H ierarchy E verywhere at S em E val-2024 Task 4: Detection of Persuasion Techniques in Memes Using Hierarchical Text Classifier (2024)

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.

## introduction 1.propaganda techniques in memes propaganda can be defined as the deliberate dissemination of information, often with a biased or misleading nature, aimed at promoting or publicizing a particular political cause, ideology, or viewpoint. this communication tactic takes various forms, including persuasive messaging, advertising campaigns, and the dissemination of ideas through media channels. the primary objective of propaganda is to influence people's beliefs, attitudes, or behaviors towards a specific agenda or ideology. examples of propaganda can range from political advertisements designed to sway voters, to ideological messaging spread through social media platforms. memes have emerged as one of the most prevalent communication tools in digital media. their utilization of both text and image allows for the transmission of substantial information, underscoring the critical need for detecting propaganda within them. ## system overview in sub-task 1, we addressed the proposed problem using hierarchical text classification and used a state-of-the-art (sota) htc model for propaganda technique detection with some modifications. we utilized hpt @xcite as the hierarchical text classifier. convert task to htc problem: the hierarchical structure of propaganda techniques was represented as a directed acyclic graph (dag). the hierarchy of propaganda techniques is depicted in figure 1 . to use the hpt model @xcite , it was imperative to transform this dag into a hierarchical tree. this transformation involved converting nodes with multiple parents into new nodes. for instance, the node "whataboutism" with two parents, "distraction" and "ad hominem" was split into two nodes labeled "distraction_whataboutism" and "ad hominem_whataboutism". two methods can be employed for organizing the first level of the hierarchy tree: (1) placing two nodes labeled "propagandistic" and "non-propagandistic" at the initial level, followed by the entire hierarchy of propaganda techniques under the "propagandistic" node, or (2) directly utilizing the hierarchy tree without this initial categorization. our observations indicate that method 1 yields superior performance. ## experimental setup the organizers provided the data in three parts: training, evaluation, and testing sets. we employed the hpt model, utilizing the bert-baseuncased language model for our study. for training purposes, we combine the training and evaluation data, randomly picking 10% for evaluation, and reserving the remaining 90% for training. our training comprised a batch size of 8 and a learning rate of 3e-5. the remaining hyperparameters are similar to the hpt paper. to use additional data, we initially trained the model on this additional dataset before continuing training on the task data. ## results the results for sub-tasks 1 and 2a are presented in however, our system encountered challenges in non-english sub-tasks, displaying poor performance. we attribute this to potential translation errors and the absence of a pre-processing pipeline for these languages. ## conclusion in this study, we addressed the challenge of detecting propaganda techniques in memes through two distinct sub-tasks: textual and multimodal analysis, conducted in both supervised and zero-shot settings across various languages. to tackle this issue, we employed hierarchical text classification. in the multimodal sub-tasks, we focused solely on the textual content of memes, achieving notable performance. however, when dealing with subtasks in languages other than english, our system's performance suffered. we concluded by presenting the metrics and conducting a thorough analysis of the results. moving forward, our next objective is to develop a better hierarchical text classification model with better performance.