{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:10:07Z","timestamp":1765545007029,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":49,"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":[{"name":"Advanced Strategic Capabilities Accelerator (ASCA)"},{"name":"Defence Innovation Network"},{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100000969","name":"Australian Academy of Science","doi-asserted-by":"publisher","id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100000969","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100008812","name":"Defence Science and Technology Group","doi-asserted-by":"publisher","id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100008812","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Australian Department of Home Affairs"},{"name":"National Science Centre, Poland","award":["2021\/41\/B\/HS6\/02798"],"award-info":[{"award-number":["2021\/41\/B\/HS6\/02798"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,4,28]]},"DOI":"10.1145\/3696410.3714527","type":"proceedings-article","created":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T22:47:11Z","timestamp":1745362031000},"page":"5244-5254","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Before It's Too Late: A State Space Model for the Early Prediction of Misinformation and Disinformation Engagement"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6889-2178","authenticated-orcid":false,"given":"Lin","family":"Tian","sequence":"first","affiliation":[{"name":"University of Technology Sydney, Sydney, NSW, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8154-6747","authenticated-orcid":false,"given":"Emily","family":"Booth","sequence":"additional","affiliation":[{"name":"University of Technology Sydney, Sydney, NSW, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5971-1921","authenticated-orcid":false,"given":"Francesco","family":"Bailo","sequence":"additional","affiliation":[{"name":"The University of Sydney, Sydney, NSW, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8979-6505","authenticated-orcid":false,"given":"Julian","family":"Droogan","sequence":"additional","affiliation":[{"name":"Macquarie University, Sydney, NSW, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0381-669X","authenticated-orcid":false,"given":"Marian-Andrei","family":"Rizoiu","sequence":"additional","affiliation":[{"name":"University of Technology Sydney, Sydney, NSW, Australia"}]}],"member":"320","published-online":{"date-parts":[[2025,4,22]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"[n. d.]. CrowdTangle. https:\/\/www.crowdtangle.com\/"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/K19-1096"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Sidney Black Stella Biderman Eric Hallahan Quentin Gregory Anthony Leo Gao Laurence Golding Horace He Connor Leahy Kyle McDonell Jason Phang Michael Martin Pieler USVSN Sai Prashanth Shivanshu Purohit Laria Reynolds Jonathan Tow Ben Wang and Samuel Weinbach. 2022. GPT-NeoX-20B: An Open-Source Autoregressive Language Model. In Challenges & Perspectives in Creating Large Language Models.","DOI":"10.18653\/v1\/2022.bigscience-1.9"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1609\/icwsm.v18i1.31306"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","unstructured":"Pio Calderon and Marian-Andrei Rizoiu. 2024. What Drives Online Popularity: Author Content or Sharers? Estimating Spread Dynamics with Bayesian Mixture Hawkes. 142--160. https:\/\/doi.org\/10.1007\/978--3-031--70362--1_9","DOI":"10.1007\/978--3-031--70362--1_9"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2024.3486117"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132973"},{"key":"e_1_3_2_1_8_1","volume-title":"Nathanael Christian Yoder, and Tomas Pfister","author":"Chen Si-An","year":"2023","unstructured":"Si-An Chen, Chun-Liang Li, Sercan O Arik, Nathanael Christian Yoder, and Tomas Pfister. 2023. TSMixer: An All-MLP Architecture for Time Series Forecast-ing. Transactions on Machine Learning Research (2023)."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1137\/070710111"},{"key":"e_1_3_2_1_10_1","first-page":"16344","article-title":"Flashattention: Fast and Memory-Efficient Exact Attention with IO-Awareness","volume":"35","author":"Dao Tri","year":"2022","unstructured":"Tri Dao, Dan Fu, Stefano Ermon, Atri Rudra, and Christopher R\u00e9. 2022. Flashattention: Fast and Memory-Efficient Exact Attention with IO-Awareness. Advances in Neural Information Processing Systems 35 (2022), 16344--16359.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_11_1","volume-title":"Proceedings of the 41st International Conference on Machine Learning","volume":"235","author":"Dao Tri","year":"2024","unstructured":"Tri Dao and Albert Gu. 2024. Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality. In Proceedings of the 41st International Conference on Machine Learning, Vol. 235. 10041--10071."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3343031.3356062"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/0047-2484(92)90081-J"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2017.12.012"},{"key":"e_1_3_2_1_15_1","volume-title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces. In Conference on Language Modeling.","author":"Gu Albert","year":"2024","unstructured":"Albert Gu and Tri Dao. 2024. Mamba: Linear-Time Sequence Modeling with Selective State Spaces. In Conference on Language Modeling."},{"key":"e_1_3_2_1_16_1","volume-title":"Efficiently Modeling Long Sequences with Structured State Spaces. In International Conference on Learning Representations.","author":"Gu Albert","year":"2022","unstructured":"Albert Gu, Karan Goel, and Christopher Re. 2022. Efficiently Modeling Long Sequences with Structured State Spaces. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_17_1","first-page":"572","article-title":"Combining Recurrent, Convolutional, and Continuoustime Models with Linear State Space Layers","volume":"34","author":"Gu Albert","year":"2021","unstructured":"Albert Gu, Isys Johnson, Karan Goel, Khaled Saab, Tri Dao, Atri Rudra, and Christopher R\u00e9. 2021. Combining Recurrent, Convolutional, and Continuoustime Models with Linear State Space Layers. Advances in Neural Information Processing Systems 34 (2021), 572--585.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i9.16936"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394231.3397889"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1609\/icwsm.v16i1.19312"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583481"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441708"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3184558.3186972"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411861"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371821"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"crossref","unstructured":"David MJ Lazer Matthew A Baum Yochai Benkler Adam J Berinsky Kelly M Greenhill Filippo Menczer Miriam J Metzger Brendan Nyhan Gordon Pennycook David Rothschild et al. 2018. The science of fake news. Science 359 6380 (2018) 1094--1096.","DOI":"10.1126\/science.aao2998"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052643"},{"key":"e_1_3_2_1_28_1","volume-title":"Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting. Advances in Neural Information Processing Systems 32","author":"Li Shiyang","year":"2019","unstructured":"Shiyang Li, Xiaoyong Jin, Yao Xuan, Xiyou Zhou, Wenhu Chen, Yu-Xiang Wang, and Xifeng Yan. 2019. Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting. Advances in Neural Information Processing Systems 32 (2019)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2023\/247"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645529"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1080\/00107510500052444"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220077"},{"key":"e_1_3_2_1_33_1","volume-title":"Deep state space models for time series forecasting. Advances in neural information processing systems 31","author":"Rangapuram Syama Sundar","year":"2018","unstructured":"Syama Sundar Rangapuram, Matthias W Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, and Tim Januschowski. 2018. Deep state space models for time series forecasting. Advances in neural information processing systems 31 (2018)."},{"key":"e_1_3_2_1_34_1","first-page":"1","article-title":"Interval-censored Hawkes processes","volume":"23","author":"Rizoiu Marian-Andrei","year":"2022","unstructured":"Marian-Andrei Rizoiu, Alexander Soen, Shidi Li, Pio Calderon, Leanne J Dong, Aditya Krishna Menon, and Lexing Xie. 2022. Interval-censored Hawkes processes. Journal of Machine Learning Research 23, 338 (2022), 1--84.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052650"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1805871115"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-86486-6_37"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583417"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.naacl-main.364"},{"key":"e_1_3_2_1_40_1","volume-title":"Advances in Neural Information Processing Systems 30","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention Is All You Need. Advances in Neural Information Processing Systems 30 (2017)."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2017.57"},{"key":"e_1_3_2_1_42_1","volume-title":"Proceedings of the 26th International Joint Conference on Artificial Intelligence. 2985--2991","author":"Shen Huawei","year":"2017","unstructured":"YongqingWang, Huawei Shen, Shenghua Liu, Jinhua Gao, and Xueqi Cheng. 2017. Cascade dynamics modeling with attention-based recurrent neural network. In Proceedings of the 26th International Joint Conference on Artificial Intelligence. 2985--2991."},{"key":"e_1_3_2_1_43_1","first-page":"22419","article-title":"Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting","volume":"34","author":"Wu Haixu","year":"2021","unstructured":"Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long. 2021. Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting. Advances in Neural Information Processing Systems 34 (2021), 22419--22430.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1609\/icwsm.v12i1.15031"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3126475"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308560.3316495"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/597"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"e_1_3_2_1_49_1","volume-title":"International Conference on Machine Learning. PMLR, 11692--11702","author":"Zuo Simiao","year":"2020","unstructured":"Simiao Zuo, Haoming Jiang, Zichong Li, Tuo Zhao, and Hongyuan Zha. 2020. Transformer hawkes process. In International Conference on Machine Learning. PMLR, 11692--11702."}],"event":{"name":"WWW '25: The ACM Web Conference 2025","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"],"location":"Sydney NSW Australia","acronym":"WWW '25"},"container-title":["Proceedings of the ACM on Web Conference 2025"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3696410.3714527","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3696410.3714527","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:33Z","timestamp":1750295913000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3696410.3714527"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,22]]},"references-count":49,"alternative-id":["10.1145\/3696410.3714527","10.1145\/3696410"],"URL":"https:\/\/doi.org\/10.1145\/3696410.3714527","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"}}]}}