{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T20:51:33Z","timestamp":1743022293344,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031496134"},{"type":"electronic","value":"9783031496141"}],"license":[{"start":{"date-parts":[[2023,12,9]],"date-time":"2023-12-09T00:00:00Z","timestamp":1702080000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,9]],"date-time":"2023-12-09T00:00:00Z","timestamp":1702080000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-49614-1_31","type":"book-chapter","created":{"date-parts":[[2023,12,8]],"date-time":"2023-12-08T14:02:45Z","timestamp":1702044165000},"page":"419-430","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Incorporating Neural Point Process-Based Temporal Feature for\u00a0Rumor Detection"],"prefix":"10.1007","author":[{"given":"Runzhe","family":"Li","sequence":"first","affiliation":[]},{"given":"Zhipeng","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Suixiang","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Wenguo","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,9]]},"reference":[{"key":"31_CR1","doi-asserted-by":"publisher","unstructured":"Castillo, C., Mendoza, M., Poblete, B.: Information credibility on twitter. In: Proceedings of the 20th International Conference on World Wide Web, pp. 675\u2013684. Association for Computing Machinery, Hyderabad (2011). https:\/\/doi.org\/10.1145\/1963405.1963500","DOI":"10.1145\/1963405.1963500"},{"key":"31_CR2","doi-asserted-by":"publisher","unstructured":"Ma, J., et al.: Detecting rumors from microblogs with recurrent neural networks. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, pp. 3818\u20133824. AAAI Press, New York (2016). https:\/\/doi.org\/10.5555\/3061053.3061153","DOI":"10.5555\/3061053.3061153"},{"key":"31_CR3","doi-asserted-by":"crossref","unstructured":"Bian, T., et al.: Rumor detection on social media with bi-directional graph convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence 34(01), pp. 549\u2013556 (2020)","DOI":"10.1609\/aaai.v34i01.5393"},{"key":"31_CR4","doi-asserted-by":"publisher","unstructured":"Lu, Y., Li, C.: GCAN: graph-aware co-attention networks for explainable fake news detection on social media. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 505\u2013514. Association for Computational Linguistics, Vancouver (2020). https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.48","DOI":"10.18653\/v1\/2020.acl-main.48"},{"key":"31_CR5","doi-asserted-by":"publisher","unstructured":"Ma, J., Gao, W., Wong, K.: Rumor detection on twitter with tree-structured recursive neural networks. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 1980\u20131989. Association for Computational Linguistics, Melbourne (2018). https:\/\/doi.org\/10.18653\/v1\/P18-1184","DOI":"10.18653\/v1\/P18-1184"},{"key":"31_CR6","doi-asserted-by":"publisher","first-page":"1146","DOI":"10.1126\/science.aap9559","volume":"359","author":"V Soroush","year":"2018","unstructured":"Soroush, V., Deb, R., Sinan, A.: The spread of true and false news online. Science 359, 1146\u20131151 (2018)","journal-title":"Science"},{"key":"31_CR7","doi-asserted-by":"publisher","unstructured":"Wu, K., Yang, S., Zhu, K.: False rumors detection on sina weibo by propagation structures. In: 2015 IEEE 31st International Conference on Data Engineering, pp. 651\u2013662. IEEE, Rio de Janeiro (2015). https:\/\/doi.org\/10.1109\/icde.2015.7113322","DOI":"10.1109\/icde.2015.7113322"},{"key":"31_CR8","unstructured":"David, V.: An Introduction to the Theory of Point Processes: Volume I: Elementary Theory and Methods. Springer, Heidelberg (2003)"},{"issue":"01","key":"31_CR9","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1093\/biomet\/58.1.83","volume":"58","author":"A Hawkes","year":"1971","unstructured":"Hawkes, A.: Spectra of some self-exciting and mutually exciting point processes. Biometrika 58(01), 83\u201390 (1971)","journal-title":"Biometrika"},{"key":"31_CR10","doi-asserted-by":"publisher","unstructured":"Naumzik, C., Feuerriegel, S.: Detecting false rumors from retweet dynamics on social media. In: Proceedings of the ACM Web Conference 2022, pp. 2798\u20132809. Association for Computing Machinery, Lyon (2022). https:\/\/doi.org\/10.1145\/3485447.3512000","DOI":"10.1145\/3485447.3512000"},{"key":"31_CR11","doi-asserted-by":"publisher","unstructured":"Zeng, F., Gao, W.: Early Rumor Detection Using Neural Hawkes Process with a New Benchmark Dataset. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 4105\u20134117. Association for Computational Linguistics, Seattle (2022). https:\/\/doi.org\/10.18653\/v1\/2022.naacl-main.302","DOI":"10.18653\/v1\/2022.naacl-main.302"},{"key":"31_CR12","doi-asserted-by":"publisher","unstructured":"Mei, H., Eisner, J.: The neural hawkes process: a neurally self-modulating multivariate point process. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6757\u20136767. Curran Associates Inc., California (2017). https:\/\/doi.org\/10.5555\/3295222.3295420","DOI":"10.5555\/3295222.3295420"},{"issue":"8","key":"31_CR13","doi-asserted-by":"publisher","first-page":"3035","DOI":"10.1109\/TKDE.2019.2961675","volume":"33","author":"C Song","year":"2021","unstructured":"Song, C., Yang, C., Chen, H., Tu, C., Liu, Z., Sun, M.: CED: credible early detection of social media rumors. IEEE Trans. Knowl. Data Eng. 33(8), 3035\u20133047 (2021)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"2","key":"31_CR14","first-page":"2941","volume":"82","author":"L Tan","year":"2022","unstructured":"Tan, L., Wang, G., Jia, F., Lian, X.: Research Status of Deep Learning Methods for Rumor Detection. Kluwer Academic Publishers 82(2), 2941\u20132982 (2022)","journal-title":"Kluwer Academic Publishers"},{"key":"31_CR15","doi-asserted-by":"publisher","unstructured":"Zhang, X., Cao, J., Li, X., Sheng, Q., Zhong, L., Shu, K.: Mining dual emotion for fake news detection. In: Proceedings of the Web Conference 2021, pp. 3465\u20133476. Association for Computing Machinery, Ljubljana (2021). https:\/\/doi.org\/10.1145\/3442381.3450004","DOI":"10.1145\/3442381.3450004"},{"key":"31_CR16","doi-asserted-by":"publisher","unstructured":"Zhao, Q., Erdogdu, M., He, H., Rajaraman, A., Leskovec, J.: SEISMIC: a self-exciting point process model for predicting tweet popularity. