{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T18:08:36Z","timestamp":1784138916685,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":68,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,7,19]],"date-time":"2026-07-19T00:00:00Z","timestamp":1784419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"National Natural Science Foundation of China","award":["U24A20335"],"award-info":[{"award-number":["U24A20335"]}]},{"name":"China Postdoctoral Science Foundation","award":["2024M753481"],"award-info":[{"award-number":["2024M753481"]}]},{"name":"Youth Innovation Promotion Association CAS","award":["-"],"award-info":[{"award-number":["-"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,7,20]]},"DOI":"10.1145\/3805712.3809585","type":"proceedings-article","created":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T14:28:19Z","timestamp":1783693699000},"page":"190-201","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Mitigating Adversarial Attacks by Transferring LLM-generated Narrative Reasoning for Robust Fake News Detection"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5592-4595","authenticated-orcid":false,"given":"Mengyang","family":"Chen","sequence":"first","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China and School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7058-2662","authenticated-orcid":false,"given":"Lingwei","family":"Wei","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7834-0839","authenticated-orcid":false,"given":"Wei","family":"Zhou","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7170-3809","authenticated-orcid":false,"given":"Songlin","family":"Hu","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China and School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,7,19]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5393"},{"key":"e_1_3_2_1_2_1","volume-title":"International Symposium on Graph Drawing. Springer, 501-512","author":"Brandes Ulrik","year":"2001","unstructured":"Ulrik Brandes, Markus Eiglsperger, Ivan Herman, Michael Himsolt, and M Scott Marshall. 2001. GraphML progress report structural layer proposal: Structural layer proposal. In International Symposium on Graph Drawing. Springer, 501-512."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1963405.1963500"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/aaai.12188"},{"key":"e_1_3_2_1_5_1","volume-title":"AAAI 2025 Workshop on Preventing and Detecting LLM Misinformation (PDLM).","author":"Chen Mengyang","year":"2025","unstructured":"Mengyang Chen, Lingwei Wei, Han Cao, Wei Zhou, and Songlin Hu. 2025. Explore the potential of llms in misinformation detection: An empirical study. In AAAI 2025 Workshop on Preventing and Detecting LLM Misinformation (PDLM)."},{"key":"e_1_3_2_1_6_1","volume-title":"Propagation Structure-Semantic Transfer Learning for Robust Fake News Detection. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 227-244","author":"Chen Mengyang","year":"2024","unstructured":"Mengyang Chen, Lingwei Wei, Han Cao, Wei Zhou, Zhou Yan, and Songlin Hu. 2024. Propagation Structure-Semantic Transfer Learning for Robust Fake News Detection. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 227-244."},{"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","volume-title":"Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805","author":"Devlin Jacob","year":"2018","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)."},{"key":"e_1_3_2_1_9_1","first-page":"2051","article-title":"User preference-aware fake news detection","author":"Dou Yingtong","year":"2021","unstructured":"Yingtong Dou, Kai Shu, Congying Xia, et al., 2021. User preference-aware fake news detection. In SIGIR. 2051-2055.","journal-title":"SIGIR."},{"key":"e_1_3_2_1_10_1","volume-title":"propaganda, and disinformation: Online media and the 2016 US presidential election","author":"Faris Robert","year":"2017","unstructured":"Robert Faris, Hal Roberts, Bruce Etling, Nikki Bourassa, Ethan Zuckerman, and Yochai Benkler. 2017. Partisanship, propaganda, and disinformation: Online media and the 2016 US presidential election. Berkman Klein Center Research Publication, Vol. 6 (2017)."},{"key":"e_1_3_2_1_11_1","first-page":"8410","article-title":"Pizzagate: From rumor, to hashtag, to gunfire in DC","volume":"6","author":"Fisher Marc","year":"2016","unstructured":"Marc Fisher, John Woodrow Cox, and Peter Hermann. 