565 citations · 1k across the 46 of their papers we have counts for
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The Clickbait Challenge 2017: Towards a Regression Model for Clickbait Strength
Martin Potthast, Tim Gollub, Matthias Hagen +1
Clickbait has grown to become a nuisance to social media users and social media operators alike. Malicious content publishers misuse social media to manipulate as many users as pos…
Wikipedia Text Reuse: Within and Without
Milad Alshomary, Michael Völske, Tristan Licht +4
We study text reuse related to Wikipedia at scale by compiling the first corpus of text reuse cases within Wikipedia as well as without (i.e., reuse of Wikipedia text in a sample o…
Heuristic Feature Selection for Clickbait Detection
Matti Wiegmann, Michael Völske, Benno Stein +2
We study feature selection as a means to optimize the baseline clickbait detector employed at the Clickbait Challenge 2017. The challenge's task is to score the "clickbaitiness" of…