ML Anthology

BaitBuster: A Clickbait Identification Framework

Md Main Uddin Rony, Naeemul Hassan, Mohammad Yousuf
AAAI 2018 pp. 8216-8217
doi:10.1609/AAAI.V32I1.11378 /aaai/2018/rony2018aaai-baitbuster/

Abstract

The use of tempting and often misleading headlines (clickbait) to allure readers has become a growing practice nowadays among the media outlets. The widespread use of clickbait risks the reader’s trust in media. In this paper, we present BaitBuster, a browser extension and social bot based framework, that detects clickbaits floating on the web, provides brief explanation behind its decision, and regularly makes users aware of potential clickbaits.

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Cite

Text

Rony et al. "BaitBuster: A Clickbait Identification Framework." AAAI Conference on Artificial Intelligence, 2018. doi:10.1609/AAAI.V32I1.11378

Markdown

[Rony et al. "BaitBuster: A Clickbait Identification Framework." AAAI Conference on Artificial Intelligence, 2018.](https://mlanthology.org/aaai/2018/rony2018aaai-baitbuster/) doi:10.1609/AAAI.V32I1.11378

BibTeX

@inproceedings{rony2018aaai-baitbuster,
  title     = {{BaitBuster: A Clickbait Identification Framework}},
  author    = {Rony, Md Main Uddin and Hassan, Naeemul and Yousuf, Mohammad},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2018},
  pages     = {8216-8217},
  doi       = {10.1609/AAAI.V32I1.11378},
  url       = {https://mlanthology.org/aaai/2018/rony2018aaai-baitbuster/}
}