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

10/01/2017
by   Alexey Grigorev, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/05/2017

Machine Learning Based Detection of Clickbait Posts in Social Media

Clickbait (headlines) make use of misleading titles that hide critical i...
research
12/02/2018

Kiki Kills: Identifying Dangerous Challenge Videos from Social Media

There has been upsurge in the number of people participating in challeng...
research
04/26/2019

Recommending research articles to consumers of online vaccination information

Research communications often introduce biases or misrepresentations wit...
research
10/17/2022

AIM 2022 Challenge on Instagram Filter Removal: Methods and Results

This paper introduces the methods and the results of AIM 2022 challenge ...

Please sign up or login with your details

Forgot password? Click here to reset