@inproceedings{reddy-bellamkonda-2026-abusive,
    title = "Abusive Content Detection in {T}elugu-{E}nglish Code-Mixed Social Media Using Hybrid Transformer Architectures",
    author = "Reddy, Bojja Revanth  and
      Bellamkonda, Sivaiah",
    editor = "Chakravarthi, Bharathi Raja  and
      Priyadharshini, Ruba  and
      Madasamy, Anand Kumar  and
      Thavareesan, Sajeetha  and
      Rajiakodi, Saranya  and
      Navaneethakrishnan, Subalalitha  and
      Chinnappa, Dhivya  and
      Palani, Balasubramanian  and
      Subramanian, Malliga  and
      Shanmugavadivel, Kogilavani  and
      Rajalakshmi, Ratnavel",
    booktitle = "Proceedings of the Sixth Workshop on Speech, Vision, and Language Technologies for {D}ravidian Languages",
    month = jul,
    year = "2026",
    address = "Underline (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.dravidianlangtech-1.1/",
    pages = "1--5",
    ISBN = "979-8-89176-401-9",
    abstract = "The rapid growth of social media platforms has led to a substantial increase in user-generated content, including abusive and offensive language. Detecting abusive content becomes particularly challenging in low-resource and code-mixed language settings such as Telugu-English social media text. Code-mixed content involves transliteration, inconsistent spelling variations, informal expressions, and frequent language switching within a single sentence. This paper focuses on detecting abusive content in Telugu-English code-mixed comments using both traditional machine learning and transformer-based deep learning models. The proposed approach incorporates preprocessing strategies to normalize transliterations and spelling variations, hybrid feature extraction techniques combining TF-IDF and FastText embeddings, and fine-tuning of multilingual transformer models. The study addresses challenges such as morphological complexity, contextual ambiguity, and limited annotated data in low-resource NLP environments."
}