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A meme is an part of media created to share an opinion or emotion across theinternet. Due to its popularity, memes have become the new forms ofcommunication on social media. However, due to its nature, they are being usedin harmful ways such as trolling and cyberbullying progressively. Various datamodelling methods create different possibilities in feature extraction andturning them into beneficial information. The variety of modalities included indata plays a significant part in predicting the results. We try to explore thesignificance of visual features of images in classifying memes. Memes are ablend of both image and text, where the text is embedded into the image. We tryto incorporate the memes as troll and non-trolling memes based on the imagesand the text on them. However, the images are to be analysed and combined withthe text to increase performance. Our work illustrates different textualanalysis methods and contrasting multimodal methods ranging from simple mergingto cross attention to utilising both worlds' - best visual and textualfeatures. The fine-tuned cross-lingual language model, XLM, performed the bestin textual analysis, and the multimodal transformer performs the best inmultimodal analysis.

Do Images really do the Talking? Analysing the significance of Images in Tamil Troll meme classification