  Social media platforms often act as breeding grounds for various forms of
trolling or malicious content targeting users or communities. One way of
trolling users is by creating memes, which in most cases unites an image with a
short piece of text embedded on top of it. The situation is more complex for
multilingual(e.g., Tamil) memes due to the lack of benchmark datasets and
models. We explore several models to detect Troll memes in Tamil based on the
shared task, "Troll Meme Classification in DravidianLangTech2022" at ACL-2022.
We observe while the text-based model MURIL performs better for Non-troll meme
classification, the image-based model VGG16 performs better for Troll-meme
classification. Further fusing these two modalities help us achieve stable
outcomes in both classes. Our fusion model achieved a 0.561 weighted average F1
score and ranked second in this task.
