Classifying clickbaits: articles with potentially misleading titles, using a state-of-the-art NLP architecture.
Model used encoder layer of Transformer architecture introduced by Vaswani et.al.
Model perfomed with an accuracy of 98%.
Abhijnan Chakraborty, Bhargavi Paranjape, Sourya Kakarla, and Niloy Ganguly. "Stop Clickbait: Detecting and Preventing Clickbaits in Online News Media”. In Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), San Fransisco, US, August 2016.
The clickbait corpus consists of article headlines from ‘BuzzFeed’, ‘Upworthy’, ‘ViralNova’, ‘Thatscoop’, ‘Scoopwhoop’ and ‘ViralStories’. The non-clickbait article headlines are collected from ‘WikiNews’, ’New York Times’, ‘The Guardian’, and ‘The Hindu’.
This dataset is an enlarged version of the dataset used in the following paper. If you are using this data for any research publication, or for preparing a technical report, you must cite the paper as the source of the dataset.
@inproceedings{chakraborty2016stop,
title={Stop Clickbait: Detecting and preventing clickbaits in online news media},
author={Chakraborty, Abhijnan and Paranjape, Bhargavi and Kakarla, Sourya and Ganguly, Niloy},
booktitle={Advances in Social Networks Analysis and Mining (ASONAM), 2016 IEEE/ACM International Conference on},
pages={9--16},
year={2016},
organization={IEEE}
}