2016. Pizzagate: From rumor, to hashtag, to gunfire in DC. Washington Post, Vol. 6 (2016), 8410-8415.","journal-title":"Washington Post"},{"key":"e_1_3_2_1_12_1","volume-title":"international conference on machine learning. PMLR","author":"Gao Hongyang","year":"2019","unstructured":"Hongyang Gao and Shuiwang Ji. 2019. Graph u-nets. In international conference on machine learning. PMLR, 2083-2092."},{"key":"e_1_3_2_1_13_1","unstructured":"Aaron Grattafiori Abhimanyu Dubey Abhinav Jauhri Abhinav Pandey Abhishek Kadian Ahmad Al-Dahle Aiesha Letman Akhil Mathur Alan Schelten Alex Vaughan et al. 2024. The llama 3 herd of models. arXiv preprint arXiv:2407.21783 (2024)."},{"key":"e_1_3_2_1_14_1","volume-title":"Graphedit: Large language models for graph structure learning. arXiv preprint arXiv:2402.15183","author":"Guo Zirui","year":"2024","unstructured":"Zirui Guo, Lianghao Xia, Yanhua Yu, Yuling Wang, Zixuan Yang, Wei Wei, Liang Pang, Tat-Seng Chua, and Chao Huang. 2024. Graphedit: Large language models for graph structure learning. arXiv preprint arXiv:2402.15183 (2024)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.3390\/s23041748"},{"key":"e_1_3_2_1_16_1","volume-title":"NIPS","volume":"30","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. NIPS, Vol. 30 (2017)."},{"key":"e_1_3_2_1_17_1","first-page":"2020","article-title":"Rumor detection on social media with event augmentations","author":"He Zhenyu","year":"2021","unstructured":"Zhenyu He, Ce Li, Fan Zhou, et al., 2021. Rumor detection on social media with event augmentations. In SIGIR. 2020-2024.","journal-title":"SIGIR."},{"key":"e_1_3_2_1_18_1","volume-title":"Distilling the Knowledge in a Neural Network. arXiv preprint arXiv:1503.02531","author":"Hinton Geoffrey","year":"2015","unstructured":"Geoffrey Hinton. 2015. Distilling the Knowledge in a Neural Network. arXiv preprint arXiv:1503.02531 (2015)."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i20.30214"},{"key":"e_1_3_2_1_20_1","first-page":"1395","article-title":"A rumor detection approach based on multi-relational propagation tree","volume":"58","author":"Hu D.","year":"2021","unstructured":"D. Hu, L. Wei, W. Zhou, et al., 2021. A rumor detection approach based on multi-relational propagation tree. Journal of Computer Research and Development, Vol. 58, 7 (2021), 1395-1411.","journal-title":"Journal of Computer Research and Development"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i16.33901"},{"key":"e_1_3_2_1_22_1","unstructured":"Yue Huang and Lichao Sun. 2023. Harnessing the Power of ChatGPT in Fake News: An In-Depth Exploration in Generation Detection and Explanation. arXiv:2310.05046 [cs.CL]"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3696410.3714610"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i17.33944"},{"key":"e_1_3_2_1_25_1","volume-title":"FakeBERT: Fake news detection in social media with a BERT-based deep learning approach. Multimedia tools and applications","author":"Kaliyar Rohit Kumar","year":"2021","unstructured":"Rohit Kumar Kaliyar, Anurag Goswami, and Pratik Narang. 2021. FakeBERT: Fake news detection in social media with a BERT-based deep learning approach. Multimedia tools and applications, Vol. 80, 8 (2021), 11765-11788."},{"key":"e_1_3_2_1_26_1","volume-title":"Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations.","author":"Kipf Thomas N","year":"2017","unstructured":"Thomas N Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.52202\/079017-1559"},{"key":"e_1_3_2_1_28_1","first-page":"2543","article-title":"Detect Rumors in Microblog Posts for Low-Resource Domains via Adversarial Contrastive Learning","author":"Lin H.","year":"2022","unstructured":"H. Lin, J. Ma, L. Chen, Z. Yang, M. Cheng, and C. Guang. 2022. Detect Rumors in Microblog Posts for Low-Resource Domains via Adversarial Contrastive Learning. In Proc. NAACL-HLT. 2543-2556.","journal-title":"Proc. NAACL-HLT."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"crossref","unstructured":"Yang Liu and Yi fang Brook Wu. 2018. Early Detection of Fake News on Social Media Through Propagation Path Classification with Recurrent and Convolutional Networks. In AAAI.","DOI":"10.1609\/aaai.v32i1.11268"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3648139"},{"key":"e_1_3_2_1_31_1","first-page":"103354","article-title":"Dual emotion based fake news detection: A deep attention-weight update approach","volume":"60","author":"Luvembe Alex Munyole","year":"2023","unstructured":"Alex Munyole Luvembe, Weimin Li, Shaohua Li, et al., 2023. Dual emotion based fake news detection: A deep attention-weight update approach. IPM, Vol. 60, 4 (2023), 103354.","journal-title":"IPM"},{"key":"e_1_3_2_1_32_1","first-page":"1441","article-title":"Towards robust false information detection on social networks with contrastive learning","author":"Ma Guanghui","year":"2022","unstructured":"Guanghui Ma, Chunming Hu, Ling Ge, et al., 2022. Towards robust false information detection on social networks with contrastive learning. In CIKM. 1441-1450.","journal-title":"CIKM."