Quartet@ LT - EDI 2024: A SVM - R es N et50 Approach For Multitask Meme Classification - Unraveling Misogynistic and Trolls in Online Memes (2024)

Abstract: Meme is a very popular term prevailing among almost all social media platforms in recent days. A meme can be a combination of text and image whose sole purpose is meant to be funny and entertain people. Memes can sometimes promote misogynistic content expressing hatred, contempt, or prejudice against women. The Shared Task LT–EDI 2024: Multitask Meme Classification: Unraveling Misogynistic and Trolls in Online Memes Task 1 was created with the purpose to classify social media memes as “misogynistic” and “Non - Misogynistic”. The task encompassed Tamil and Malayalam datasets. We separately classified the textual data using Multinomial Naive Bayes and pictorial data using ResNet50 model. The results of from both data were combined to yield an overall result. We were ranked 2nd for both languages in this task.

## introduction social media is a platform where millions of people connect and engage with each other. social media has shaped the way people communicate, share ideas and information among each other colossally. with the immense number of people joining social media each day, social media platforms have become inevitable and play a pivotal role in the modern society. with the rising usage of social media platforms in the society, they are also used as a source of entertainment. people create entertaining content and post it on social media which is then viewed by millions of people on the internet. with this trend of people posting entertaining contents on social media, a term called "meme" has become prominent especially among youngsters in the society @xcite . memes are a ubiquitous form of internet culture whose sole purpose is to be funny and entertain people. a meme can be a text, image, video, audio or a combination of these that embodies humour, sarcasm or irony in it. memes has the ability to transcend linguistic and cultural barriers reaching a wide and diverse range of audience. while memes can serve as a form of humorous and relatable content on social media, they also have their darker side that includes misogyny portraying very harmful stereotypes about women, objectifying them and also initiate gender-based hatred and violence. multimodal data analysis can be employed to analyse the memes that are available on the internet. a modality is defined as the type or the nature of representation of the data which includes text, image, video and audio. multimodal data is a representation of data that comprises of two or more modalities of data @xcite . a meme which can be a combination of different modalities such as text and image, text and video, audio and video, etc are multimodal in nature. multimodal data analysis can be applied on these memes to classify them as "misogynistic" or "non-misogynistic". supervised learning can be used for the process for which a well labelled balanced training data is very essential. in case of unavailability of a proper training data, unsupervised leaning can also be performed to carry on multimodal data analysis. misogyny is something that deprives women of their rights and privileges and promotes toxic masculinity. in a society which is moulding itself towards gender equality and women empowerment, misogyny should be eliminated. with social media being an inevitable and widely used tool in recent times, having misogynistic content in them will lead to a lot of misinformation and stereotypes against women. therefore, detecting and moderating these types of memes in social media platforms is indeed vital and assists the movement towards a better society. the given task aims to encourage the development of models for detecting misogynistic memes in tamil and malayalam. the memes and the text inscribed in them were provided in the dataset for suryawanshi et al. (2020) developed a meme classification system using an early fusion technique to combine the text and image modality and compared it with a text and an image only baseline to investigate its effectiveness. simple prompts were constructed and a few in-context examples were provided by @xcite to exploit the implicit knowledge in the pre-trained roberta language model for hateful memes classification. ## task and data description the shared task lt-edi 2024: multitask meme classification -unraveling misogynistic and trolls in online memes foot_0 (chakravarthi et al., 2024) task 1 was created with the purpose of classifying memes as "misogynistic" and "non-misogynistic". memes on languages tamil and malayalam were provided to us as datasets. a sample record from the dataset encompassed the meme image, the text that is inscribed in the image and the label of whether the meme is misogynistic or not. ## methodology the approach we took was to create build two separate models for each modality. we employed multinomial naive bayes for text classification and resnet50 for image classification. the resulting probabilities from each model were considered and simple arithmetic was performed to yield the overall result. ## limitations as two separate models were used in the proposed solution, one for text and the other for image, the training process takes place for both the models, thereby increasing the training time of the overall procedure. masking the text in the images emanates a significant increase in the performance of the model which could not be implemented as the operation requires large gpu resources. due to unavailability of a balanced dataset, even after up sampling the misogynistic instances, a small amount of bias towards the non-misogynistic category is still present over the misogynistic category. the tf-idf vectorizer which is used to extract features from textual data computes document similarity directly in the word-count space, which may be slow for large vocabularies. also, the semantic relations between words are not considered during feature extraction. ## ethics statement the acl code of ethics foot_1 has been followed and practiced throughout the process of working on the shared task. the classification system is built with the notion to eliminate misogyny from the society resulting in a safe and inclusive social environment for all community of people to participate in. all the authors whose existing ideas, invention, work or artifact has been referenced or utilized is given credit providing a link to the original work in the references section. our solution prioritizes data privacy by not providing any access to random entities ensuring no leak of information to any other individual or organization. the given task was used as an opportunity to upgrade and enhance our skills while practicing the principles for professional competence. the proposed solution abides by the local, regional, national and international laws and regulations. shardul suryawanshi, bharathi raja chakravarthi, mihael arcan, and paul buitelaar. 2020. multimodal meme dataset (multioff) for identifying offensive content in image and text. in proceedings of the second workshop on trolling, aggression and cyberbullying, pages 32-41, marseille, france. european language resources association (elra). riza velioglu and jewgeni rose. 2020. detecting hate speech in memes using multimodal deep learning approaches: prize-winning solution to hateful memes challenge.