  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 possible to visit their websites using clickbait
messages. Machine learning technology may help to handle this problem, giving
rise to automatic clickbait detection. To accelerate progress in this
direction, we organized the Clickbait Challenge 2017, a shared task inviting
the submission of clickbait detectors for a comparative evaluation. A total of
13 detectors have been submitted, achieving significant improvements over the
previous state of the art in terms of detection performance. Also, many of the
submitted approaches have been published open source, rendering them
reproducible, and a good starting point for newcomers. While the 2017 challenge
has passed, we maintain the evaluation system and answer to new registrations
in support of the ongoing research on better clickbait detectors.
