{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T01:34:04Z","timestamp":1791855244525,"version":"4.3.4"},"reference-count":36,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,3,3]],"date-time":"2022-03-03T00:00:00Z","timestamp":1646265600000},"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>In the age of the digital revolution and the widespread usage of social networks, the modalities of information consumption and production were disrupted by the shift to instantaneous transmission. Sometimes the scoop and exclusivity are just for a few minutes. Information spreads like wildfire throughout the world, with little regard for context or critical thought, resulting in the proliferation of fake news. As a result, it is preferable to have a system that allows consumers to obtain balanced news information. Some researchers attempted to detect false and authentic news using tagged data and had some success. Online social groups propagate digital false news or fake news material in the form of shares, reshares, and repostings. This work aims to detect fake news forms dispatched on social networks to enhance the quality of trust and transparency in the social network recommendation system. It provides an overview of traditional techniques used to detect fake news and modern approaches used for multiclassification using unlabeled data. Many researchers are focusing on detecting fake news, but fewer works highlight this detection\u2019s role in improving the quality of trust in social network recommendation systems. In this research paper, we take an improved approach to assisting users in deciding which information to read by alerting them about the degree of inaccuracy of the news items they are seeing and recommending the many types of fake news that the material represents.<\/jats:p>","DOI":"10.3390\/info13030128","type":"journal-article","created":{"date-parts":[[2022,3,3]],"date-time":"2022-03-03T09:24:53Z","timestamp":1646299493000},"page":"128","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Towards the Detection of Fake News on Social Networks Contributing to the Improvement of Trust and Transparency in Recommendation Systems: Trends and Challenges"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2454-0752","authenticated-orcid":false,"given":"Oumaima","family":"Stitini","sequence":"first","affiliation":[{"name":"Computer and System Engineering Laboratory, Faculty of Science and Technology, Cadi Ayyad University, Marrakesh 40000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Soulaimane","family":"Kaloun","sequence":"additional","affiliation":[{"name":"Computer and System Engineering Laboratory, Faculty of Science and Technology, Cadi Ayyad University, Marrakesh 40000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Omar","family":"Bencharef","sequence":"additional","affiliation":[{"name":"Computer and System Engineering Laboratory, Faculty of Science and Technology, Cadi Ayyad University, Marrakesh 40000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.procs.2018.10.495","article-title":"Detecting rumors in social media: A survey","volume":"142","author":"Alzanin","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/s41060-017-0088-4","article-title":"Deep learning for detecting inappropriate content in text","volume":"6","author":"Yenala","year":"2018","journal-title":"Int. J. Data Sci. Anal."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ahmed, M.B., Mellouli, S., Braganca, L., Abdelhakim, B.A., and Bernadetta, K.A. (2021). Artificial Intelligence in Predicting the Spread of Coronavirus to Ensure Healthy Living for All Age Groups. Emerging Trends in ICT for Sustainable Development, Springer International Publishing.","DOI":"10.1007\/978-3-030-53440-0"},{"key":"ref_4","unstructured":"Oumaima, S. (2021, August 23). How Can We Analyse Emotions on Twitter during an Epidemic Situation? A Features Engineering Approach to Evaluate People\u2019s Emotions during The COVID-19 Pandemic. Available online: https:\/\/doi.org\/10.17605\/OSF.IO\/U9H52."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"de Oliveira, N.R., Pisa, P.S., Lopez, M.A., de Medeiros, D.S.V., and Mattos, D.M.F. (2021). Identifying Fake News on Social Networks Based on Natural Language Processing: Trends and Challenges. Information, 12.","DOI":"10.3390\/info12010038"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"40416","DOI":"10.1109\/ACCESS.2019.2897586","article-title":"Recommendation Based on Review Texts and Social Communities: A Hybrid Model","volume":"7","author":"Ji","year":"2019","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Hassan, T., and McCrickard, D.S. (2019, January 13\u201317). Trust and Trustworthiness in Social Recommender Systems. Proceedings of the 2019 World Wide Web Conference, San Francisco, CA, USA.","DOI":"10.1145\/3308560.3317596"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Collins, B., Hoang, D.T., Nguyen, N.T., and Hwang, D. (2020). Trends in combating fake news on social media\u2014A survey. J. Inf. Telecommun., 1\u201320.","DOI":"10.1080\/24751839.2020.1847379"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Li, Q., Zhang, Q., Si, L., and Liu, Y. (2019). Rumor Detection on Social Media: Datasets, Methods and Opportunities. arXiv.","DOI":"10.18653\/v1\/D19-5008"},{"key":"ref_10","unstructured":"Heuer, H., and Breiter, A. (October, January 29). Trust in news on social media. Proceedings of the 10th Nordic Conference on Human-Computer Interaction, Oslo, Norway."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wu, L., Rao, Y., Yu, H., Wang, Y., and Nazir, A. (2018, January 25\u201328). False Information Detection on Social Media via a Hybrid Deep Model. Proceedings of the International Conference on Social Informatics, Saint-Petersburg, Russia.","DOI":"10.1007\/978-3-030-01159-8_31"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Imran, M., Castillo, C., Diaz, F., and Vieweg, S. (2018, January 23\u201327). Processing Social Media Messages in Mass Emergency: Survey Summary. Proceedings of the The Web Conference 2018, Lyon, France.","DOI":"10.1145\/3184558.3186242"},{"key":"ref_13","first-page":"240","article-title":"The Recommendation of a Practical Guide for Doctoral Students Using Recommendation System Algorithms in the Education Field","volume":"Volume 4","author":"Ahmed","year":"2021","journal-title":"Innovations in Smart Cities Applications"},{"key":"ref_14","unstructured":"Oumaima, S., Soulaimane, K., and Omar, B. (April, January 31). Latest Trends in Recommender Systems Applied in the Medical Domain: A Systematic Review. Proceedings of the 3rd International Conference on Networking, Information Systems & Security, Marrakech, Morocco."