{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T15:34:23Z","timestamp":1789659263488,"version":"4.0.1"},"reference-count":159,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T00:00:00Z","timestamp":1693785600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fundamental Research Funds for the Central Universities","award":["CUC23GY005"],"award-info":[{"award-number":["CUC23GY005"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["KX202315"],"award-info":[{"award-number":["KX202315"]}]},{"name":"Guangxi Key Laboratory of Trusted Software","award":["CUC23GY005"],"award-info":[{"award-number":["CUC23GY005"]}]},{"name":"Guangxi Key Laboratory of Trusted Software","award":["KX202315"],"award-info":[{"award-number":["KX202315"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>With the popularization of digital technology, the problem of information pollution caused by fake news has become more common. Malicious dissemination of harmful, offensive or illegal content may lead to misleading, misunderstanding and social unrest, affecting social stability and sustainable economic development. With the continuous iteration of artificial intelligence technology, researchers have carried out automatic and intelligent news data mining and analysis based on aspects of information characteristics and realized the effective identification of fake news information. However, the current research lacks the application of multidisciplinary knowledge and research on the interpretability of related methods. This paper focuses on the existing fake news detection technology. The survey includes fake news datasets, research methods for fake news detection, general technical models and multimodal related technical methods. The innovation contribution is to discuss the research progress of fake news detection in communication, linguistics, psychology and other disciplines. At the same time, it classifies and summarizes the explainable fake news detection methods and proposes an explainable human-machine-theory triangle communication system, aiming at establishing a people-centered, sustainable human\u2013machine interaction information dissemination system. Finally, we discuss the promising future research topics of fake news detection technology.<\/jats:p>","DOI":"10.3390\/systems11090458","type":"journal-article","created":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T02:43:20Z","timestamp":1693795400000},"page":"458","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Sustainable Development of Information Dissemination: A Review of Current Fake News Detection Research and Practice"],"prefix":"10.3390","volume":"11","author":[{"given":"Lu","family":"Yuan","sequence":"first","affiliation":[{"name":"School of Data Science and Media Intelligence, Communication University of China, Beijing 100024, China"},{"name":"State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hangshun","family":"Jiang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Shen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5570-7818","authenticated-orcid":false,"given":"Lei","family":"Shi","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China"},{"name":"Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nanchang","family":"Cheng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,4]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"101475","DOI":"10.1016\/j.tele.2020.101475","article-title":"Fake news and COVID-19: Modelling the predictors of fake news sharing among social media users","volume":"56","author":"Apuke","year":"2021","journal-title":"Telemat. Inform."},{"key":"ref_3","first-page":"2928","article-title":"Inoculating against fake news about COVID-19","volume":"2020","author":"Roozenbeek","year":"2020","journal-title":"Front. Psychol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1007\/s10389-021-01658-z","article-title":"The impact of fake news on social media and its influence on health during the COVID-19 pandemic: A systematic review","volume":"31","author":"Rocha","year":"2021","journal-title":"J. Public Health"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Moscadelli, A., Albora, G., Biamonte, M.A., Giorgetti, D., Innocenzio, M., Paoli, S., Lorini, C., Bonanni, P., and Bonaccorsi, G. (2020). Fake news and Covid-19 in Italy: Results of a quantitative observational study. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17165850"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3395046","article-title":"A survey of fake news: Fundamental theories, detection methods, and opportunities","volume":"53","author":"Zhou","year":"2020","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"102025","DOI":"10.1016\/j.ipm.2019.03.004","article-title":"An overview of online fake news: Characterization, detection, and discussion","volume":"57","author":"Zhang","year":"2020","journal-title":"Inf. Process. Manag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.aiopen.2022.09.001","article-title":"Deep learning for fake news detection: A comprehensive survey","volume":"3","author":"Hu","year":"2022","journal-title":"AI Open"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"106087","DOI":"10.1016\/j.engappai.2023.106087","article-title":"A systematic survey on explainable AI applied to fake news detection","volume":"122","author":"Athira","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"19","DOI":"10.21248\/jlcl.20.2005.68","article-title":"A brief survey of text mining","volume":"20","author":"Hotho","year":"2005","journal-title":"J. Lang. Technol. Comput. Linguist."