{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T11:40:17Z","timestamp":1778067617200,"version":"3.51.4"},"reference-count":33,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,3,7]],"date-time":"2022-03-07T00:00:00Z","timestamp":1646611200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Over the last few years, the phenomenon of fake news has become an important issue, especially during the worldwide COVID-19 pandemic, and also a serious risk for the public health. Due to the huge amount of information that is produced by the social media such as Facebook and Twitter it is becoming difficult to check the produced contents manually. This study proposes an automatic fake news detection system that supports or disproves the dubious claims while returning a set of documents from verified sources. The system is composed of multiple modules and it makes use of different techniques from machine learning, deep learning and natural language processing. Such techniques are used for the selection of relevant documents, to find among those, the ones that are similar to the tested claim and their stances. The proposed system will be used to check medical news and, in particular, the trustworthiness of posts related to the COVID-19 pandemic, vaccine and cure.<\/jats:p>","DOI":"10.3390\/info13030137","type":"journal-article","created":{"date-parts":[[2022,3,7]],"date-time":"2022-03-07T10:21:16Z","timestamp":1646648476000},"page":"137","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["An Explainable Fake News Detector Based on Named Entity Recognition and Stance Classification Applied to COVID-19"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3076-4509","authenticated-orcid":false,"given":"Giorgio","family":"De Magistris","sequence":"first","affiliation":[{"name":"Department of Computer, Automation and Management Engineering, Sapienza University of Rome, via Ariosto 25, 00185 Roma, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1846-9996","authenticated-orcid":false,"given":"Samuele","family":"Russo","sequence":"additional","affiliation":[{"name":"Department of Psychology, Sapienza University of Rome, via dei Marsi 78, 00185 Roma, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1031-0948","authenticated-orcid":false,"given":"Paolo","family":"Roma","sequence":"additional","affiliation":[{"name":"Department of Human Neurosciences, Sapienza Univesity of Rome, Piazzale Aldo Moro 5, 00185 Roma, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4694-7868","authenticated-orcid":false,"given":"Janusz T.","family":"Starczewski","sequence":"additional","affiliation":[{"name":"Department of Intelligent Computer Systems, Czestochowa University of Technology, al.Armii Krajowej 36, 42-200C Czestochowa, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3336-5853","authenticated-orcid":false,"given":"Christian","family":"Napoli","sequence":"additional","affiliation":[{"name":"Department of Computer, Automation and Management Engineering, Sapienza University of Rome, via Ariosto 25, 00185 Roma, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1257\/jep.31.2.211","article-title":"Social media and fake news in the 2016 election","volume":"31","author":"Allcott","year":"2017","journal-title":"J. Econ. Perspect."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"84","DOI":"10.22329\/il.v38i1.5068","article-title":"Fake news: A definition","volume":"38","author":"Gelfert","year":"2018","journal-title":"Informal Log."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Gall\u00e8, F., Veshi, A., Sabella, E.A., \u00c7itozi, M., Da Molin, G., Ferracuti, S., Liguori, G., Orsi, G.B., Napoli, C., and Napoli, C. (2021). Awareness and Behaviors Regarding COVID-19 among Albanian Undergraduates. Behav. Sci., 11.","DOI":"10.3390\/bs11040045"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"eaay7120","DOI":"10.1126\/scirobotics.aay7120","article-title":"XAI\u2014Explainable artificial intelligence","volume":"4","author":"Gunning","year":"2019","journal-title":"Sci. Robot."},{"key":"ref_5","unstructured":"Oshikawa, R., Qian, J., and Wang, W.Y. (2018). A survey on natural language processing for fake news detection. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wang, W.Y. (2017). \u201c liar, liar pants on fire\u201d: A new benchmark dataset for fake news detection. arXiv.","DOI":"10.18653\/v1\/P17-2067"},{"key":"ref_7","unstructured":"Long, Y. (December, January 27). Fake news detection through multi-perspective speaker profiles. Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), Taipei, Taiwan."},{"key":"ref_8","unstructured":"Pham, T.T. (2022, January 04). A Study on Deep Learning for Fake News Detection. Available online: https:\/\/dspace.jaist.ac.jp\/dspace\/bitstream\/10119\/15196\/3\/paper.pdf."