# Identifying Clickbait Posts on Social Media with an Ensemble of Linear Models

arXiv: https://arxiv.org/abs/1710.00399 · HTML: https://mlsanity.com/paper/1710.00399/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2017.

## Built on by (most cited first, 2 of 2)

- [SOK: Fake News Outbreak 2021: Can We Stop the Viral Spread?](https://mlsanity.com/paper/2105.10671/) (2021)
- [Is it a click bait? Let's predict using Machine Learning](https://mlsanity.com/paper/2106.07348/) (2021)

## Abstract (arXiv)

The purpose of a clickbait is to make a link so appealing that people click on it. However, the content of such articles is often not related to the title, shows poor quality, and at the end leaves the reader unsatisfied. To help the readers, the organizers of the clickbait challenge (http://www.clickbait-challenge.org/) asked the participants to build a machine learning model for scoring articles with respect to their "clickbaitness". In this paper we propose to solve the clickbait problem with an ensemble of Linear SVM models, and our approach was tested successfully in the challenge: it showed great performance of 0.036 MSE and ranked 3rd among all the solutions to the contest.