A fine-tuned large language model for improved click-bait title detection
| dc.contributor.author | Vuppala, Pavan Sai | |
| dc.contributor.author | Sekharan, Chandra N. | |
| dc.date.accessioned | 2023-09-20T20:06:27Z | |
| dc.date.available | 2023-09-20T20:06:27Z | |
| dc.date.issued | 2023-08-29 | |
| dc.description | Department of Computing Sciences, College of Engineering | |
| dc.description.abstract | The internet has experienced a widespread phenomenon of clickbait, especially on social media platforms and news websites. Clickbait headlines and descriptions attract clicks and generate ad revenue by using exaggerated, sensational, or misleading language. Clickbait can harm online users by wasting their time, spreading misinformation, damaging reputations, or even exposing them to malware or phishing attacks. Detecting clickbait manually is subjective and time-consuming since different people may have different opinions on what constitutes clickbait. Rule-based approaches, machine learning models[8], deep learning models[9] and natural language processing techniques are some of the existing methods for clickbait detection. However, clickbait detection remains a challenging task due to the diversity and complexity of clickbait content, as well as the constantly evolving strategies used by clickbait creators. In this research, we employed a methodology to detect clickbait titles using a fine-tuned large language model (LLM) that was trained on a limited dataset of clickbait titles. | |
| dc.identifier.uri | https://hdl.handle.net/1969.6/97366 | |
| dc.language.iso | en_US | |
| dc.title | A fine-tuned large language model for improved click-bait title detection | |
| dc.type | Presentation |
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