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1513\u20131522. Association for Computing Machinery, Sydney (2015). https:\/\/doi.org\/10.1145\/2783258.2783401","DOI":"10.1145\/2783258.2783401"},{"key":"31_CR17","doi-asserted-by":"publisher","unstructured":"Omi, T., Ueda, N., Aihara, K.: Fully neural network based model for general temporal point processes. In: Proceedings of the 33rd International Conference on Neural Information Processing Systems, pp. 2122\u20132132. Curran Associates Inc., New York (2019). https:\/\/doi.org\/10.5555\/3454287.3454477","DOI":"10.5555\/3454287.3454477"},{"key":"31_CR18","doi-asserted-by":"publisher","unstructured":"Zhang, Q., Lipani, A., Kirnap, O., Yilmaz, E.: Self-attentive hawkes process. In: Proceedings of the 37th International Conference on Machine Learning, pp. 11183\u201311193. PMLR, Vienna (2020). https:\/\/doi.org\/10.5555\/3524938.3525975","DOI":"10.5555\/3524938.3525975"},{"key":"31_CR19","doi-asserted-by":"publisher","unstructured":"Shchur, O., T\u00fcrkmen, A., Januschowski, T., G\u00fcnnemann, S.: Neural temporal point processes: a review. In: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, pp. 4585\u20134593. International Joint Conferences on Artificial Intelligence Organization, Montreal (2021). https:\/\/doi.org\/10.24963\/ijcai.2021\/623","DOI":"10.24963\/ijcai.2021\/623"},{"issue":"3","key":"31_CR20","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1007\/s11009-011-9272-5","volume":"15","author":"J Rasmussen","year":"2013","unstructured":"Rasmussen, J.: Bayesian inference for Hawkes processes. Methodol. Comput. Appl. Probab. 15(3), 623\u2013642 (2013)","journal-title":"Methodol. Comput. Appl. Probab."},{"key":"31_CR21","doi-asserted-by":"publisher","unstructured":"Du, N., Dai, H., Trivedi, R., Upadhyay, U., Gomez-Rodriguez, M., Song, L.: Recurrent marked temporal point processes: embedding event history to vector. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1555\u20131564. Association for Computing Machinery, California (2016). https:\/\/doi.org\/10.1145\/2939672.2939875","DOI":"10.1145\/2939672.2939875"},{"key":"31_CR22","doi-asserted-by":"publisher","unstructured":"Jeffrey, P., Richard, S., Christopher, M.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp. 1532\u20131543. Association for Computational Linguistics, Doha (2014). https:\/\/doi.org\/10.3115\/v1\/D14-1162","DOI":"10.3115\/v1\/D14-1162"},{"key":"31_CR23","doi-asserted-by":"publisher","unstructured":"Ma, J., Gao, W., Wong, K.: Detect rumors in microblog posts using propagation structure via kernel learning. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 708\u2013717. Association for Computational Linguistics, Vancouver (2017). https:\/\/doi.org\/10.18653\/v1\/P17-1066","DOI":"10.18653\/v1\/P17-1066"},{"key":"31_CR24","doi-asserted-by":"publisher","unstructured":"Ma, J., Gao, W., Wei, Z., Lu, Y., Wong, K.: Detect rumors using time series of social sontext information on microblogging websites. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 1751\u20131754. Association for Computing Machinery, Melbourne (2015). https:\/\/doi.org\/10.1145\/2806416.2806607","DOI":"10.1145\/2806416.2806607"},{"key":"31_CR25","doi-asserted-by":"publisher","unstructured":"Ruchansky, N., Seo, S., Liu, Y.: CSI: a hybrid deep model for fake news detection. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 797\u2013806. Association for Computing Machinery, Singapore (2017). https:\/\/doi.org\/10.1145\/3132847.3132877","DOI":"10.1145\/3132847.3132877"},{"key":"31_CR26","doi-asserted-by":"publisher","unstructured":"Shu, K., Cui, L., Wang, S., Lee, D., Liu, H.: DEFEND: explainable fake news detection. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 395\u2013405. Association for Computing Machinery, Anchorage (2019). https:\/\/doi.org\/10.1145\/3292500.3330935","DOI":"10.1145\/3292500.3330935"}],"container-title":["Lecture Notes in Computer Science","Combinatorial Optimization and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-49614-1_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,10]],"date-time":"2024-02-10T09:05:39Z","timestamp":1707555939000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-49614-1_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,9]]},"ISBN":["9783031496134","9783031496141"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-49614-1_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,12,9]]},"assertion":[{"value":"9 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"COCOA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Combinatorial Optimization and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hawai, HI","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 December 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cocoa2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/theory.utdallas.edu\/COCOA2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EquinOCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"117","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"73","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"62% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}