SSN _ MLRG 1@ D ravidian L ang T ech- ACL 2022: Troll Meme Classification in T amil using Transformer Models (2022)

Abstract: The ACL shared task of DravidianLangTech-2022 for Troll Meme classification is a binary classification task that involves identifying Tamil memes as troll or not-troll. Classification of memes is a challenging task since memes express humour and sarcasm in an implicit way. Team SSN_MLRG1 tested and compared results obtained by using three models namely BERT, ALBERT and XLNET. The XLNet model outperformed the other two models in terms of various performance metrics. The proposed XLNet model obtained the 3rd rank in the shared task with a weighted F1-score of 0.558.

## introduction memes are interesting ideas that spread the emotions of the people or culture across the internet. social media plays a pivotal role in facilitating the spread of memes. memes have become a powerful tool of everyday communication that uses humour to catch the attention of the people and also help in sharing the views @xcite . and though a good number of them are harmless, there are many memes that are not. troll memes are such memes whose main goal is to provoke the audience with intent to offend or demean them @xcite . since the internet aids in the widespread propagation of memes it can be utilised in trolling @xcite @xcite @xcite . considering the adverse mental effects of troll memes on individuals, the task is to identify and label memes as troll or not in an effort to monitor what is being posted on the internet. tamil is one of the world's longest-surviving classical languages. according to a. k. ramanujan, it is "the only language of modern india that is recognizably continuous with a classical his-tory." because of the range and quality of ancient tamil literature, it has been referred to as "one of the world's major classical traditions and literatures." for almost 2000 years, there has been a recorded tamil literature. the earliest period of tamil literature, known as sangam literature, is said to have lasted from from 600 bc to ad 300 @xcite . among dravidian languages, it possesses the oldest existing literature. the earliest epigraphic documents discovered on rock edicts and 'hero stones' date from the third century bc @xcite @xcite . in tamil nadu, the archaeological survey of india discovered over 60,000 of the 100,000 odd inscriptions discovered in india. the majority of them are in tamil, with just around 5% in languages other than tamil. inscriptions in tamil inscribed in brahmi script have been unearthed in sri lanka, as well as on trade products in thailand and egypt @xcite @xcite @xcite . the task of classifying troll memes is challenging as it needs to discover the intention of the meme. moreover, memes often use offensive words to express feelings. we used xlnet @xcite , albert @xcite and bert @xcite models that classify memes as troll and not a troll. the training data set provided contains 2300 memes that have been annotated, out of which 1610 memes were used for training and the rest were used for development of the model. the troll memes are very subjective and the usage of colloquial language, emojis, references, symbols and images without text add more challenges in predicting the trolls. ## related work many researchers in the field of artificial intelligence and natural language processing have been working to detect hateful memes @xcite . in the past couple of years, social media usage has increased drastically and so the data available has also increased @xcite @xcite . @xcite studied the performance of nlp transformer models bert, roberta , distilbert, xlnet and m-bert in large multi-label text classification (lmtc) and found that roberta and bert yield best results. afridi et al. ( 2020 ) presented an inclusive study on meme classification and proposed a generalised framework for visual-linguistic problems. suryawanshi et al. (2020 ) created a dataset that has been used for classifying the memes. they have used image classification to address the difficulties in the state-of-the-art methods and concluded that such an image classifier is not feasible for classifying memes. the findings of previous shared tasks (suryawanshi and chakravarthi, 2021), @xcite have been shared where submissions show multiple ways to approach the problem. smitha et al. ( 2018 ) stress the importance of visual memes and recommend a framework that could be utilized to categorize the internet memes by certain visual and textual features. hegde et al. (2021b) illustrates different textual analysis methods and contrasting multi-modal methods from simple merging to cross attention to using both visual and textual features. crosslingual language model xlm was found to perform the best in textual analysis, and the multi-modal transformer performed the best in multi-modal analysis. it was noted that the distribution of the test set does matter and the type of images was different in the test set which could mainly affect the performance of the imagenet models while fine-tuning. @xcite provided the first large-scale analysis of who shares the image with text (iwt) memes, relative to other forms of expression and provided an analysis of the relationship between the demographics of users and their meme sharing patterns. they also developed an accurate, publicly available classifier to identify iwt memes in other data sets. hegde et al. (2021a) have put forth a model using vision transformer for images and bidirectional encoder representations from transformers (bert) for captions of memes to achieve an overall f1-score of 0.59 on the test set. they believed that the preprocessing of the images was a huge factor for achieving a great f1-score on the validation set. in @xcite , we worked to identify the presence of offensive language in social media posts using bert. deep network model with bert embeddings was found to achieve better f1 score when compared to 1d-cnn model trained with glove pretrained embeddings, 2d-cnn and bilstm models with word2vec embeddings. colbert (contextualized late interaction over bert), a modification of bert was used to detect offense and humor in text in @xcite which outperformed the machine learning models tested by a large margin. ## methodology the architecture diagram for the offensive text classification is shown in figure 1 . ## conclusion we built an xlnet based model for the task troll meme classification in tamil. the model uses the preprocessed data done using nltk, which we believe is a key factor for improved accuracy. classifying a meme based only on the text is a reason for the reduced accuracy. how a meme is perceived is based upon a multitude of factors and cannot be judged using simple conventional models. this is another reason for the reduced precision of classification. the words present in a meme are too intuitive for the models to detect accurately. we intend to further proceed by adding multiple hidden layers and building a complex network structure.