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Multimodal Meme Classification: Identifying Offensive Content in Image and Text

If you are using the code or the dataset for your research work then please cite our paper below:


title = "Multimodal Meme Dataset (MultiOFF) for Identifying Offensive Content in Image and Text",

author = "Suryawanshi, Shardul and Chakravarthi, Bharathi Raja and Arcan, Mihael and Buitelaar, Paul,

booktitle = "Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying ({TRAC}-2020)",

month = May,

year = "2020",

publisher = "Association for Computational Linguistics",}

This is a document that involves step by step instructions to execute the code

(Pre-requisite: Conda/python environment should have packages mentioned in requirement.txt file before execution Glove embedding of 50d has been used can be dowloaded from "")

--> Use google drive link to access the data ""

--> Split dataset has train, test and validation data

--> Labelled Image has memes belonging to each of the above dataset

--> This data needs to be placed and directory location needs to be changed while reading the data in main code

--> Once done with the setup mentioned above, one can execute the code in the sequence mentioned as below:

--> Stacked_LSTM_VGG16.ipynb
--> BiLSTM_VGG16.ipynb
--> CNN_VGG16.ipynb
--> LR_NB_DNN.ipynb