BaitBuster: A Clickbait Identification Framework

Authors

  • Md Main Uddin Rony The University of Mississippi
  • Naeemul Hassan The University of Mississippi
  • Mohammad Yousuf The  University of Oklahoma

DOI:

https://doi.org/10.1609/aaai.v32i1.11378

Keywords:

Disinformation, Social Media, Machine Learning

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.

AAAI 2018 Proceedings Cover

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Published

2018-04-29

How to Cite

Rony, M. M. U., Hassan, N., & Yousuf, M. (2018). BaitBuster: A Clickbait Identification Framework. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.11378

Issue

Vol. 32 No. 1 (2018): Thirty-Second AAAI Conference on Artificial Intelligence

Section

Demonstrations