@inproceedings{potthast-etal-2018-crowdsourcing,
title = "Crowdsourcing a Large Corpus of Clickbait on {T}witter",
author = "Potthast, Martin and
Gollub, Tim and
Komlossy, Kristof and
Schuster, Sebastian and
Wiegmann, Matti and
Garces Fernandez, Erika Patricia and
Hagen, Matthias and
Stein, Benno",
editor = "Bender, Emily M. and
Derczynski, Leon and
Isabelle, Pierre",
booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
month = aug,
year = "2018",
address = "Santa Fe, New Mexico, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/C18-1127",
pages = "1498--1507",
abstract = "Clickbait has become a nuisance on social media. To address the urging task of clickbait detection, we constructed a new corpus of 38,517 annotated Twitter tweets, the Webis Clickbait Corpus 2017. To avoid biases in terms of publisher and topic, tweets were sampled from the top 27 most retweeted news publishers, covering a period of 150 days. Each tweet has been annotated on 4-point scale by five annotators recruited at Amazon{'}s Mechanical Turk. The corpus has been employed to evaluate 12 clickbait detectors submitted to the Clickbait Challenge 2017. Download: \url{https://webis.de/data/webis-clickbait-17.html} Challenge: \url{https://clickbait-challenge.org}",
}
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%0 Conference Proceedings
%T Crowdsourcing a Large Corpus of Clickbait on Twitter
%A Potthast, Martin
%A Gollub, Tim
%A Komlossy, Kristof
%A Schuster, Sebastian
%A Wiegmann, Matti
%A Garces Fernandez, Erika Patricia
%A Hagen, Matthias
%A Stein, Benno
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F potthast-etal-2018-crowdsourcing
%X Clickbait has become a nuisance on social media. To address the urging task of clickbait detection, we constructed a new corpus of 38,517 annotated Twitter tweets, the Webis Clickbait Corpus 2017. To avoid biases in terms of publisher and topic, tweets were sampled from the top 27 most retweeted news publishers, covering a period of 150 days. Each tweet has been annotated on 4-point scale by five annotators recruited at Amazon’s Mechanical Turk. The corpus has been employed to evaluate 12 clickbait detectors submitted to the Clickbait Challenge 2017. Download: https://webis.de/data/webis-clickbait-17.html Challenge: https://clickbait-challenge.org
%U https://aclanthology.org/C18-1127
%P 1498-1507
Markdown (Informal)
[Crowdsourcing a Large Corpus of Clickbait on Twitter](https://aclanthology.org/C18-1127) (Potthast et al., COLING 2018)
ACL
- Martin Potthast, Tim Gollub, Kristof Komlossy, Sebastian Schuster, Matti Wiegmann, Erika Patricia Garces Fernandez, Matthias Hagen, and Benno Stein. 2018. Crowdsourcing a Large Corpus of Clickbait on Twitter. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1498–1507, Santa Fe, New Mexico, USA. Association for Computational Linguistics.