@inproceedings{j-hs-2021-trollmeta,
    title = "{T}roll{M}eta@{D}ravidian{L}ang{T}ech-{EACL}2021: Meme classification using deep learning",
    author = "J, Manoj Balaji  and
      Hs, Chinmaya",
    editor = "Chakravarthi, Bharathi Raja  and
      Priyadharshini, Ruba  and
      Kumar M, Anand  and
      Krishnamurthy, Parameswari  and
      Sherly, Elizabeth",
    booktitle = "Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages",
    month = apr,
    year = "2021",
    address = "Kyiv",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2021.dravidianlangtech-1.39/",
    pages = "277--280",
    abstract = "Memes act as a medium to carry one{'}s feelings, cultural ideas, or practices by means of symbols, imitations, or simply images. Whenever social media is involved, hurting the feelings of others and abusing others are always a problem. Here we are proposing a system, that classifies the memes into abusive/offensive memes and neutral ones. The work involved classifying the images into offensive and non-offensive ones. The system implements resnet-50, a deep residual neural network architecture."
}