@inproceedings{,
    author = {Blanc, Olivier; Pritzkau, Albert; Schade, Ulrich; Geierhos, Michaela},
    title = {CODE at CheckThat! 2022: Multi-class fake news detection of news articles with BERT},
    editor = {Faggioli, Guglielmo; Ferro, Nicola; Hanbury, Allan; Potthast, Martin},
    booktitle = {Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum : Bologna, Italy, September 5th to 8th, 2022},
    series = {CEUR Workshop Proceedings},
    journal = {},
    address = {},
    publisher = {},
    edition = {},
    year = {2022},
    isbn = {1613-0073},
    volume = {3180},
    number =  {},
    pages = {444-455},
    url = {http://ceur-ws.org/Vol-3180/http://ceur-ws.org/Vol-3180/paper-34.pdf},
    doi = {},
    keywords = {Sequence Classification ; Deep Learning ; Transformers ; BERT},
    abstract = {The following system description presents our approach for detecting fake news in texts. The given task was formulated as a multi-class classification problem. Our approach is based on the combination of two BERT-based classification models: One model determines whether the textual content is relevant to the task; the second model assigns it a truth value. Starting from a pre-trained model for language representation, we fine-tuned these models on the given classification task in supervised training steps using the annotated data provided.},
    note = {},
    institution = {Universität der Bundeswehr München, Fakultät für Informatik, INF 7 - Institut für Datensicherheit, Professur: Geierhos, Michaela},
    
    
}