A Community Driven Approach for click Bait Reporting

@article{Bufnea2018ACD,
  title={A Community Driven Approach for click Bait Reporting},
  author={Darius Bufnea and Diana Sotropa},
  journal={2018 26th International Conference on Software, Telecommunications and Computer Networks (SoftCOM)},
  year={2018},
  pages={1-6}
}
  • Darius Bufnea, Diana Sotropa
  • Published 2018
  • Computer Science
  • 2018 26th International Conference on Software, Telecommunications and Computer Networks (SoftCOM)
Click baits are primarily used by online content publishers. Their purpose is to allure readers to click on a link and subsequently visit other articles by the same publisher, in order to increase page views and ad revenue. Most of the time click baits are used for pointing to low quality articles or thin content. The user falls into the publishers’ trap due to a misleading or incomplete title or content exaggeration. A bait article link might also appear on social network shares or within the… Expand
A New Language Independent Strategy for Clickbait Detection
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A machine-learning model is presented that achieves high performance in predicting clickbaits and shows that the degree of informality of a web-page (as measured by different metrics) is a strong indicator of it being a clickbait. Expand
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This research proves that it is possible to identify clickbaits using all parts of the post while having minimum number of features possible. Expand
Academic clickbait: articles with positively-framed titles, interesting phrasing, and no wordplay get more attention online.
Clickbait is one of those words which is hard to define, but, like hardcore pornography, people know it when they see it (Jacobellis v. Ohio, 1964). More formally, clickbait is defined as "(on theExpand
Clickbait Detection
TLDR
This paper proposes a new model for the detection of clickbait, i.e., short messages that lure readers to click a link, based on 215 features that enables a random forest classifier to achieve 0.79 ROC-AUC at 0.76 precision and0.76 recall. Expand
Crowdsourcing a Large Corpus of Clickbait on Twitter
TLDR
A new corpus of 38,517 annotated Twitter tweets, the Webis Clickbait Corpus 2017, is constructed to address the urging task of clickbait detection. Expand