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhou, Z.-H. (2021). Machine Learning, Springer Nature.","DOI":"10.1007\/978-981-15-1967-3"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chowdhary, K., and Chowdhary, K.R. (2020). Fundamentals of Artificial Intelligence, Springer.","DOI":"10.1007\/978-81-322-3972-7"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"7068349","DOI":"10.1155\/2018\/7068349","article-title":"Deep learning for computer vision: A brief review","volume":"2018","author":"Voulodimos","year":"2018","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","article-title":"Backpropagation applied to handwritten zip code recognition","volume":"1","author":"LeCun","year":"1989","journal-title":"Neural Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1207\/s15516709cog1402_1","article-title":"Finding structure in time","volume":"14","author":"Elman","year":"1990","journal-title":"Cogn. Sci."},{"key":"ref_17","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the Advances in Neural Information Processing Systems 30 (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_18","unstructured":"Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv."},{"key":"ref_19","unstructured":"Radford, A., Narasimhan, K., Salimans, T., and Sutskever, I. (2018). Improving language understanding by generative pre-training. OpenAI Blog."},{"key":"ref_20","first-page":"9","article-title":"Language models are unsupervised multitask learners","volume":"1","author":"Radford","year":"2019","journal-title":"OpenAI Blog"},{"key":"ref_21","first-page":"1877","article-title":"Language models are few-shot learners","volume":"33","author":"Brown","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Peng, N., and Dredze, M. (2015, January 17\u201321). Named Entity Recognition for Chinese Social Media with Jointly Trained Embeddings. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal.","DOI":"10.18653\/v1\/D15-1064"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1089\/big.2020.0062","article-title":"Fakenewsnet: A data repository with news content, social context, and spatiotemporal information for studying fake news on social media","volume":"8","author":"Shu","year":"2020","journal-title":"Big Data"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wang, W.Y. (2017). \u201cLiar, Liar Pants on Fire\u201d: A New Benchmark Dataset for Fake News Detection. arXiv.","DOI":"10.18653\/v1\/P17-2067"},{"key":"ref_25","unstructured":"Zellers, R., Holtzman, A., Rashkin, H., Bisk, Y., Farhadi, A., Roesner, F., and Choi, Y. (2020). Defending Against Neural Fake News. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Potthast, M., Kiesel, J., Reinartz, K., Bevendorff, J., and Stein, B. (2017). A stylometric inquiry into hyperpartisan and fake news. arXiv.","DOI":"10.18653\/v1\/P18-1022"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Rashkin, H., Choi, E., Jang, J.Y., Volkova, S., and Choi, Y. (2017, January 9\u201311). Truth of varying shades: Analyzing language in fake news and political fact-checking. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark.","DOI":"10.18653\/v1\/D17-1317"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"107559","DOI":"10.1016\/j.asoc.2021.107559","article-title":"An effective fake news detection method using WOA-xgbTree algorithm and content-based features","volume":"109","author":"Sheikhi","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Shu, K., Wang, S., and Liu, H. (2018, January 10\u201312). Understanding user profiles on social media for fake news detection. Proceedings of the 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), IEEE, Miami, FL, USA.","DOI":"10.1109\/MIPR.2018.00092"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Shu, K., Wang, S., and Liu, H. (2019, January 11\u201315). Beyond news contents: The role of social context for fake news detection. Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, Melbourne, VIC, Australia.","DOI":"10.1145\/3289600.3290994"},{"key":"ref_31","unstructured":"Monti, F., Frasca, F., Eynard, D., Mannion, D., and Bronstein, M.M. (2019). Fake news detection on social media using geometric deep learning. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1007\/s41060-021-00302-z","article-title":"Fake news detection based on news content and social contexts: A transformer-based approach","volume":"13","author":"Raza","year":"2022","journal-title":"Int. J. Data Sci. Anal."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Pan, J.Z., Pavlova, S., Li, C., Li, N., Li, Y., and Liu, J. (2018, January 8\u201312). Content based fake news detection using knowledge graphs. Proceedings of the Semantic Web\u2013ISWC 2018: 17th International Semantic Web Conference, Monterey, CA, USA. Proceedings, Part I 17.","DOI":"10.1007\/978-3-030-00671-6_39"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Hu, L., Yang, T., Zhang, L., Zhong, W., Tang, D., Shi, C., Duan, N., and Zhou, M. (2021, January 1\u20136). Compare to the knowledge: Graph neural fake news detection with external knowledge. