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Knowl. Discov. Data"],"published-print":{"date-parts":[[2025,11,30]]},"abstract":"<jats:p>\n            In social networks, studying rumor propagation patterns is essential for curbing the spread of rumors. Given the coexistence and conflict of multiple-type rumor information, as well as users\u2019 cognitive differences, this article presents a rumor propagation model grounded in user cognition and evolutionary game theory. First, considering the potential impact of social relationships between users on rumor propagation, the KD-Tree algorithm is employed to uncover hidden connections between users, thereby enriching the topology of the user\u2019s social network. Second, a user behavior driving mechanism for rumor, anti-rumor, and motivation-rumor types is constructed based on evolutionary games to reflect the interactive and strategic nature of users\u2019 responses. Moreover, the Lotka-Volterra equation is utilized to explore the dynamic game of multi-type rumor information and the cognitive process of users. Finally, to address differences in users\u2019 cognition, this article introduces the anti-rumor trust state\n            <jats:italic toggle=\"yes\">A<\/jats:italic>\n            and the motivation-rumor trust state\n            <jats:italic toggle=\"yes\">M<\/jats:italic>\n            , which arise from users\u2019 exposure to multiple types of rumor information. Based on these trust states, a rumor propagation model, SIAMR, is constructed using user cognition and evolutionary game theory. Experiments demonstrate that the model accurately captures the dynamic interactions between multi-type rumor information and the transmission process of rumor topics in social networks. The proposed model integrates cognitive psychology with a strategic interaction framework, offering a more realistic representation of rumor propagation behavior in the real world. Experimental results reveal that SIAMR improves prediction accuracy by 14.23% over baseline models in simulating the dynamics of multiple types of rumors, effectively capturing users\u2019 cognitive influences and the mechanisms of information competition.\n          <\/jats:p>","DOI":"10.1145\/3767161","type":"journal-article","created":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T22:22:12Z","timestamp":1757629332000},"page":"1-33","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A Rumor Propagation Model Based on User Cognition and Evolutionary Game"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7963-1766","authenticated-orcid":false,"given":"Rong","family":"Wang","sequence":"first","affiliation":[{"name":"School of Communications and Information Engineering, Chongqing University of Posts\u00a0and Telecommunications, Chongqing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3619-8046","authenticated-orcid":false,"given":"Zerui","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1522-6948","authenticated-orcid":false,"given":"Liangyu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4595-8215","authenticated-orcid":false,"given":"Chaolong","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2846-3571","authenticated-orcid":false,"given":"Yunpeng","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China"}]}],"member":"320","published-online":{"date-parts":[[2025,10,18]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-023-06883-y"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.3390\/app15116092"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.23919\/ACC60939.2024.10644997"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2024.3468903"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102530"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1140\/epjb\/s10051-024-00797-y"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cnsns.2023.107603"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1088\/1674-1056\/ad531f"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.physd.2023.133921"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2024.3383493"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2023.3336649"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2022.3181130"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/3701738"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2023.3331333"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2023.113229"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2025.3525896"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i8.28701"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119417"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2024.3505205"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.amc.2023.128233"},{"issue":"2","key":"e_1_3_1_22_2","first-page":"1","article-title":"Exploiting conversation-branch-tweet hypergraph structure to detect misinformation on social media","volume":"18","author":"Li Fangfang","year":"2023","unstructured":"Fangfang Li, Zhi Liu, Junwen Duan, Xingliang Mao, Heyuan Shi, and Shichao Zhang. 2023. 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