{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T14:34:24Z","timestamp":1780497264465,"version":"3.54.1"},"reference-count":55,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,3,14]],"date-time":"2022-03-14T00:00:00Z","timestamp":1647216000000},"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>This paper proposes a supervised machine learning system to detect fake news in online sources published in Romanian. Additionally, this work presents a comparison of the obtained results by using recurrent neural networks based on long short-term memory and gated recurrent unit cells, a convolutional neural network, and a Bidirectional Encoder Representations from Transformers (BERT) model, namely RoBERT, a pre-trained Romanian BERT model. The deep learning architectures are compared with the results achieved by two classical classification algorithms: Na\u00efve Bayes and Support Vector Machine. The proposed approach is based on a Romanian news corpus containing 25,841 true news items and 13,064 fake news items. The best result is over 98.20%, achieved by the convolutional neural network, which outperforms the standard classification methods and the BERT models. Moreover, based on irony detection and sentiment analysis systems, additional details are revealed about the irony phenomenon and sentiment analysis field which are used to tackle fake news challenges.<\/jats:p>","DOI":"10.3390\/info13030151","type":"journal-article","created":{"date-parts":[[2022,3,15]],"date-time":"2022-03-15T03:06:10Z","timestamp":1647313570000},"page":"151","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Automatic Fake News Detection for Romanian Online News"],"prefix":"10.3390","volume":"13","author":[{"given":"Marius Cristian","family":"Buzea","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, University Politehnica of Bucharest, 060042 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8082-8497","authenticated-orcid":false,"given":"Stefan","family":"Trausan-Matu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University Politehnica of Bucharest, 060042 Bucharest, Romania"},{"name":"Research Institute for Artificial Intelligence \u201cMihai Draganescu\u201d of the Romanian Academy, 050711 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7255-5537","authenticated-orcid":false,"given":"Traian","family":"Rebedea","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University Politehnica of Bucharest, 060042 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,14]]},"reference":[{"key":"ref_1","unstructured":"Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., and Askell, A. 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