Abstract
—A meme is a culturally relevant, and brief form of media that raise a content over the internet. Now a days posting a meme is popular communication medium, due to its multimodal nature. Postings of hateful memes or fooling, cyberbullying are growing gradually. Meme takes a major part in forming people’s trust and perspective. Meme can be quickly post by anybody, and its integrity stands compromised. Hateful and aggressive matter detection have been largely traversed in a form such as text or image. And Memes complicate the task, because some meme can have a good caption and normal pictures, but if combined in some way, they can become offensive. So, it is required to fuse both modality to identify whether a given meme is hateful or not. So here for text classification we found the sequential model like Bi-LSTM and for image we will go with CNN. Late fusion technique is used to combine the image and text mode with EX-OR method to investigate its effectiveness.
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