A deep learning framework for clickbait detection on social area network using natural language cues

Author:

Naeem Bilal,Khan Aymen,Beg Mirza OmerORCID,Mujtaba Hasan

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences,General Environmental Science

Reference20 articles.

1. Agrawal, A. (2016). Clickbait detection using deep learning. In 2nd international conference on next generation computing technologies (NGCT) (pp. 268–272).

2. Bourgonje, P., Schneider, J. M., & Rehm, G. (2017). From clickbait to fake news detection: An approach based on detecting the stance of headlines to articles. In Proceedings of the 2017 EMNLP workshop: Natural language processing meets journalism (pp. 84–89). Association for Computational Linguistics.

3. Capdevila, J., Cerquides, J., & Torres, J. (2018). Mining urban events from the tweet stream through a probabilistic mixture model. Data Mining and Knowledge Discovery, 32(3), 764–786.

4. Conroy, N. J., Rubin, V. L., & Chen, Y. (2015). Automatic deception detection: Methods for finding fake news. In Proceedings of the 78th ASIS&T annual meeting: Information science with impact: Research in and for the community, ASIST’15 (pp. 82:1–82:4).

5. Freid, J. (2018). Facebook’s fight against spam and clickbait and what it means for advertisers. In Marketing land.

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