian","year":"2023","unstructured":"Jiaqian Ren, Hao Peng, Lei Jiang, Zhiwei Liu, Jia Wu, Zhengtao Yu, and Philip S. Yu. 2023. 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Curran Associates, Inc., 28798\u201328810."},{"key":"e_1_3_2_84_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2017.2672672"},{"key":"e_1_3_2_85_2","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1145\/3097983.3098027","volume-title":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Zhang Chao","year":"2017","unstructured":"Chao Zhang, Liyuan Liu, Dongming Lei, Quan Yuan, Honglei Zhuang, Tim Hanratty, and Jiawei Han. 2017. Triovecevent: Embedding-based online local event detection in geo-tagged tweet streams. 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