Magnum JUCSE at S em E val-2024 Task 4: Multilingual Detection of Persuasion Techniques in Memes (2024)

Abstract: This paper focuses on the task of detecting persuasion techniques organised in a hierarchy within meme text in multiple languages like English, North Macedonian, Arabic and Bulgarian, exploring the ways in which textual elements contribute to the dissemination of persuasive messages.The main strategy of the system is to train a binary classifier for each node in the hierarchy and predict labels in a top down fashion by seeing the confidence value of the prediction at any node. For each unique label in the hierarchy, a dataset is created from the original dataset which is then used to train the binary classifier for that label

## introduction in the realm of digital communication and social media, memes have emerged as a powerful and widely shared form of content, known for their ability to convey messages in a succinct and often humorous manner. while memes are commonly associated with entertainment, their potential as a tool for persuasive communication, particularly in the context of textual content, has become increasingly evident. this paper focuses on the nuanced task of detecting persuasion techniques within meme text in multiple languages like english, north macedonian, arabic and bulgarian, exploring the ways in which textual elements contribute to the dissemination of persuasive messages. the main strategy of the system is to train a binary classifier for each node in the hierarchy and predict labels in a top down fashion by seeing the confidence value of the prediction at any node. for each unique label in the hierarchy, a dataset is created from the original dataset which is then used to train the binary classifier for that label. this task @xcite helped in understanding the intricacies of hierarchical classifi-cation as well as sentence transformers. our team participated in subtask 1 and ranked 21 out of 34 in english language whereas 4 out of 20 in bulgarian, 3 out of 20 in north macedonian and 11 out of 17 in arabic. ## background our task involves the detection of persuasion techniques from memes. the data is provided in json format with string text and a list of string labels for each text. the labels are defined in a hierarchical manner in the form of a directed acyclic graph (dag). a training set, a development or validation set, as well as a test set are available. the training and validation sets contain the labels while the test set only contains the text. in our dataset, 29 persuasion labels are defined. the dataset is quite unbalanced, with some labels occurring many times, with others occurring much less. this can be partially attributed to the hierarchical nature of the data. labels present neaerer to the root of the label dag tend to appear much more frequently than the labels present nearer to the leaves of the dag ## system overview the step by step flow of the system is shown in the figure @xcite and explained in detail in the subsections which follow. ## results we have provided the results of our method using some different thresholds. the result contains hierarchical f1 score, hierarchical precision as well as hierarchical recall. how the threshold is set is explained in the table @xcite . a confusion matrix is shown for the prediction of leaf nodes in figure the system gives satisfactory results on the validation dataset but more testing is required to measure the accuracy of the model. the accuracy of classifiers for some of the internal nodes is low because of a large variety of text sentences corresponding to the internal labels the leaf node classifiers generally have very high accuracy due to low number of example instances this system only works with textual data, considering memes have rich image information as well, utilizing it in sync with the textual data to accurately predict persuasion techniques would be a natural continuation of this work.