ABSTRACT
During the corona pandemic misinformation has been increasingly spread on social media. Since the automatic verification of social media postings has shown to be challenging, there exists the need of classification systems, that can identify check-worthy posts in social media feeds. In this paper, the classification system used in the CLEF2022-CheckThat! Lab to detect check-worthiness in English tweets is presented. The proposed system, that took advantage of ensemble and semi-supervised learning showed promising results in the experimental evaluation. Further, first experiments were conducted with the novel framework lambeq to solve the classification problem using quantum natural language processing (QNLP), which were not part of the final model. The final model ranked fifth best in terms of the F1-score in the competition. © 2022 Copyright for this paper by its authors.