A Two-Level Classification Approach for Detecting Clickbait Posts using Text-Based Features
This work addresses the problem of misleading content for social media users, but it is incremental as it builds on existing clickbait and fake news detection methods.
The paper tackled detecting clickbait posts on social media using a two-level classification approach with text-based features, achieving an F-score of 0.63 in blind tests but only 0.43 in the final challenge evaluation.
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. We present our exploratory results with individual features and their combinations, taken from the post text and the target article title, as well as feature selection. While our own blind tests with the dataset led to an F-score of 0.63, our final evaluation in the Challenge only achieved an F-score of 0.43. We explore the possible causes of this, and lay out potential future steps to achieve more successful results.