{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T02:45:44Z","timestamp":1783737944593,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":38,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T00:00:00Z","timestamp":1745280000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["FT210100624,LP230200892,DE230101033,DP240101108,DP240101814"],"award-info":[{"award-number":["FT210100624,LP230200892,DE230101033,DP240101108,DP240101814"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,4,28]]},"DOI":"10.1145\/3696410.3714610","type":"proceedings-article","created":{"date-parts":[[2025,5,5]],"date-time":"2025-05-05T16:42:02Z","timestamp":1746463322000},"page":"3618-3627","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Epidemiology-informed Network for Robust Rumor Detection"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9821-2276","authenticated-orcid":false,"given":"Wei","family":"Jiang","sequence":"first","affiliation":[{"name":"The University of Queensland, Brisbane, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7269-146X","authenticated-orcid":false,"given":"Tong","family":"Chen","sequence":"additional","affiliation":[{"name":"The University of Queensland, Brisbane, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1146-8925","authenticated-orcid":false,"given":"Xinyi","family":"Gao","sequence":"additional","affiliation":[{"name":"The University of Queensland, Brisbane, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7532-5550","authenticated-orcid":false,"given":"Wentao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8262-8883","authenticated-orcid":false,"given":"Lizhen","family":"Cui","sequence":"additional","affiliation":[{"name":"Shandong University, Jinan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1395-261X","authenticated-orcid":false,"given":"Hongzhi","family":"Yin","sequence":"additional","affiliation":[{"name":"The University of Queensland, Brisbane, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,4,22]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-019-01527-4"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5393"},{"key":"e_1_3_2_1_3_1","volume-title":"Kagnns: Kolmogorov-arnold networks meet graph learning. arXiv preprint arXiv:2406.18380","author":"Bresson Roman","year":"2024","unstructured":"Roman Bresson, Giannis Nikolentzos, George Panagopoulos, Michail Chatzianastasis, Jun Pang, and Michalis Vazirgiannis. 2024. Kagnns: Kolmogorov-arnold networks meet graph learning. arXiv preprint arXiv:2406.18380 (2024)."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.epidem.2023.100672"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-023-01864-z"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1140\/epjb\/e2012-30483-5"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i1.27757"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-016-0366-5"},{"key":"e_1_3_2_1_9_1","volume-title":"Spatio-Temporal Field Neural Networks for Air Quality Inference. arXiv preprint arXiv:2403.02354","author":"Feng Yutong","year":"2024","unstructured":"Yutong Feng, Qiongyan Wang, Yutong Xia, Junlin Huang, Siru Zhong, Kun Wang, Shifen Cheng, and Yuxuan Liang. 2024. Spatio-Temporal Field Neural Networks for Air Quality Inference. arXiv preprint arXiv:2403.02354 (2024)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271709"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i12.26664"},{"key":"e_1_3_2_1_12_1","volume-title":"AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Prediction. arXiv preprint arXiv:2402.03784","author":"Hettige Kethmi Hirushini","year":"2024","unstructured":"Kethmi Hirushini Hettige, Jiahao Ji, Shili Xiang, Cheng Long, Gao Cong, and Jingyuan Wang. 2024. AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Prediction. arXiv preprint arXiv:2402.03784 (2024)."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2018.02.032"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611976700.69"},{"key":"e_1_3_2_1_15_1","volume-title":"Physics-guided Active Sample Reweighting for Urban Flow Prediction. arXiv preprint arXiv:2407.13605","author":"Jiang Wei","year":"2024","unstructured":"Wei Jiang, Tong Chen, Guanhua Ye, Wentao Zhang, Lizhen Cui, Zi Huang, and Hongzhi Yin. 2024. Physics-guided Active Sample Reweighting for Urban Flow Prediction. arXiv preprint arXiv:2407.13605 (2024)."},{"key":"e_1_3_2_1_16_1","volume-title":"Proceedings of the AAAI conference on artificial intelligence","volume":"34","author":"Serena Khoo Ling Min","year":"2020","unstructured":"Ling Min Serena Khoo, Hai Leong Chieu, Zhong Qian, and Jing Jiang. 2020. Interpretable rumor detection in microblogs by attending to user interactions. In Proceedings of the AAAI conference on artificial intelligence, Vol. 34. 8783--8790."