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3647","DOI":"10.1007\/s13042-019-00951-4","article-title":"A multiclass boosting algorithm to labeled and unlabeled data","volume":"10","author":"Tanha","year":"2019","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.patrec.2018.03.031","article-title":"Learning error-correcting graph matching with a multiclass neural network","volume":"134","author":"Martineau","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_17","unstructured":"Kaneko, T., Sato, I., and Sugiyama, M. (2019). Online Multiclass Classification Based on Prediction Margin for Partial Feedback. arXiv."},{"key":"ref_18","unstructured":"Tayyaba, R., Wasi, H.B., Arslan, S., and Usman, A.M. (2019, January 23\u201325). Multi-Label Fake News Detection using Multi-layered Supervised Learning. Proceedings of the 2019 11th International Conference on Computer and Automation Engineering (ICCAE 2019), New York, NY, USA."},{"key":"ref_19","unstructured":"Vijayaraghavan, S., Wang, Y., Guo, Z., Voong, J., Xu, W., Nasseri, A., Cai, J., Li, L., Vuong, K., and Wadhwa, E. (2020). Fake News Detection with Different Models. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Leonardi, S., Rizzo, G., and Morisio, M. (2021). Automated Classification of Fake News Spreaders to Break the Misinformation Chain. Information, 12.","DOI":"10.3390\/info12060248"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Duradoni, M., Collodi, S., Perfumi, S.C., and Guazzini, A. (2021). Reviewing Stranger on the Internet: The Role of Identifiability through \u201cReputation\u201d in Online Decision Making. Future Int., 13.","DOI":"10.3390\/fi13050110"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Duradoni, M., Paolucci, M., Bagnoli, F., and Guazzini, A. (2018). Fairness and Trust in Virtual Environments: The Effects of Reputation. Future Int., 10.","DOI":"10.3390\/fi10060050"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1002\/hbe2.170","article-title":"Reputation Matters the Most: The Reputation Inertia Effect","volume":"2","author":"Duradoni","year":"2020","journal-title":"Hum. Behav. Emerg. Technol."},{"key":"ref_24","unstructured":"Gao, P., Baras, J., and Golbeck, J. (2018). Trust-aware Social Recommender System Design. Doctor Consortium of 2015 International Conference On Information Systems Security and Privacy, Science and Technology Publications, Lda."},{"key":"ref_25","unstructured":"Dong, M., Yuan, F., Yao, L., Wang, X., Xu, X., and Zhu, L. (2020). Trust in Recommender Systems: A Deep Learning Perspective. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1387","DOI":"10.18517\/ijaseit.10.4.10883","article-title":"The Role of Trust to Enhance the Recommendation System Based on Social Network","volume":"10","author":"Tharwat","year":"2020","journal-title":"Int. J. Adv. Sci. Eng. Inf. Technol."},{"key":"ref_27","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), Miami, FL, USA.","DOI":"10.1109\/MIPR.2018.00092"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.ins.2018.02.030","article-title":"Reliability quality measures for recommender systems","volume":"442\u2013443","author":"Bobadilla","year":"2018","journal-title":"Inf. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Gereme, F., Zhu, W., Ayall, T., and Alemu, D. (2021). Combating Fake News in \u201cLow-Resource\u201d Languages: Amharic Fake News Detection Accompanied by Resource Crafting. Information, 12.","DOI":"10.3390\/info12010020"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kasnesis, P., Toumanidis, L., and Patrikakis, C.Z. (2021). Combating Fake News with Transformers: A Comparative Analysis of Stance Detection and Subjectivity Analysis. Information, 12.","DOI":"10.3390\/info12100409"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Galal, S., Nagy, N., and El-Sharkawi, M.E. (2021). CNMF: A Community-Based Fake News Mitigation Framework. Information, 12.","DOI":"10.3390\/info12090376"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Qian, F., Gong, C., Sharma, K., and Liu, Y. (2018, January 13\u201319). Neural user response generator: Fake news detection with collective user intelligence. Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI\u201918), Stockholm, Sweden.","DOI":"10.24963\/ijcai.2018\/533"},{"key":"ref_33","unstructured":"(2021, February 24). Fact Checking. Available online: https:\/\/hrashkin.github.io\/factcheck.html."},{"key":"ref_34","unstructured":"(2021, February 24). Getting Real about Fake News. Available online: https:\/\/www.kaggle.com\/mrisdal\/fake-news\/data."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Granik, M., and Mesyura, V. (June, January 29). Fake news detection using naive Bayes classifier. Proceedings of the 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON), Kyiv, Ukraine.","DOI":"10.1109\/UKRCON.2017.8100379"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Lyu, S., and Lo, D.C.T. (2020, January 28\u201329). Fake News Detection by Decision Tree. Proceedings of the 2020 Southeast Con, Raleigh, NC, USA.","DOI":"10.1109\/SoutheastCon44009.2020.9249688"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/13\/3\/128\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:31:08Z","timestamp":1760135468000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/13\/3\/128"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,3]]},"references-count":36,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["info13030128"],"URL":"https:\/\/doi.org\/10.3390\/info13030128","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,3]]}}}