{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T05:22:21Z","timestamp":1764393741162,"version":"3.46.0"},"reference-count":34,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T00:00:00Z","timestamp":1764201600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62377009"],"award-info":[{"award-number":["62377009"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>The harmfulness of online fake news has brought widespread attention to fake news detection by researchers. Most existing methods focus on improving the accuracy and early detection of fake news, while ignoring the frequent cross-topic issues faced by fake news in online environments. A hierarchical fake news detection method (HAMFD) based on multi-task learning and adversarial training is proposed. Through the multi-task learning task at the event level, subjective and objective information is introduced. A subjectivity classifier is used to capture sentiment shift within events, aiming to improve in-domain performance and generalization ability of fake news detection. On this basis, textual features and sentiment shift features are fused to perform event-level fake news detection and enhance detection accuracy. The post-level loss and event-level loss are weighted and summed for backpropagation. Adversarial perturbations are added to the embedding layer of the post-level module to deceive the detector, enabling the model to better resist adversarial attacks and enhance its robustness and topic adaptability. Experiments are conducted on three real-world social media datasets, and the results show that the proposed method improves performance in both in-domain and cross-topic fake news detection. Specifically, the model attains accuracies of 91.3% on Twitter15, 90.4% on Twitter16, and 95.7% on Weibo, surpassing advanced baseline methods by 1.6%, 1.5%, and 1.1%, respectively.<\/jats:p>","DOI":"10.3390\/informatics12040131","type":"journal-article","created":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T10:25:16Z","timestamp":1764239116000},"page":"131","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hierarchical Fake News Detection Model Based on Multi-Task Learning and Adversarial Training"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-5389-3589","authenticated-orcid":false,"given":"Yi","family":"Sun","sequence":"first","affiliation":[{"name":"Manchester Metropolitan Joint Institute, Hubei University, Wuhan 430062, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3384-5301","authenticated-orcid":false,"given":"Dunhui","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hubei University, Wuhan 430062, China"},{"name":"Hubei Key Laboratory of Big Data Intelligent Analysis and Application, Hubei University, Wuhan 430062, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"171","DOI":"10.3961\/jpmph.20.094","article-title":"Impact of Rumors and Misinformation on COVID-19 in Social Media","volume":"53","author":"Tasnim","year":"2020","journal-title":"J. Prev. Med. Public Health"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1146","DOI":"10.1126\/science.aap9559","article-title":"The spread of true and false news online","volume":"359","author":"Vosoughi","year":"2018","journal-title":"Science"},{"key":"ref_3","unstructured":"P\u00e9rez-Rosas, V., Kleinberg, B., Lefevre, A., and Mihalcea, R. (2017). Automatic Detection of Fake News. arXiv."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Kwon, S., Cha, M., Jung, K., Chen, W., and Wang, Y. (2013, January 7\u201310). Prominent Features of Rumor Propagation in Online Social Media. Proceedings of the 2013 IEEE 13th International Conference on Data Mining, Dallas, TX, USA.","DOI":"10.1109\/ICDM.2013.61"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Yang, F., Yu, X., Liu, Y., and Yang, M. (2012, January 12\u201316). Automatic Detection of Rumor on Sina Weibo. Proceedings of the MDS \u201912: Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics, Beijing, China.","DOI":"10.1145\/2350190.2350203"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2248","DOI":"10.1111\/j.1559-1816.2008.00390.x","article-title":"Rumor Has It: The Moderating Effect of Identification on Rumor Impact and the Effectiveness of Rumor Refutation","volume":"38","author":"Einwiller","year":"2008","journal-title":"J. Appl. Soc. Psychol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1016\/j.jesp.2012.12.010","article-title":"Rumor clustering, consensus, and polarization: Dynamic social impact and self-organization of hearsay","volume":"49","author":"DiFonzo","year":"2013","journal-title":"J. Exp. Soc. Psychol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.physa.2014.07.023","article-title":"Dynamic 8-state ICSAR rumor propagation model considering official rumor refutation","volume":"415","author":"Zhang","year":"2014","journal-title":"Phys. A Stat. Mech. Its Appl."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1037\/0003-066X.46.5.484","article-title":"Inside rumor: A personal journey","volume":"46","author":"Rosnow","year":"2016","journal-title":"Am. Psychol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1109\/TASLP.2022.3140474","article-title":"SIFTER: A Framework for Robust Rumor Detection","volume":"30","author":"Lu","year":"2022","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"ref_11","unstructured":"Castillo, C., Mendoza, M., and Poblete, B. (April, January 28). Information credibility on Twitter. Proceedings of the WWW \u201911: Proceedings of the 20th International Conference on World Wide Web, Hyderabad, India."