  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.
