Creation and Classification of Kannada Meme Dataset: Exploring Domain and Troll Categories

Abstract

In this pioneering research, the first-ever Kannada memes dataset is established, marking a groundbreaking contribution. This dataset encompasses 2002 memes, spanning various categories such as movies, politics, sports, trolls, and non-troll memes. The classification models have been meticulously fine-tuned for memes, incorporating image-based models using DenseNet169 and text-based models with BERT for text encoding. An innovative multimodal approach combines insights from images and text, acknowledging the comprehensive nature of meme content. Throughout the study, model strengths and weaknesses are assessed, emphasizing their reliance on cutting-edge technologies like Deep Learning and Natural Language Processing. Valuable improvements are recommended, such as the implementation of oversampling techniques and regular dataset updates to enhance relevance and accuracy. This work extends beyond immediate research, contributing to the development of adaptive meme classification systems, particularly for Kannada-speaking audiences within the evolving meme culture landscape. Notably, the results indicate that multimodal models achieved the best scores for domain classification, while image-based models excelled in troll meme classification, further highlighting the significance of this approach within the field. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.