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Virtual Event.","DOI":"10.18653\/v1\/2021.acl-long.62"},{"key":"ref_35","first-page":"1","article-title":"Natural language processing based hybrid model for detecting fake news using content-based features and social features","volume":"11","author":"Bauskar","year":"2019","journal-title":"Int. J. Inf. Eng. Electron. Bus."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wu, Y., Zhan, P., Zhang, Y., Wang, L., and Xu, Z. (2021, January 1\u20136). Multimodal fusion with co-attention networks for fake news detection. Proceedings of the Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, Online Event.","DOI":"10.18653\/v1\/2021.findings-acl.226"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wang, L., Zhang, C., Xu, H., Zhang, S., Xu, X., and Wang, S. (2023). Cross-modal Contrastive Learning for Multimodal Fake News Detection. arXiv.","DOI":"10.1145\/3581783.3613850"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Amri, S., Sallami, D., and A\u00efmeur, E. (2021, January 7\u201310). Exmulf: An explainable multimodal content-based fake news detection system. Proceedings of the International Symposium on Foundations and Practice of Security, Paris, France.","DOI":"10.1007\/978-3-031-08147-7_12"},{"key":"ref_39","unstructured":"Cao, J., Qi, P., Sheng, Q., Yang, T., Guo, J., and Li, J. (2020). Disinformation, Misinformation, and Fake News in Social Media: Emerging Research Challenges and Opportunities, Springer."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Qi, P., Cao, J., Yang, T., Guo, J., and Li, J. (2019, January 8\u201311). Exploiting multi-domain visual information for fake news detection. Proceedings of the 2019 IEEE International Conference on Data Mining (ICDM), IEEE, Beijing, China.","DOI":"10.1109\/ICDM.2019.00062"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Singhal, S., Shah, R.R., Chakraborty, T., Kumaraguru, P., and Satoh, S. (2019, January 11\u201313). Spotfake: A multi-modal framework for fake news detection. Proceedings of the 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM), IEEE, Singapore.","DOI":"10.1109\/BigMM.2019.00-44"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Qian, S., Wang, J., Hu, J., Fang, Q., and Xu, C. (2021, January 11\u201315). Hierarchical multi-modal contextual attention network for fake news detection. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event.","DOI":"10.1145\/3404835.3462871"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_44","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zhou, X., Zafarani, R., Shu, K., and Liu, H. (2019, January 11\u201315). Fake news: Fundamental theories, detection strategies and challenges. Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, Melbourne, VIC, Australia.","DOI":"10.1145\/3289600.3291382"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhou, X., Jain, A., Phoha, V.V., and Zafarani, R. (2019). Fake news early detection: An interdisciplinary study. arXiv.","DOI":"10.1145\/3308560.3316476"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"153806","DOI":"10.1007\/s11704-020-9256-0","article-title":"The mass, fake news, and cognition security","volume":"15","author":"Guo","year":"2021","journal-title":"Front. Comput. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Greifeneder, R., Jaffe, M., Newman, E., and Schwarz, N. (2021). The Psychology of Fake News: Accepting, Sharing, and Correcting Misinformation, Routledge.","DOI":"10.4324\/9780429295379"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Abraham, A., Hanne, T., Castillo, O., Gandhi, N., Rios, T.N., and Hong, T.-P. (2021). Hybrid Intelligent Systems: 20th International Conference on Hybrid Intelligent Systems (HIS 2020), 14\u201316 December 2020, Springer Nature.","DOI":"10.1007\/978-3-030-73050-5"},{"key":"ref_50","unstructured":"Bordia, P., and DiFonzo, N. (2013). The Psychology of Organizational Change: Viewing Change from the Employee\u2019s Perspective, Cambridge University Press."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1016\/j.tics.2021.02.007","article-title":"The psychology of fake news","volume":"25","author":"Pennycook","year":"2021","journal-title":"Trends Cogn. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Arisoy, C., Mandal, A., and Saxena, N. (2022, January 22\u201324). Human Brains Can\u2019t Detect Fake News: A Neuro-Cognitive Study of Textual Disinformation Susceptibility. Proceedings of the 2022 19th Annual International Conference on Privacy, Security & Trust (PST), IEEE, Fredericton, NB, Canada.","DOI":"10.1109\/PST55820.2022.9851990"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Giachanou, A., R\u00edssola, E.A., Ghanem, B., Crestani, F., and Rosso, P. (2020, January 24\u201326). The role of personality and linguistic patterns in discriminating between fake news spreaders and fact checkers. Proceedings of the Natural Language Processing and Information Systems: 25th International Conference on Applications of Natural Language to Information Systems, NLDB 2020, Saarbr\u00fccken, Germany. Proceedings 25.","DOI":"10.1007\/978-3-030-51310-8_17"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"114171","DOI":"10.1016\/j.eswa.2020.114171","article-title":"Linguistic feature based learning model for fake news detection and classification","volume":"169","author":"Choudhary","year":"2021","journal-title":"Expert Syst. Appl. May"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Shu, K., Cui, L., Wang, S., Lee, D., and Liu, H. (2019, January 4\u20138). defend: Explainable fake news detection. