SubmissionNumber#=%=#79
FinalPaperTitle#=%=#BERTastic at SemEval-2024 Task 4: State-of-the-Art Multilingual Propaganda Detection in Memes via Zero-Shot Learning with Vision-Language Models
ShortPaperTitle#=%=#
NumberOfPages#=%=#8
CopyrightSigned#=%=#Tarek Haissam Mahmoud
JobTitle#==#
Organization#==#MBZUAI, Masdar City, Abu Dhabi, UAE
Abstract#==#Analyzing propagandistic memes in a multilingual, multimodal dataset is a challenging problem due to the inherent complexity of memes' multimodal content, which combines images, text, and often, nuanced context. In this paper, we use a VLM in a zero-shot approach to detect propagandistic memes and achieve a state-of-the-art average macro F1 of 66.7% over all languages. Notably, we outperform other systems on North Macedonian memes, and obtain competitive results on Bulgarian and Arabic memes. We also present our early fusion approach for identifying persuasion techniques in memes in a hierarchical multilabel classification setting. This approach outperforms all other approaches in average hierarchical precision with an average score of 77.66%. The systems presented contribute to the evolving field of research on the detection of persuasion techniques in multimodal datasets by offering insights that could be of use in the development of more effective tools for combating online propaganda.
Author{1}{Firstname}#=%=#Tarek
Author{1}{Lastname}#=%=#Mahmoud
Author{1}{Username}#=%=#tarrekko
Author{1}{Email}#=%=#tarek_haissam@outlook.com
Author{1}{Affiliation}#=%=#Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI)
Author{2}{Firstname}#=%=#Preslav
Author{2}{Lastname}#=%=#Nakov
Author{2}{Username}#=%=#preslav
Author{2}{Email}#=%=#preslav.nakov@gmail.com
Author{2}{Affiliation}#=%=#Mohamed bin Zayed University of Artificial Intelligence

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