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

TL;DR

The paper presents the Clickbait Challenge 2017, which organized a shared task to develop and evaluate machine learning models for detecting clickbait, resulting in improved detection performance and open-source solutions.

Contribution

It introduces a competitive evaluation framework for clickbait detection, encouraging reproducible research and advancing the state of the art in clickbait identification.

Findings

Significant improvements over previous methods

Multiple open-source detector implementations

Ongoing evaluation system for future research

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.