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA.","DOI":"10.1145\/3292500.3330935"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1013","DOI":"10.1108\/JD-12-2018-0209","article-title":"Disinformation and misinformation triangle: A conceptual model for \u201cfake news\u201d epidemic, causal factors and interventions","volume":"75","author":"Rubin","year":"2019","journal-title":"J. Doc."},{"key":"ref_57","first-page":"97","article-title":"Fake news as a two-dimensional phenomenon: A framework and research agenda","volume":"43","author":"Egelhofer","year":"2019","journal-title":"Ann. Int. Commun. Assoc."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1016\/j.jbusres.2020.11.037","article-title":"Fake news, social media and marketing: A systematic review","volume":"124","author":"Sit","year":"2021","journal-title":"J. Bus. Res."},{"key":"ref_59","unstructured":"Apostol, E.-S., Truic\u0103, C.-O., and Paschke, A. (2023). ContCommRTD: A Distributed Content-based Misinformation-aware Community Detection System for Real-Time Disaster Reporting. arXiv."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Truic\u0103, C.-O., Apostol, E.-S., Nicolescu, R.-C., and Karras, P. (2023). MCWDST: A Minimum-Cost Weighted Directed Spanning Tree Algorithm for Real-Time Fake News Mitigation in Social Media. arXiv.","DOI":"10.1109\/ACCESS.2023.3331220"},{"key":"ref_61","unstructured":"Coban, \u00d6., Truic\u0103, C.-O., and Apostol, E.-S. (2023). CONTAIN: A Community-based Algorithm for Network Immunization. arXiv."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1109\/TKDE.2015.2465378","article-title":"Node Immunization on Large Graphs: Theory and Algorithms","volume":"28","author":"Chen","year":"2016","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Petrescu, A., Truic\u0103, C.-O., Apostol, E.S., and Karras, P. (2021, January 1\u20135). Sparse Shield: Social Network Immunization vs. Harmful Speech. Proceedings of the 30th ACM International Conference on Information & Knowledge Management, New York, NY, USA.","DOI":"10.1145\/3459637.3482481"},{"key":"ref_64","first-page":"1","article-title":"Data-Aware Vaccine Allocation Over Large Networks","volume":"10","author":"Zhang","year":"2015","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"ref_65","unstructured":"Oshikawa, R., Qian, J., and Wang, W.Y. (2018). A survey on natural language processing for fake news detection. arXiv."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1145\/3137597.3137600","article-title":"Fake news detection on social media: A data mining perspective","volume":"19","author":"Shu","year":"2017","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1109\/MIS.2019.2899143","article-title":"Supervised learning for fake news detection","volume":"34","author":"Reis","year":"2019","journal-title":"IEEE Intell. Syst."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Alonso, M.A., Vilares, D., G\u00f3mez-Rodr\u00edguez, C., and Vilares, J. (2021). Sentiment analysis for fake news detection. Electronics, 10.","DOI":"10.3390\/electronics10111348"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Nadeem, M.I., Ahmed, K., Li, D., Zheng, Z., Alkahtani, H.K., Mostafa, S.M., Mamyrbayev, O., and Abdel Hameed, H. (2022). EFND: A semantic, visual, and socially augmented deep framework for extreme fake news detection. Sustainability, 15.","DOI":"10.3390\/su15010133"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"106991","DOI":"10.1016\/j.asoc.2020.106991","article-title":"Detecting fake news with capsule neural networks","volume":"101","author":"Goldani","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Huh, M., Liu, A., Owens, A., and Efros, A.A. (2018, January 8\u201314). Fighting fake news: Image splice detection via learned self-consistency. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01252-6_7"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Zhou, P., Han, X., Morariu, V.I., and Davis, L.S. (2018, January 18\u201323). Learning rich features for image manipulation detection. Proceedings of the IEEE Conference on Computer vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00116"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/5254.708428","article-title":"Support vector machines","volume":"13","author":"Hearst","year":"1998","journal-title":"IEEE Intell. Syst. Their Appl."},{"key":"ref_74","unstructured":"Duda, R.O., and Hart, P.E. (1973). Pattern Classification and Scene Analysis, Wiley. Available online: https:\/\/api.semanticscholar.org\/CorpusID:12946615."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Hosmer Jr, D.W., Lemeshow, S., and Sturdivant, R.X. (2013). Applied Logistic Regression, John Wiley & Sons.","DOI":"10.1002\/9781118548387"},{"key":"ref_76","unstructured":"Hunt, E.B., Marin, J., and Stone, P.J. (1966). Experiments in Induction, Academic Press."