SubmissionNumber#=%=#299 FinalPaperTitle#=%=#SemEval-2024 Task 4: Multilingual Detection of Persuasion Techniques in Memes ShortPaperTitle#=%=# NumberOfPages#=%=#18 CopyrightSigned#=%=#Dimitar Dimitrov JobTitle#==# Organization#==# Abstract#==#The automatic identification of misleading and persuasive content has emerged as a significant issue among various stakeholders, including social media platforms, policymakers, and the broader society. To tackle this issue within the context of memes, we organized a shared task at SemEval-2024, focusing on the multilingual detection of persuasion techniques. This paper outlines the dataset, the organization of the task, the evaluation framework, the outcomes, and the systems that participated. The task targets memes in four languages, with the inclusion of three surprise test datasets in Bulgarian, North Macedonian, and Arabic. It encompasses three subtasks: (i) identifying whether a meme utilizes a persuasion technique; (ii) identifying persuasion techniques within the meme's ''textual content''; and (iii) identifying persuasion techniques across both the textual and visual components of the meme (a multimodal task). Furthermore, due to the complex nature of persuasion techniques, we present a hierarchy that groups the 22 persuasion techniques into several levels of categories. This became one of the attractive shared tasks in SemEval 2024, with 153 teams registered, 48 teams submitting results, and finally, 32 system description papers submitted. Author{1}{Firstname}#=%=#Dimitar Iliyanov Author{1}{Lastname}#=%=#Dimitrov Author{1}{Username}#=%=#didimitrov Author{1}{Email}#=%=#mitko.bg.ss@gmail.com Author{1}{Affiliation}#=%=#University of Sofia "St. Kliment Ohridski" Author{2}{Firstname}#=%=#Firoj Author{2}{Lastname}#=%=#Alam Author{2}{Username}#=%=#firojalam Author{2}{Email}#=%=#firojalam@gmail.com Author{2}{Affiliation}#=%=#Qatar Computing Research Institute, HBKU Author{3}{Firstname}#=%=#Maram Author{3}{Lastname}#=%=#Hasanain Author{3}{Username}#=%=#maramhasanain Author{3}{Email}#=%=#maramhasanain@gmail.com Author{3}{Affiliation}#=%=#Qatar Computing Research Institute Author{4}{Firstname}#=%=#Abul Author{4}{Lastname}#=%=#Hasnat Author{4}{Username}#=%=#hasnat Author{4}{Email}#=%=#mhasnat@gmail.com Author{4}{Affiliation}#=%=#Blackbird.ai Author{5}{Firstname}#=%=#Fabrizio Author{5}{Lastname}#=%=#Silvestri Author{5}{Username}#=%=#silvestr Author{5}{Email}#=%=#fabrizio.silvestri@gmail.com Author{5}{Affiliation}#=%=#Sapienza, University of Rome Author{6}{Firstname}#=%=#Preslav Author{6}{Lastname}#=%=#Nakov Author{6}{Username}#=%=#preslav Author{6}{Email}#=%=#preslav.nakov@gmail.com Author{6}{Affiliation}#=%=#Mohamed bin Zayed University of Artificial Intelligence Author{7}{Firstname}#=%=#Giovanni Author{7}{Lastname}#=%=#Da San Martino Author{7}{Username}#=%=#gmartino Author{7}{Email}#=%=#joedsm@gmail.com Author{7}{Affiliation}#=%=#University of Padova ========== èéáğö