SubmissionNumber#=%=#263
FinalPaperTitle#=%=#HierarchyEverywhere at SemEval-2024 Task 4: Detection of Persuasion Techniques in Memes Using Hierarchical Text Classifier
ShortPaperTitle#=%=#
NumberOfPages#=%=#6
CopyrightSigned#=%=#EA
JobTitle#==#
Organization#==#
Abstract#==#Text classification is an important task in natural language processing. Hierarchical Text Classification (HTC) is a subset of text classification task-type. HTC tackles multi-label classification challenges by leveraging tree structures that delineate relationships between classes, thereby striving to enhance classification accuracy through the utilization of inter-class relationships. Memes, as prevalent vehicles of modern communication within social networks, hold immense potential as instruments for propagandistic dissemination due to their profound impact on users. In SemEval-2024 Task 4, the identification of propaganda and its various forms in memes is explored through two sub-tasks: (i) utilizing only the textual component of memes, and (ii) incorporating both textual and pictorial elements. In this study, we address the proposed problem through the lens of HTC, using state-of-the-art hierarchical text classification methodologies to detect propaganda in memes. Our system achieved first place in  English Sub-task 2a, underscoring its efficacy in tackling the complexities inherent in propaganda detection within the meme landscape.
Author{1}{Firstname}#=%=#Omid
Author{1}{Lastname}#=%=#Ghahroodi
Author{1}{Username}#=%=#omid_ghahroodi
Author{1}{Email}#=%=#oghahroodi98@gmail.com
Author{1}{Affiliation}#=%=#Sharif University of Technology / Language Processing and Digital Humanities Laboratory
Author{2}{Firstname}#=%=#Ehsaneddin
Author{2}{Lastname}#=%=#Asgari
Author{2}{Username}#=%=#asgari
Author{2}{Email}#=%=#asgari@berkeley.edu
Author{2}{Affiliation}#=%=#University of California, Berkeley

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