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Clickbait Detection
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Clickbait detection is the task of identifying clickbait, a form of false advertisement, that uses hyperlink text or a thumbnail link that is designed to attract attention and to entice users to follow that link and read, view, or listen to the linked piece of online content, with a defining characteristic of being deceptive, typically sensationalized or misleading (Source: Adapted from Wikipedia)
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/clickbait-challenge/snapper
공식 코드A Two-Level Classification Approach for Detecting Clickbait Posts using Text-Based Features
https://github.com/dariabroscoteanu/roclico
공식 코드https://github.com/mdmotaharmahtab/banglabait
공식 코드BanglaBait: Semi-Supervised Adversarial Approach for Clickbait Detection on Bangla Clickbait Dataset
https://github.com/zhouyiwei/cc
공식 코드https://github.com/zhuyiyzu/chatgptforclickbait
공식 코드https://github.com/ankeshanand/deep-clickbait-detection
We used Neural Networks to Detect Clickbaits: You won't believe what happened Next!
https://github.com/clickbait-challenge/albacore
Using Neural Network for Identifying Clickbaits in Online News Media
https://github.com/khelloufelbakrilaaroussi/El-bekri-Laaroussi-Khellouf
We used Neural Networks to Detect Clickbaits: You won't believe what happened Next!
https://github.com/tanamania5555/InstaAsli
Using Neural Network for Identifying Clickbaits in Online News Media
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