},{"key":"ref_77","unstructured":"Antony Vijay, J., Anwar Basha, H., and Arun Nehru, J. (2020). Computational Methods and Data Engineering: Proceedings of ICMDE 2020, Volume 2, Springer."},{"key":"ref_78","first-page":"1891","article-title":"Fake news detection based on word and document embedding using machine learning classifiers","volume":"99","author":"Eldesoky","year":"2021","journal-title":"J. Theor. Appl. Inf. Technol."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Lai, C.-M., Chen, M.-H., Kristiani, E., Verma, V.K., and Yang, C.-T. (2022). Fake News Classification Based on Content Level Features. Appl. Sci., 12.","DOI":"10.3390\/app12031116"},{"key":"ref_80","unstructured":"Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"162122","DOI":"10.1109\/ACCESS.2021.3132502","article-title":"Context-Aware Misinformation Detection: A Benchmark of Deep Learning Architectures Using Word Embeddings","volume":"9","author":"Ilie","year":"2021","journal-title":"IEEE Access"},{"key":"ref_82","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, New York, NY, USA."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.cogsys.2019.12.005","article-title":"FNDNet\u2013a deep convolutional neural network for fake news detection","volume":"61","author":"Kaliyar","year":"2020","journal-title":"Cogn. Syst. Res."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Huang, Q., Zhou, C., Wu, J., Wang, M., and Wang, B. (2019, January 14\u201319). Deep structure learning for rumor detection on twitter. Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), IEEE, Budapest, Hungary.","DOI":"10.1109\/IJCNN.2019.8852468"},{"key":"ref_85","unstructured":"Li, Y., Tarlow, D., Brockschmidt, M., and Zemel, R. (2015). Gated graph sequence neural networks. arXiv."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"115824","DOI":"10.1016\/j.eswa.2021.115824","article-title":"Distinguishing between fake news and satire with transformers","volume":"187","author":"Low","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Jwa, H., Oh, D., Park, K., Kang, J.M., and Lim, H. (2019). exbake: Automatic fake news detection model based on bidirectional encoder representations from transformers (bert). Appl. Sci., 9.","DOI":"10.3390\/app9194062"},{"key":"ref_88","unstructured":"Gundapu, S., and Mamidi, R. (2021). Transformer based Automatic COVID-19 Fake News Detection System. arXiv."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Truic\u0103, C.-O., and Apostol, E.-S. (2023). It\u2019s All in the Embedding! Fake News Detection Using Document Embeddings. Mathematics, 11.","DOI":"10.3390\/math11030508"},{"key":"ref_90","unstructured":"Truic\u0103, C.-O., Apostol, E.-S., and Paschke, A. (2022, January 5\u20138). Awakened at CheckThat! 2022: Fake News Detection using BiLSTM and Sentence Transformer. Proceedings of the CLEF 2022: Conference and Labs of the Evaluation Forum, Bologna, Italy."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Truic\u0103, C.-O., and Apostol, E.-S. (2022). MisRoB\u00c6RTa: Transformers versus Misinformation. Mathematics, 10.","DOI":"10.3390\/math10040569"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"11765","DOI":"10.1007\/s11042-020-10183-2","article-title":"FakeBERT: Fake news detection in social media with a BERT-based deep learning approach","volume":"80","author":"Kaliyar","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_93","unstructured":"Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach. arXiv."},{"key":"ref_94","unstructured":"Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., and Soricut, R. (2019). Albert: A lite bert for self-supervised learning of language representations. arXiv."},{"key":"ref_95","unstructured":"Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R.R., and Le, Q.V. (2019, January 8\u201314). Xlnet: Generalized autoregressive pretraining for language understanding. Proceedings of the Advances in Neural Information Processing Systems 32 (NeurIPS 2019), Vancouver, BC, Canada."},{"key":"ref_96","unstructured":"Gunel, B., Du, J., Conneau, A., and Stoyanov, V. (2020). Supervised contrastive learning for pre-trained language model fine-tuning. arXiv."},{"key":"ref_97","unstructured":"Nakamura, K., Levy, S., and Wang, W.Y. (2019). r\/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection. arXiv."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1007\/s13278-020-00689-w","article-title":"FauxWard: A Graph Neural Network Approach to Fauxtography Detection Using Social Media Comments","volume":"10","author":"Shang","year":"2020","journal-title":"Soc. Netw. Anal. Min."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1007\/s13735-017-0143-x","article-title":"Detection and visualization of misleading content on Twitter","volume":"7","author":"Boididou","year":"2018","journal-title":"Int. J. Multimed. Info. Retr."},{"key":"ref_100","unstructured":"Heller, S., Rossetto, L., and Schuldt, H. (2018). The PS-Battles Dataset\u2014An Image Collection for Image Manipulation Detection. arXiv."