A Two-Level Classification Approach for Detecting Clickbait Posts using Text-Based Features

Understand

The emergence of social media as news sources has led to the rise of clickbait posts attempting to attract users to click on article links without informing them on the actual article content.

• This paper presents our efforts to create a clickbait detector inspired by fake news detection algorithms, and our submission to the Clickbait Challenge 2017.
• The detector is based almost exclusively on text-based features taken from previous work on clickbait detection, our own work on fake post detection, and features we designed specifically for the challenge.
• We use a two-level classification approach, combining the outputs of 65 first-level classifiers in a second-level feature vector.