@inproceedings{r-etal-2025-linguaists,
    title = "{L}ingu{AI}sts@{D}ravidian{L}ang{T}ech 2025: Misogyny Meme Detection using multimodel Approach",
    author = "R, Arthi  and
      J, Pavithra  and
      Manikandan, Dr G  and
      A, Lekhashree  and
      G, Dhanyashree  and
      Sahitya, Bommineni  and
      K, Arivuchudar  and
      K, Kalpana",
    editor = "Chakravarthi, Bharathi Raja  and
      Priyadharshini, Ruba  and
      Madasamy, Anand Kumar  and
      Thavareesan, Sajeetha  and
      Sherly, Elizabeth  and
      Rajiakodi, Saranya  and
      Palani, Balasubramanian  and
      Subramanian, Malliga  and
      Cn, Subalalitha  and
      Chinnappa, Dhivya",
    booktitle = "Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages",
    month = may,
    year = "2025",
    address = "Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Author-Pages-WenzhengZhang-ZhengyanShi-ShuYang/2025.dravidianlangtech-1.54/",
    doi = "10.18653/v1/2025.dravidianlangtech-1.54",
    pages = "309--314",
    ISBN = "979-8-89176-228-2",
    abstract = "Memes often disseminate misogynistic material, which nurtures gender discrimination and stereotyping. While it is an effective tool of communication, social media has also provided a fertile ground for online abuse. This vital issue in the multilingual and multimodal setting is tackled by the Misogyny Meme Detection Shared Task. Our method employs advanced NLP techniques and machine learning models to classify memes in Malayalam and Tamil, two low-resource languages. Preprocessing of text includes tokenization, lemmatization, and stop word removal. Features are then extracted using TF-IDF. With the best achievable hyperparameters, along with the SVM model, our system provided very promising outcomes and ranked 9th among the systems competing in the Tamil task with a 0.71259 F1-score, and ranked 15th with an F1-score of 0.68186 in the Malayalam taks. With this research work, it would be established how important AI-based solutions are toward stopping online harassment and developing secure online spaces."
}