Truică, Ciprian-OctavianCiprian-OctavianTruicăApostol, Elena-SimonaElena-SimonaApostolPaschke, AdrianAdrianPaschke2022-09-122022-09-122022https://publica.fraunhofer.de/handle/publica/4252682-s2.0-85137003249In recent years, online social networks and online news venues have become some of the main news and event-related information spreading mediums. Although using these mediums has facilitated the speed of accessing information, it also created a new phenomenon used for propaganda and disinformation: fake news. As fake news has detrimental consequences to society, new technologies need to be developed in order to stop their harmful effects. In this paper, we propose two Bidirectional Long Short-Term Memory (BiLSTM) architectures with sentence transformers to solve two tasks: (1) a multi-class mono-lingual fake news detection task (i.e., mono-lingual task); and (2) a multi-class cross-lingual fake news detection task (i.e., cross-lingual task). For the mono-lingual task, we train and test a BiLSTM with BART sentence transformers model on an English dataset and obtain an accuracy of ∼ 0.53 and an F1-Score of ∼ 0.32. For the cross-lingual task, we train a BiLSTM with XLM sentence transformers model on an English dataset and test the model using transfer learning on a German dataset. For this task, we obtain an accuracy of ∼ 0.28 and an F1-Score of ∼ 0.19.enfake news detectionneural networkssentence transformerstransfer learningDDC::000 Informatik, Informationswissenschaft, allgemeine Werke::000 Informatik, Wissen, Systeme::006 Spezielle ComputerverfahrenDDC::300 Sozialwissenschaften::380 Handel, Kommunikation, Verkehr::384 Kommunikation, Telekommunikationconference paper