Do Images really do the Talking? Analysing the significance of Images in Tamil Troll meme classification
This work addresses the problem of detecting harmful content like trolling in memes for social media moderation, but it appears incremental as it applies existing multimodal methods to a specific language dataset.
The study investigated the role of visual features in classifying Tamil memes as troll or non-troll, finding that a multimodal transformer model performed best by combining image and text analysis, with specific performance metrics not provided in the abstract.
A meme is an part of media created to share an opinion or emotion across the internet. Due to its popularity, memes have become the new forms of communication on social media. However, due to its nature, they are being used in harmful ways such as trolling and cyberbullying progressively. Various data modelling methods create different possibilities in feature extraction and turning them into beneficial information. The variety of modalities included in data plays a significant part in predicting the results. We try to explore the significance of visual features of images in classifying memes. Memes are a blend of both image and text, where the text is embedded into the image. We try to incorporate the memes as troll and non-trolling memes based on the images and the text on them. However, the images are to be analysed and combined with the text to increase performance. Our work illustrates different textual analysis methods and contrasting multimodal methods ranging from simple merging to cross attention to utilising both worlds' - best visual and textual features. The fine-tuned cross-lingual language model, XLM, performed the best in textual analysis, and the multimodal transformer performs the best in multimodal analysis.