},{"key":"e_1_3_2_1_33_1","first-page":"1751","article-title":"Detect rumors using time series of social context information on microblogging websites","author":"Ma Jing","year":"2015","unstructured":"Jing Ma, Wei Gao, Zhongyu Wei, et al., 2015. Detect rumors using time series of social context information on microblogging websites. In CIKM. 1751-1754.","journal-title":"CIKM."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1184"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-1066"},{"key":"e_1_3_2_1_36_1","first-page":"3049","article-title":"Detect rumors on twitter by promoting information campaigns with generative adversarial learning","author":"Ma Jing","year":"2019","unstructured":"Jing Ma, Wei Gao, and Kam-Fai Wong. 2019. Detect rumors on twitter by promoting information campaigns with generative adversarial learning. In WWW. 3049-3055.","journal-title":"WWW."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3627673.3679519"},{"key":"e_1_3_2_1_38_1","first-page":"797","article-title":"Csi: A hybrid deep model for fake news detection","author":"Ruchansky Natali","year":"2017","unstructured":"Natali Ruchansky, Sungyong Seo, and Yan Liu. 2017. Csi: A hybrid deep model for fake news detection. In CIKM. 797-806.","journal-title":"CIKM."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543873.3587596"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330935"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3137597.3137600"},{"key":"e_1_3_2_1_42_1","first-page":"3035","article-title":"CED: Credible early detection of social media rumors","volume":"33","author":"Song Changhe","year":"2019","unstructured":"Changhe Song, Cheng Yang, Huimin Chen, et al., 2019. CED: Credible early detection of social media rumors. TKDE, Vol. 33, 8 (2019), 3035-3047.","journal-title":"TKDE"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.findings-acl.118"},{"key":"e_1_3_2_1_44_1","first-page":"2789","article-title":"Rumor detection on social media with graph adversarial contrastive learning","author":"Sun Tiening","year":"2022","unstructured":"Tiening Sun, Zhong Qian, Sujun Dong, et al., 2022. Rumor detection on social media with graph adversarial contrastive learning. In WWW. 2789-2797.","journal-title":"WWW."},{"key":"e_1_3_2_1_45_1","volume-title":"Graph Attention Networks. In International Conference on Learning Representations.","author":"Veli\u010dkovi\u0107 Petar","year":"2018","unstructured":"Petar Veli\u010dkovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2018. Graph Attention Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_46_1","volume-title":"The spread of true and false news online. science","author":"Vosoughi Soroush","year":"2018","unstructured":"Soroush Vosoughi, Deb Roy, and Sinan Aral. 2018. The spread of true and false news online. science, Vol. 359, 6380 (2018), 1146-1151."},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-acl.155"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2025.acl-long.480"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3726302.3729928"},{"key":"e_1_3_2_1_50_1","first-page":"3845","author":"Wei Lingwei","year":"2021","unstructured":"Lingwei Wei, Dou Hu, Wei Zhou, et al., 2021. Towards Propagation Uncertainty: Edge-enhanced Bayesian Graph Convolutional Networks for Rumor Detection. In ACL. 3845-3854.","journal-title":"In ACL."},{"key":"e_1_3_2_1_51_1","first-page":"2759","article-title":"Uncertainty-aware propagation structure reconstruction for fake news detection","author":"Wei Lingwei","year":"2022","unstructured":"Lingwei Wei, Dou Hu, Wei Zhou, et al., 2022. Uncertainty-aware propagation structure reconstruction for fake news detection. In COLING. 2759-2768.","journal-title":"COLING."