},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Thorne, J., Vlachos, A., Christodoulopoulos, C., and Mittal, A. (2018). FEVER: A large-scale dataset for Fact Extraction and VERification. arXiv.","DOI":"10.18653\/v1\/N18-1074"},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Ferreira, W., and Vlachos, A. (2016). Emergent: A Novel Data-Set for Stance Classification[EB\/OL], ACL. Available online: http:\/\/aclweb.org\/anthology\/N\/N16\/N16-1138.pdf.","DOI":"10.18653\/v1\/N16-1138"},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Popat, K., Mukherjee, S., Str\u00f6tgen, J., and Weikum, G. (2016, January 24\u201328). Credibility Assessment of Textual Claims on the Web. Proceedings of the 25th ACM international on conference on information and knowledge management, Indianapolis, IN, USA.","DOI":"10.1145\/2983323.2983661"},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Popat, K., Mukherjee, S., Yates, A., and Weikum, G. (2018). DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning. arXiv.","DOI":"10.18653\/v1\/D18-1003"},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Hanselowski, A., Stab, C., Schulz, C., Li, Z., and Gurevych, I. (2019). A Richly Annotated Corpus for Different Tasks in Automated Fact-Checking. arXiv.","DOI":"10.18653\/v1\/K19-1046"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Augenstein, I., Lioma, C., Wang, D., Lima, L.C., Hansen, C., Hansen, C., and Simonsen, J.G. (2019). MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims. arXiv.","DOI":"10.18653\/v1\/D19-1475"},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Thorne, J., Vlachos, A., Cocarascu, O., Christodoulopoulos, C., and Mittal, A. (2019, January 3). The FEVER2.0 Shared Task. Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER), Hong Kong, China.","DOI":"10.18653\/v1\/D19-6601"},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Aly, R., Guo, Z., Schlichtkrull, M., Thorne, J., Vlachos, A., Christodoulopoulos, C., Cocarascu, O., and Mittal, A. (2021). FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information. arXiv.","DOI":"10.18653\/v1\/2021.fever-1.1"},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Nakov, P., Barr\u00f3n-Cede\u00f1o, A., Elsayed, T., Suwaileh, R., M\u00e0rquez, L., Zaghouani, W., Atanasova, P., Kyuchukov, S., and Martino, G. (2018, January 10\u201314). Overview of the CLEF-2018 CheckThat! Lab on Automatic Identification and Verification of Political Claims. Proceedings of the 9th International Conference of the CLEF Association, CLEF 2018, Avignon, France.","DOI":"10.1007\/978-3-319-98932-7_32"},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Elsayed, T., Nakov, P., Barr\u00f3n-Cede\u00f1o, A., Hasanain, M., Suwaileh, R., Martino, G.D.S., and Atanasova, P. (2021). Overview of the CLEF-2019 CheckThat!: Automatic Identification and Verification of Claims. arXiv.","DOI":"10.1007\/978-3-030-28577-7_25"},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Barron-Cedeno, A., Elsayed, T., Nakov, P., Martino, G.D.S., Hasanain, M., Suwaileh, R., Haouari, F., Babulkov, N., Hamdan, B., and Nikolov, A. (2020, January 22\u201325). Overview of CheckThat! 2020: Automatic Identification and Verification of Claims in Social Media 2020. Proceedings of the 11th International Conference of the CLEF Association, CLEF 2020, Thessaloniki, Greece.","DOI":"10.1007\/978-3-030-58219-7_17"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Baly, R., Mohtarami, M., Glass, J., Marquez, L., Moschitti, A., and Nakov, P. (2018). Integrating Stance Detection and Fact Checking in a Unified Corpus. arXiv.","DOI":"10.18653\/v1\/N18-2004"},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Khouja, J. (2020). Stance Prediction and Claim Verification: An Arabic Perspective. arXiv.","DOI":"10.18653\/v1\/2020.fever-1.2"},{"key":"ref_114","unstructured":"N\u00f8rregaard, J., and Derczynski, L. (June, January 31). DanFEVER: Claim verification dataset for Danish. Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa), Reykjavik, Iceland (Online)."},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Kotonya, N., and Toni, F. (2020). Explainable Automated Fact-Checking for Public Health Claims. arXiv.","DOI":"10.18653\/v1\/2020.emnlp-main.623"},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Wadden, D., Lin, S., Lo, K., Wang, L.L., van Zuylen, M., Cohan, A., and Hajishirzi, H. (2020). Fact or Fiction: Verifying Scientific Claims. arXiv.","DOI":"10.18653\/v1\/2020.emnlp-main.609"},{"key":"ref_117","unstructured":"Lee, N., Bang, Y., Madotto, A., and Fung, P. (2020). Misinformation Has High Perplexity. arXiv."},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Hossain, T., Logan Iv, R.L., Ugarte, A., Matsubara, Y., Young, S., and Singh, S. (2020, January 20). COVIDLies: Detecting COVID-19 Misinformation on Social Media. Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020, Online.","DOI":"10.18653\/v1\/2020.nlpcovid19-2.11"},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Bordia, S., Zhong, Z., Dognin, C., Singh, M., and Bansal, M. (2020). HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verification. arXiv.","DOI":"10.18653\/v1\/2020.findings-emnlp.309"},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/978-3-030-73696-5_3","article-title":"Fighting an Infodemic: COVID-19 Fake News Dataset","volume":"Volume 1402","author":"Chakraborty","year":"2021","journal-title":"Combating Online Hostile Posts in Regional Languages during Emergency Situation"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1609\/icwsm.v9i1.14625","article-title":"CREDBANK: A Large-Scale Social Media Corpus With Associated Credibility Annotations","volume":"9","author":"Mitra","year":"2015","journal-title":"Proc. Int. AAAI Conf. Web Soc. Media"},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Zubiaga, A., Liakata, M., Procter, R., Hoi, G.W.S., and Tolmie, P. (2016). Analysing How People Orient to and Spread Rumours in Social Media by Looking at Conversational Threads. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0150989"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1609\/icwsm.v12i1.14985","article-title":"BuzzFace: A News Veracity Dataset with Facebook User Commentary and Egos","volume":"12","author":"Santia","year":"2018","journal-title":"Proc. Int. AAAI Conf. Web Soc. Media"},{"key":"ref_124","unstructured":"Singer-Vine, C.S., Strapagiel, L., Shaban, H., and Hall, E. (2023, August 26). Jeremy Hyperpartisan Facebook Pages Are Publishing False and Misleading Information at an Alarming Rate[EB\/OL]. Available online: https:\/\/www.buzzfeednews.com\/article\/craigsilverman\/partisan-fb-pages-analysis."},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Li, Y., Jiang, B., Shu, K., and Liu, H. (2020). MM-COVID: A Multilingual and Multimodal Data Repository for Combating COVID-19 Disinformation. arXiv.","DOI":"10.1109\/BigData50022.2020.9378472"},{"key":"ref_126","unstructured":"Hanselowski, A., PVS, A., Schiller, B., Caspelherr, F., Chaudhuri, D., Meyer, C.M., and Gurevych, I. (2018). A Retrospective Analysis of the Fake News Challenge Stance Detection Task. arXiv."},{"key":"ref_127","unstructured":"Szpakowski, M. (2023, August 26). Fake News Corpus[CP\/OL]. Available online: https:\/\/github.com\/several27\/FakeNewsCorpus."},{"key":"ref_128","unstructured":"Gruppi, M., Horne, B.D., and Adal\u0131, S. (2021). NELA-GT-2020: A Large Multi-Labelled News Dataset for The Study of Misinformation in News Articles. arXiv."},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Vlachos, A., and Riedel, S. (2014, January 26). Fact Checking: Task definition and dataset construction. Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, Baltimore, MD, USA.","DOI":"10.3115\/v1\/W14-2508"},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1609\/icwsm.v11i1.14976","article-title":"This Just In: Fake News Packs A Lot In Title, Uses Simpler, Repetitive Content in Text Body, More Similar To Satire Than Real News","volume":"11","author":"Horne","year":"2017","journal-title":"Proc. Int. AAAI Conf. Web Soc. Media"},{"key":"ref_131","unstructured":"Pathak, A., and Srihari, R. (August, January 28). BREAKING! Presenting Fake News Corpus for Automated Fact Checking. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, Florence, Italy."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1007\/978-3-319-69155-8_9","article-title":"Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques","volume":"Volume 10618","author":"Traore","year":"2017","journal-title":"Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments"},{"key":"ref_133","unstructured":"P\u00e9rez-Rosas, V., Kleinberg, B., Lefevre, A., and Mihalcea, R. (2017). Automatic Detection of Fake News. arXiv."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"2053951719843310","DOI":"10.1177\/2053951719843310","article-title":"Big Data and quality data for fake news and misinformation detection","volume":"6","author":"Taboada","year":"2019","journal-title":"Big Data Soc."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1609\/icwsm.v13i01.3254","article-title":"FA-KES: A Fake News Dataset around the Syrian War","volume":"13","author":"Elbassuoni","year":"2019","journal-title":"ICWSM"},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"4869","DOI":"10.3233\/JIFS-179034","article-title":"Detection of fake news in a new corpus for the Spanish language","volume":"36","author":"Sidorov","year":"2019","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_137","unstructured":"Shahi, G.K., and Nandini, D. (2020). FakeCovid\u2014A Multilingual Cross-domain Fact Check News Dataset for COVID-19. arXiv."},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"e1391","DOI":"10.1002\/widm.1391","article-title":"A historical perspective of explainable Artificial Intelligence","volume":"11","author":"Confalonieri","year":"2021","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"1808","DOI":"10.1080\/10447318.2022.2062113","article-title":"XFlag: Explainable fake news detection model on social media","volume":"38","author":"Chien","year":"2022","journal-title":"Int. J. Hum. Comput. Interact."},{"key":"ref_140","unstructured":"Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-K., and Woo, W. (2015, January 7\u201312). Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Proceedings of the Advances in Neural Information Processing Systems 28 (NIPS 2015), Montreal, QC, Canada."},{"key":"ref_141","unstructured":"Binder, A., Bach, S., Montavon, G., M\u00fcller, K.-R., and Samek, W. Proceedings of the Information Science and Applications (ICISA) 2016, Springer."