@inproceedings{indurthi-varma-2023-francis,
    title = "{F}rancis Wilde at {S}em{E}val-2023 Task 5: Clickbait Spoiler Type Identification with Transformers",
    author = "Indurthi, Vijayasaradhi  and
      Varma, Vasudeva",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Da San Martino, Giovanni  and
      Tayyar Madabushi, Harish  and
      Kumar, Ritesh  and
      Sartori, Elisa},
    booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/2023.semeval-1.260/",
    doi = "10.18653/v1/2023.semeval-1.260",
    pages = "1890--1893",
    abstract = "Clickbait is the text or a thumbnail image that entices the user to click the accompanying link. Clickbaits employ strategies while deliberately hiding the critical elements of the article and revealing partial information in the title, which arouses sufficient curiosity and motivates the user to click the link. In this work, we identify the kind of spoiler given a clickbait title. We formulate this as a text classification problem. We finetune pretrained transformer models on the title of the post and build models for theclickbait-spoiler classification. We achieve a balanced accuracy of 0.70 which is close to the baseline."
}