},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3190348"},{"key":"e_1_3_2_1_53_1","first-page":"9739","article-title":"Structure-adaptive Adversarial Contrastive Learning for Multi-Domain Fake News Detection. In ACL (Findings) (Findings of ACL, )","author":"Wei Lingwei","year":"2025","unstructured":"Lingwei Wei, Dou Hu, Wei Zhou, Philip S. Yu, and Songlin Hu. 2025. Structure-adaptive Adversarial Contrastive Learning for Multi-Domain Fake News Detection. In ACL (Findings) (Findings of ACL, ). Association for Computational Linguistics, 9739-9752.","journal-title":"Association for Computational Linguistics"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671977"},{"key":"e_1_3_2_1_55_1","first-page":"2582","article-title":"Decor: Degree-corrected social graph refinement for fake news detection","author":"Wu Jiaying","year":"2023","unstructured":"Jiaying Wu and Bryan Hooi. 2023. Decor: Degree-corrected social graph refinement for fake news detection. In KDD. 2582-2593.","journal-title":"KDD."},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2025.acl-long.431"},{"key":"e_1_3_2_1_57_1","unstructured":"An Yang Anfeng Li Baosong Yang Beichen Zhang Binyuan Hui Bo Zheng Bowen Yu Chang Gao Chengen Huang Chenxu Lv et al. 2025. Qwen3 technical report. arXiv preprint arXiv:2505.09388 (2025)."},{"key":"e_1_3_2_1_58_1","first-page":"1417","article-title":"Rumor detection on social media with graph structured adversarial learning","author":"Yang Xiaoyu","year":"2021","unstructured":"Xiaoyu Yang, Yuefei Lyu, Tian Tian, et al., 2021. Rumor detection on social media with graph structured adversarial learning. In IJCAI. 1417-1423.","journal-title":"IJCAI."},{"key":"e_1_3_2_1_59_1","first-page":"3901","article-title":"A Convolutional Approach for Misinformation Identification","author":"Yu Feng","year":"2017","unstructured":"Feng Yu, Qiang Liu, Shu Wu, et al., 2017. A Convolutional Approach for Misinformation Identification.. In IJCAI. 3901-3907.","journal-title":"IJCAI."},{"key":"e_1_3_2_1_60_1","first-page":"5444","article-title":"Early Detection of Fake News by Utilizing the Credibility of News, Publishers, and Users based on Weakly Supervised Learning","author":"Yuan Chunyuan","year":"2020","unstructured":"Chunyuan Yuan, Qianwen Ma, Wei Zhou, et al., 2020. Early Detection of Fake News by Utilizing the Credibility of News, Publishers, and Users based on Weakly Supervised Learning. In COLING. 5444-5454.","journal-title":"COLING."},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2025.acl-long.617"},{"key":"e_1_3_2_1_62_1","volume-title":"Rethinking Graph Structure Learning in the Era of LLMs. arXiv preprint arXiv:2503.21223","author":"Zhang Zhihan","year":"2025","unstructured":"Zhihan Zhang, Xunkai Li, Zhu Lei, Guang Zeng, Ronghua Li, and Guoren Wang. 2025a. Rethinking Graph Structure Learning in the Era of LLMs. arXiv preprint arXiv:2503.21223 (2025)."},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/3690624.3709256"},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1145\/3690624.3709256"},{"key":"e_1_3_2_1_65_1","volume-title":"Proceedings of the Annual Meeting of the Cognitive Science Society","volume":"47","author":"Zhao Congyuan","year":"2025","unstructured":"Congyuan Zhao, Lingwei Wei, Ziming Qin, Wei Zhou, Yunya Song, and Songlin Hu. 2025. MPPFND: A Dataset and Analysis of Detecting Fake News with Multi-Platform Propagation. In Proceedings of the Annual Meeting of the Cognitive Science Society, Vol. 47."},{"key":"e_1_3_2_1_66_1","volume-title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence. 568-576","author":"Zhu Peican","year":"2024","unstructured":"Peican Zhu, Zechen Pan, Yang Liu, Jiwei Tian, Keke Tang, and Zhen Wang. 2024. A general black-box adversarial attack on graph-based fake news detectors. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence. 568-576."},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1145\/3696410.3714801"},{"key":"e_1_3_2_1_68_1","doi-asserted-by":"crossref","unstructured":"Arkaitz Zubiaga Maria Liakata and Rob Procter. 2016. Learning Reporting Dynamics during Breaking News for Rumour Detection in Social Media. arXiv:1610.07363 [cs.CL]","DOI":"10.1007\/978-3-319-67217-5_8"}],"event":{"name":"SIGIR '26: The 49th International ACM SIGIR Conference on Research and Development in Information Retrieval","location":"Melbourne VIC Australia","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval"],"original-title":[],"deposited":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T17:26:23Z","timestamp":1784136383000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3805712.3809585"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,19]]},"references-count":68,"alternative-id":["10.1145\/3805712.3809585","10.1145\/3805712"],"URL":"https:\/\/doi.org\/10.1145\/3805712.3809585","relation":{},"subject":[],"published":{"date-parts":[[2026,7,19]]},"assertion":[{"value":"2026-07-19","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}