},{"key":"ref_142","doi-asserted-by":"crossref","unstructured":"Wu, K., Yuan, X., and Ning, Y. (2021, January 11\u201314). Incorporating relational knowledge in explainable fake news detection. Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining, Virtual Event.","DOI":"10.1007\/978-3-030-75768-7_32"},{"key":"ref_143","unstructured":"Chen, M., Wang, N., and Subbalakshmi, K.P. (2020). Explainable rumor detection using inter and intra-feature attention networks. arXiv."},{"key":"ref_144","unstructured":"Qiao, Y., Wiechmann, D., and Kerz, E. (2020, January 13). A language-based approach to fake news detection through interpretable features and BRNN. Proceedings of the 3rd International Workshop on Rumours and Deception in Social Media (RDSM), Online."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"2673","DOI":"10.1109\/78.650093","article-title":"Bidirectional recurrent neural networks","volume":"45","author":"Schuster","year":"1997","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"102618","DOI":"10.1016\/j.ipm.2021.102618","article-title":"Propagation2Vec: Embedding partial propagation networks for explainable fake news early detection","volume":"58","author":"Silva","year":"2021","journal-title":"Inf. Process. Manag."},{"key":"ref_147","doi-asserted-by":"crossref","unstructured":"Yang, F., Pentyala, S.K., Mohseni, S., Du, M., Yuan, H., Linder, R., Ragan, E.D., Ji, S., and Hu, X. (2019, January 13\u201317). Xfake: Explainable fake news detector with visualizations. Proceedings of the The World Wide Web Conference, San Francisco, CA, USA.","DOI":"10.1145\/3308558.3314119"},{"key":"ref_148","doi-asserted-by":"crossref","unstructured":"Jin, Y., Wang, X., Yang, R., Sun, Y., Wang, W., Liao, H., and Xie, X. (2022). Towards Fine-Grained Reasoning for Fake News Detection. arXiv.","DOI":"10.1609\/aaai.v36i5.20517"},{"key":"ref_149","doi-asserted-by":"crossref","unstructured":"Kurasinski, L., and Mihailescu, R.-C. (2020, January 14\u201317). Towards machine learning explainability in text classification for fake news detection. Proceedings of the 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE, Miami, FL, USA.","DOI":"10.1109\/ICMLA51294.2020.00127"},{"key":"ref_150","unstructured":"Yang, Z., Ma, J., Chen, H., Lin, H., Luo, Z., and Chang, Y. (2022). A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection. arXiv."},{"key":"ref_151","doi-asserted-by":"crossref","unstructured":"Lu, Y.-J., and Li, C.-T. (2020). GCAN: Graph-aware co-attention networks for explainable fake news detection on social media. arXiv.","DOI":"10.18653\/v1\/2020.acl-main.48"},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"108378","DOI":"10.1016\/j.knosys.2022.108378","article-title":"A quantitative argumentation-based Automated eXplainable Decision System for fake news detection on social media","volume":"242","author":"Chi","year":"2022","journal-title":"Knowl. Based Syst."},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"106907","DOI":"10.1109\/ACCESS.2021.3100245","article-title":"MVAN: Multi-view attention networks for fake news detection on social media","volume":"9","author":"Ni","year":"2021","journal-title":"IEEE Access"},{"key":"ref_154","doi-asserted-by":"crossref","unstructured":"Tao, J., Lin, J., Zhang, S., Zhao, S., Wu, R., Fan, C., and Cui, P. (2019, January 4\u20138). Mvan: Multi-view attention networks for real money trading detection in online games. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA.","DOI":"10.1145\/3292500.3330687"},{"key":"ref_155","doi-asserted-by":"crossref","unstructured":"Raha, T., Choudhary, M., Menon, A., Gupta, H., Srivatsa, K.V., Gupta, M., and Varma, V. (2023). Neural models for Factual Inconsistency Classification with Explanations. arXiv.","DOI":"10.1007\/978-3-031-43418-1_25"},{"key":"ref_156","unstructured":"Bhattarai, B., Granmo, O.-C., and Jiao, L. (2021). Explainable tsetlin machine framework for fake news detection with credibility score assessment. arXiv."},{"key":"ref_157","unstructured":"Granmo, O.-C. (2018). The Tsetlin Machine\u2013A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logic. arXiv."},{"key":"ref_158","doi-asserted-by":"crossref","unstructured":"Fu, D., Ban, Y., Tong, H., Maciejewski, R., and He, J. (2022, January 17\u201321). DISCO: Comprehensive and explainable disinformation detection. Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Atlanta, GA, USA.","DOI":"10.1145\/3511808.3557202"},{"key":"ref_159","doi-asserted-by":"crossref","unstructured":"De Magistris, G., Russo, S., Roma, P., Starczewski, J.T., and Napoli, C. (2022). An explainable fake news detector based on named entity recognition and stance classification applied to COVID-19. Information, 13.","DOI":"10.3390\/info13030137"}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/11\/9\/458\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:45:57Z","timestamp":1760129157000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/11\/9\/458"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,4]]},"references-count":159,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["systems11090458"],"URL":"https:\/\/doi.org\/10.3390\/systems11090458","relation":{},"ISSN":["2079-8954"],"issn-type":[{"value":"2079-8954","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,4]]}}}