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.- Anthology ID:
- 2023.semeval-1.260
- Volume:
- Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Giovanni Da San Martino, Harish Tayyar Madabushi, Ritesh Kumar, Elisa Sartori
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1890–1893
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.260
- DOI:
- 10.18653/v1/2023.semeval-1.260
- Cite (ACL):
- Vijayasaradhi Indurthi and Vasudeva Varma. 2023. Francis Wilde at SemEval-2023 Task 5: Clickbait Spoiler Type Identification with Transformers. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 1890–1893, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Francis Wilde at SemEval-2023 Task 5: Clickbait Spoiler Type Identification with Transformers (Indurthi & Varma, SemEval 2023)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2023.semeval-1.260.pdf