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Liu, X., Nourbakhsh, A., Li, Q., Fang, R., and Shah, S. (2015, January 18\u201323). Real-time rumor debunking on Twitter. Proceedings of the CIKM \u201915: Proceedings of the 24th ACM International Conference on Information and Knowledge Management, Melbourne, Australia.","DOI":"10.1145\/2806416.2806651"},{"key":"ref_13","first-page":"1811","article-title":"An improving deception detection method in computer-mediated communication","volume":"7","author":"Zhang","year":"2012","journal-title":"Networks"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wu, K., Yang, S., and Zhu, K.Q. (2015, January 13\u201317). False rumors detection on Sina Weibo by propagation structures. Proceedings of the 2015 IEEE 31st International Conference on Data Engineering, Seoul, Republic of Korea.","DOI":"10.1109\/ICDE.2015.7113322"},{"key":"ref_15","unstructured":"Vosoughi, S. (2015). Automatic Detection and Verification of Rumors on Twitter, Massachusetts Institute of Technology."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3070644","article-title":"Rumor Gauge: Predicting the veracity of rumors on Twitter","volume":"11","author":"Vosoughia","year":"2017","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"ref_17","unstructured":"Zubiaga, A., Liakata, M., Procter, R., Bontcheva, K., and Tolmie, P. (2015). Towards Detecting Rumours in Social Media. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"113595","DOI":"10.1016\/j.eswa.2020.113595","article-title":"Rumor detection based on propagation graph neural network with attention mechanism","volume":"158","author":"Wu","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.ins.2021.04.018","article-title":"JUDO: Just-in-time rumour detection in streaming social platforms","volume":"570","author":"Nguyen","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"107818","DOI":"10.1016\/j.patcog.2021.107818","article-title":"BCMM: A novel post-based augmentation representation for early rumour detection on social media","volume":"113","author":"Luo","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"116071","DOI":"10.1016\/j.eswa.2021.116071","article-title":"PostCom2DR: Utilizing information from post and comments to detect rumors","volume":"189","author":"Yang","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4887","DOI":"10.1109\/TNNLS.2022.3161697","article-title":"Nowhere to Hide: Online Rumor Detection Based on Retweeting Graph Neural Networks","volume":"35","author":"Liu","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"119083","DOI":"10.1016\/j.ins.2023.119083","article-title":"Rumor detection on social media through mining the social circles with high homogeneity","volume":"642","author":"Zheng","year":"2023","journal-title":"Inf. Sci."},{"key":"ref_24","first-page":"549","article-title":"Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks","volume":"34","author":"Bian","year":"2020","journal-title":"Proc. Aaai Conf. Artif. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"17347","DOI":"10.1007\/s11042-022-12761-y","article-title":"Rumor Detection in Social Network Based on User, Content and Lexical Features","volume":"81","author":"Shelke","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Dan, Z., Dong, F., Gao, Z., and Zhang, Y. (2022). A Rumor Detection Method Based on Adaptive Fusion of Statistical Features and Textual Features. Information, 13.","DOI":"10.3390\/info13080388"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., and Manning, C. (2014, January 25\u201329). GloVe: Global Vectors for Word Representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar.","DOI":"10.3115\/v1\/D14-1162"},{"key":"ref_28","unstructured":"Ma, J., Gao, W., Mitra, P., Kwon, S., Jansen, B.J., Wong, K.F., and Cha, M. (2016, January 9\u201315). Detecting rumors from microblogs with recurrent neural networks. Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI), New York, NY, USA. Available online: https:\/\/dl.acm.org\/doi\/proceedings\/10.5555\/3061053."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Pang, B., and Lee, L. (2004, January 21\u201326). A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. Proceedings of the ACL \u201904: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, Barcelona, Spain.","DOI":"10.3115\/1218955.1218990"},{"key":"ref_30","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ma, J., Gao, W., Wei, Z., Lu, Y., and Wong, K.-F. (2015, January 18\u201323). Detect Rumors Using Time Series of Social Context Information on Microblogging Websites. Proceedings of the CIKM \u201915: Proceedings of the 24th ACM International Conference on Information and Knowledge Management, Melbourne, Australia.","DOI":"10.1145\/2806416.2806607"},{"key":"ref_32","unstructured":"Ma, J., Gao, W., and Wong, K.F. (August, January 30). Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, BC, Canada."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ma, J., Gao, W., and Wong, K.F. (2018, January 15\u201320). Rumor Detection on Twitter with Tree-structured Recursive Neural Networks. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia.","DOI":"10.18653\/v1\/P18-1184"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Liu, Y., and Wu, Y.F.B. (2018, January 2\u20137). Early Detection of Fake News on Social Media Through Propagation Path Classification with Recurrent and Convolutional Networks. Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), New Orleans, LA, USA. Available online: https:\/\/dl.acm.org\/doi\/abs\/10.5555\/3504035.3504079.","DOI":"10.1609\/aaai.v32i1.11268"}],"container-title":["Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9709\/12\/4\/131\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T05:19:27Z","timestamp":1764393567000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9709\/12\/4\/131"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,27]]},"references-count":34,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["informatics12040131"],"URL":"https:\/\/doi.org\/10.3390\/informatics12040131","relation":{},"ISSN":["2227-9709"],"issn-type":[{"type":"electronic","value":"2227-9709"}],"subject":[],"published":{"date-parts":[[2025,11,27]]}}}