},{"key":"e_1_3_2_1_17_1","volume-title":"Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2013.61"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3649462"},{"key":"e_1_3_2_1_20_1","volume-title":"Can Large Language Models Detect Rumors on Social Media? arXiv preprint arXiv:2402.03916","author":"Liu Qiang","year":"2024","unstructured":"Qiang Liu, Xiang Tao, Junfei Wu, Shu Wu, and Liang Wang. 2024. Can Large Language Models Detect Rumors on Social Media? arXiv preprint arXiv:2402.03916 (2024)."},{"key":"e_1_3_2_1_21_1","unstructured":"Jing Ma Wei Gao Prasenjit Mitra Sejeong Kwon Bernard J Jansen Kam-Fai Wong and Meeyoung Cha. 2016. Detecting rumors from microblogs with recurrent neural networks. (2016)."},{"key":"e_1_3_2_1_22_1","volume-title":"Rumor detection on twitter with tree-structured recursive neural networks","author":"Ma Jing","unstructured":"Jing Ma, Wei Gao, and Kam-Fai Wong. 2018. Rumor detection on twitter with tree-structured recursive neural networks. Association for Computational Linguistics."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-017-0462-9"},{"key":"e_1_3_2_1_24_1","volume-title":"Twenty-sixth International Joint Conference on Artificial Intelligence.","author":"Nguyen Thanh Tam","year":"2017","unstructured":"Thanh Tam Nguyen, Chi Thang Duong, Matthias Weidlich, Hongzhi Yin, and Quoc Viet Hung Nguyen. 2017b. Retaining data from streams of social platforms with minimal regret. In Twenty-sixth International Joint Conference on Artificial Intelligence."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00291"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i12.26690"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i4.20385"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3511999"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3235312"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.14778\/3329772.3329778"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645478"},{"key":"e_1_3_2_1_32_1","volume-title":"Cassidy Hardin, Surya Bhupatiraju, L\u00e9onard Hussenot, Thomas Mesnard, Bobak Shahriari, Alexandre Ram\u00e9, et al.","author":"Team Gemma","year":"2024","unstructured":"Gemma Team, Morgane Riviere, Shreya Pathak, Pier Giuseppe Sessa, Cassidy Hardin, Surya Bhupatiraju, L\u00e9onard Hussenot, Thomas Mesnard, Bobak Shahriari, Alexandre Ram\u00e9, et al. 2024. Gemma 2: Improving open language models at a practical size. arXiv preprint arXiv:2408.00118 (2024)."},{"key":"e_1_3_2_1_33_1","volume-title":"Proceedings of the 2019 IEEE\/ACM international conference on advances in social networks analysis and mining. 113--120","author":"Ben Veyseh Amir Pouran","year":"2019","unstructured":"Amir Pouran Ben Veyseh, My T Thai, Thien Huu Nguyen, and Dejing Dou. 2019. Rumor detection in social networks via deep contextual modeling. In Proceedings of the 2019 IEEE\/ACM international conference on advances in social networks analysis and mining. 113--120."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0028873"},{"key":"e_1_3_2_1_35_1","volume-title":"How powerful are graph neural networks? arXiv preprint arXiv:1810.00826","author":"Xu Keyulu","year":"2018","unstructured":"Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2018. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018)."},{"key":"e_1_3_2_1_36_1","volume-title":"Graph contrastive learning with augmentations. Advances in neural information processing systems","author":"You Yuning","year":"2020","unstructured":"Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020. Graph contrastive learning with augmentations. Advances in neural information processing systems, Vol. 33 (2020), 5812--5823."},{"key":"e_1_3_2_1_37_1","volume-title":"SIR rumor spreading model in the new media age. Physica A: Statistical Mechanics and its Applications","author":"Zhao Laijun","year":"2013","unstructured":"Laijun Zhao, Hongxin Cui, Xiaoyan Qiu, Xiaoli Wang, and Jiajia Wang. 2013. SIR rumor spreading model in the new media age. Physica A: Statistical Mechanics and its Applications, Vol. 392, 4 (2013), 995--1003."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-67217-5_8"}],"event":{"name":"WWW '25: The ACM Web Conference 2025","location":"Sydney NSW Australia","acronym":"WWW '25","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the ACM on Web Conference 2025"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3696410.3714610","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3696410.3714610","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:34Z","timestamp":1750295914000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3696410.3714610"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,22]]},"references-count":38,"alternative-id":["10.1145\/3696410.3714610","10.1145\/3696410"],"URL":"https:\/\/doi.org\/10.1145\/3696410.3714610","relation":{},"subject":[],"published":{"date-parts":[[2025,4,22]]},"assertion":[{"value":"2025-04-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}