},{"key":"ref_9","unstructured":"Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., and Manning, C.D. (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_11","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_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/pra2.2015.145052010082","article-title":"Automatic deception detection: Methods for finding fake news","volume":"52","author":"Conroy","year":"2015","journal-title":"Proc. Assoc. Inf. Sci. Technol."},{"key":"ref_13","unstructured":"Dungs, S., Aker, A., Fuhr, N., and Bontcheva, K. (2018, January 20\u201326). Can rumour stance alone predict veracity?. Proceedings of the 27th International Conference on Computational Linguistics, Santa Fe, NM, USA."},{"key":"ref_14","unstructured":"Tacchini, E., Ballarin, G., Della Vedova, M.L., Moret, S., and de Alfaro, L. (2017). Some like it hoax: Automated fake news detection in social networks. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Gupta, M., Zhao, P., and Han, J. (2012, January 25). Evaluating event credibility on twitter. Proceedings of the 2012 SIAM International Conference on Data Mining, California, CA, USA.","DOI":"10.1137\/1.9781611972825.14"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Jin, Z., Cao, J., Jiang, Y.G., and Zhang, Y. (2014, January 14\u201317). News credibility evaluation on microblog with a hierarchical propagation model. Proceedings of the 2014 IEEE International Conference on Data Mining, Shenzhen, China.","DOI":"10.1109\/ICDM.2014.91"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Jin, Z., Cao, J., Zhang, Y., and Luo, J. (2016, January 12\u201317). News verification by exploiting conflicting social viewpoints in microblogs. Proceedings of the AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA.","DOI":"10.1609\/aaai.v30i1.10382"},{"key":"ref_18","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_19","doi-asserted-by":"crossref","unstructured":"Ferreira, W., and Vlachos, A. (2016, January 12\u201317). Emergent: A novel data-set for stance classification. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego, CA, USA.","DOI":"10.18653\/v1\/N16-1138"},{"key":"ref_20","unstructured":"Yufeng (2022, January 04). BBC Articles Fulltext and Category. Available online: https:\/\/www.kaggle.com\/yufengdev\/bbc-fulltext-and-category\/code."},{"key":"ref_21","unstructured":"Byron Galbraith, D.R. (2022, January 04). Fake News Challenge FNC-1. Available online: http:\/\/www.fakenewschallenge.org\/."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Dietterich, T.G. (2000). Ensemble methods in machine learning. International Workshop on Multiple Classifier Systems, Springer.","DOI":"10.1007\/3-540-45014-9_1"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/TKDE.2020.2981314","article-title":"A survey on deep learning for named entity recognition","volume":"34","author":"Li","year":"2020","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","article-title":"BioBERT: A pre-trained biomedical language representation model for biomedical text mining","volume":"36","author":"Lee","year":"2020","journal-title":"Bioinformatics"},{"key":"ref_26","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention is all you need. arXiv."},{"key":"ref_27","unstructured":"Le, Q., and Mikolov, T. (2014, January 2\u201324). Distributed representations of sentences and documents. Proceedings of the International Conference on Machine Learning, PMLR, Bejing, China."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lau, J.H., and Baldwin, T. (2016). An empirical evaluation of doc2vec with practical insights into document embedding generation. arXiv.","DOI":"10.18653\/v1\/W16-1609"},{"key":"ref_29","unstructured":"Dai, A.M., Olah, C., and Le, Q.V. (2015). Document embedding with paragraph vectors. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1646","DOI":"10.1016\/j.procs.2018.05.132","article-title":"Stance-in-depth deep neural approach to stance classification","volume":"132","author":"Rajendran","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. arXiv.","DOI":"10.3115\/v1\/D14-1181"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wei, W., Zhang, X., Liu, X., Chen, W., and Wang, T. (2016, January 16\u201317). pkudblab at SemEval-2016 Task 6: A Specific Convolutional Neural Network System for Effective Stance Detection. Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), San Diego, CA, USA.","DOI":"10.18653\/v1\/S16-1062"},{"key":"ref_33","unstructured":"Julio, A., Saenz, S.R.K.G., and Shukla, D. (2022, January 04). CoVID-19 Fake News Infodemic Research (CoVID19-FNIR) Dataset. Available online: https:\/\/ieee-dataport.org\/open-access\/covid-19-fake-news-infodemic-research-dataset-covid19-fnir-dataset."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/13\/3\/137\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:33:14Z","timestamp":1760135594000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/13\/3\/137"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,7]]},"references-count":33,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["info13030137"],"URL":"https:\/\/doi.org\/10.3390\/info13030137","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,7]]}}}