CV coders on S emeval-2024 Task 4 (2024)

Abstract: In this paper, we present our methodology for addressing the SemEval 2024 Task 4 on “Multilingual Detection of Persuasion Techniques in Memes.” Our method focuses on identifying persuasion techniques within textual and multimodal meme content using a combination of preprocessing techniques and established models. By integrating advanced preprocessing methods, such as the OpenAI API for text processing, and utilizing a multimodal architecture combining VGG for image feature extraction and GPT-2 for text feature extraction, we achieve improved model performance. To handle class imbalance, we employ Focal Loss as the loss function and AdamW as the optimizer. Experimental results demonstrate the effectiveness of our approach, achieving competitive performance in the task. Notably, our system attains an F1 macro score of 0.67 and an F1 micro score of 0.74 on the test dataset, ranking third among all participants in the competition. Our findings highlight the importance of robust preprocessing techniques and model selection in effectively analyzing memes for persuasion techniques, contributing to efforts to combat misinformation on social media platforms.

## introduction the semeval 2024 task 4 foot_0 focuses on the multilingual detection of persuasion techniques in memes, a crucial endeavor in combating disinformation campaigns prevalent on social media platforms. memes, being potent vehicles for influencing public opinion, necessitate robust methods for identifying rhetorical and psychological techniques embedded within their textual and visual content. this task spans multiple languages, including bulgarian, english, and north macedonian, underscoring the global significance of addressing online misinformation @xcite . our system employs a combination of pretrained models for text and image processing to tackle the challenge posed by subtask 2b of task 4. specifically, we utilize pre-trained language models such as xlm-roberta and gpt-2 for textual feature extraction, while employing vgg and vit models for image feature extraction. this multimodal approach allows us to effectively capture both textual and visual cues present in memes. through our participation in this task, we discovered the importance of advanced preprocessing techniques, particularly in cleaning and standardizing textual data extracted from memes. leveraging the gpt api for text preprocessing and nltk for further cleaning proved instrumental in enhancing the quality of our training data. additionally, we observed the significance of model selection and hyperparameter tuning in achieving competitive performance. despite encountering challenges in cleaning textual data, our system achieved promising results, demonstrating the efficacy of our approach. ## background the task at hand, subtask 2b of semeval-2024 task 4, revolves around the multilingual detection of persuasion techniques in memes. memes, which are widely circulated across social media platforms, often contain subtle rhetorical and psychological strategies aimed at influencing public opinion. the goal of the task is to develop models capable of identifying these persuasion techniques embedded within the textual and visual content of memes. the input to the task consists of textual and visual data extracted from memes in various languages, including bulgarian, english, and north macedonian. the textual content of memes may contain linguistic elements such as catchphrases, slogans, or captions, while the visual component typically comprises images or graphics. for example, a meme may feature a humorous image accompanied by a caption containing persuasive language or propaganda. as for our participation, we focused on subtask 2b of task 4, which involves analyzing the presence of persuasion techniques in memes using both textual and visual features. our approach combines advanced preprocessing techniques with state-ofthe-art models to effectively tackle this challenging task. we draw inspiration from related work in the fields of natural language processing and computer vision, leveraging pre-trained models and techniques to enhance the accuracy and efficiency of our system. ## system overview our system for subtask 2b of task 4 in semeval 2024 employs a combination of algorithms and modeling decisions to detect persuasion techniques in memes based on both textual and visual content. in this section, we outline the key components of our system, including preprocessing steps, model architectures, and training procedures. ## experiment setup in this section, we detail the experimental setup used to train and evaluate our system for subtask 2b of task4 2024. this task is a multi-model binary classification task. ## conclusion this paper presents our approach to the semeval 2024 task 4 on "multilingual detection of persuasion techniques in memes." leveraging preprocessing techniques and a multimodal model architecture combining vgg for image features and gpt-2 for text features, our system achieved competitive results on the test dataset in subtask 2b. furthermore, our findings suggest that gpt-2 exhibits greater generalizability than xlm-roberta, with a lower limit on the number of tokens. insights from our experiments underscore the potential of pre-training models on similar data to enhance performance and generalization. looking ahead, future work could focus on pretraining models on meme-specific data and refining preprocessing techniques for extracted text. the large amount of available data presents an opportunity to delve deeper into this aspect, potentially improving model accuracy and robustness in meme analysis tasks.