@inproceedings{nandi-etal-2022-teamx,
    title = "{T}eam{X}@{D}ravidian{L}ang{T}ech-{ACL}2022: A Comparative Analysis for Troll-Based Meme Classification",
    author = "Nandi, Rabindra Nath  and
      Alam, Firoj  and
      Nakov, Preslav",
    editor = "Chakravarthi, Bharathi Raja  and
      Priyadharshini, Ruba  and
      Madasamy, Anand Kumar  and
      Krishnamurthy, Parameswari  and
      Sherly, Elizabeth  and
      Mahesan, Sinnathamby",
    booktitle = "Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/2022.dravidianlangtech-1.13/",
    doi = "10.18653/v1/2022.dravidianlangtech-1.13",
    pages = "79--85",
    abstract = "The spread of fake news, propaganda, misinformation, disinformation, and harmful content online raised concerns among social mediaplatforms, government agencies, policymakers, and society as a whole. This is because such harmful or abusive content leads to several consequences to people such as physical, emotional, relational, and financial. Among different harmful content trolling-based online content is one of them, where the idea is to post a message that is provocative, offensive, or menacing with an intent to mislead the audience. The content can be textual, visual, a combination of both, or a meme. In this study, we provide a comparative analysis of troll-based memes classification using the textual, visual, and multimodal content. We report several interesting findings in terms of code-mixed text, multimodal setting, and combining an additional dataset, which shows improvements over the majority baseline."
}