The Clickbait Challenge 2017: Towards a Regression Model for Clickbait Strength

Abstract

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.

References in corpus (1)

• Machine Learning Based Detection of Clickbait Posts in Social Media

Cited by in corpus (7)

• Clickbait Detection in Tweets Using Self-attentive Network
• Machine Learning Based Detection of Clickbait Posts in Social Media
• Fishing for Clickbaits in Social Images and Texts with Linguistically-Infused Neural Network Models
• A Two-Level Classification Approach for Detecting Clickbait Posts using Text-Based Features
• Clickbait Identification using Neural Networks
• Identifying Clickbait Posts on Social Media with an Ensemble of Linear Models
• Clickbait detection using word embeddings