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The impact of information is overpowering and can lead to many socially undesirable phenomena, such as panic or political instability. To eliminate the threats of fake news publishing, modern computer security systems need flexible and intelligent tools. The design of models meeting the above-mentioned criteria is enabled by artificial intelligence and, above all, by the state-of-the-art neural network architectures, applied in NLP tasks. The BERT neural network belongs to this type of architectures. This paper presents Transformer-based hybrid architectures applied to create models for detecting fake news.<\/jats:p>","DOI":"10.1007\/s00521-021-06276-0","type":"journal-article","created":{"date-parts":[[2021,7,22]],"date-time":"2021-07-22T11:03:03Z","timestamp":1626951783000},"page":"20449-20461","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Implementation of the BERT-derived architectures to tackle disinformation challenges"],"prefix":"10.1007","volume":"34","author":[{"given":"Sebastian","family":"Kula","sequence":"first","affiliation":[]},{"given":"Rafa\u0142","family":"Kozik","sequence":"additional","affiliation":[]},{"given":"Micha\u0142","family":"Chora\u015b","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,7,22]]},"reference":[{"issue":"1","key":"6276_CR1","doi-asserted-by":"publisher","first-page":"e9","DOI":"10.1002\/spy2.9","volume":"1","author":"H Ahmed","year":"2018","unstructured":"Ahmed H, Traore I, Saad S (2018) Detecting opinion spams and fake news using text classification. 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