@inproceedings{KoehlerShahiStrussetal., author = {K{\"o}hler, Juliane and Shahi, Gautam Kishore and Struß, Julia Maria and Wiegand, Michael and Siegel, Melanie and Mandl, Thomas and Sch{\"u}tz, Mina}, title = {Overview of the CLEF-2022 CheckThat! Lab: Task 3 on Fake News Detection}, series = {CLEF 2022 Working Notes : Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum}, booktitle = {CLEF 2022 Working Notes : Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum}, editor = {Faggiolo, Guglielmo and Ferro, Nicola and Hanburry, Allan and Potthast, Martin}, address = {Bologna}, organization = {University of Bologna}, issn = {1613-0073}, url = {http://nbn-resolving.de/urn:nbn:de:0074-3180-7}, pages = {404 -- 421}, abstract = {This paper describes the results of the CheckThat! Lab 2022 Task 3. This is the fifth edition of the lab, which concentrates on the evaluation of technologies supporting three tasks related to factuality. Task 3 is designed as a multi-class classification problem and focuses on the veracity of German and English news articles. The German subtask is ought to be solved using an cross-lingual approach while the English subtask was offered as mono-lingual task. The participants of the lab were provided an English training, development and test dataset as well as a German test dataset. In total, 25 teams submitted successful runs for the English subtask and 8 for the German subtask. The best performing system for the mono-lingual subtask achieved a macro F1-score of 0.339. The best system for the cross-lingual task achieved a macro F1-score of 0.242. In the paper at hand we will elaborate on the process of data collection, the task setup, the evaluation results and give a brief overview of the participating systems.}, subject = {Deep Learning}, language = {en} }