Cross-Lingual Fake News Detection with a Large Pre-Trained Transformer

Mina Schütz (Speaker), Jaqueline Böck, Medina-Petrina Andresel, Armin Kirchknopf, Daria Liakhovets, Djordje Slijepcevic, Alexander Schindler

Research output: Chapter in Book or Conference ProceedingsConference Proceedings with Oral Presentationpeer-review


The increase of fake news in today’s society, partially due to the accelerating digital transformation, is a
major problem in today’s world. This year’s CheckThat! Lab 2022 challenge addresses this problem as a
Natural Language Processing (NLP) task aiming to detect fake news in English and German texts. Within
this paper, we present our methodology and results for both, the monolingual (English) and cross-lingual
(German) tasks of the CheckThat! challenge in 2022. We applied the multilingual transformer model
XLM-RoBERTa to solve these tasks by pre-training the models on additional datasets and fine-tuning
them on the original data as well as its translations for the cross-lingual task. Our final model achieves a
macro F1-score of 15,48% and scores the 22𝑡ℎ rank in the benchmark. Regarding the second task, i.e., the
cross-lingual German classification, our final model achieves an F1-score of 19.46% and reaches the 4
rank in the benchmark.
Original languageEnglish
Title of host publicationProceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum
EditorsGuglielmo Faggioli, Nicola Ferro, Allan Hanbury, Martin Potthast
Number of pages10
Publication statusPublished - 2022
EventCheckThat! 2022 -
Duration: 5 Sept 20228 Sept 2022


ConferenceCheckThat! 2022

Research Field

  • Former Research Field - Data Science

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