Using Neural Network for Identifying Clickbaits in Online News Media

TL;DR

This paper presents a deep learning model that effectively detects clickbait headlines in online news media, achieving top performance in the Clickbait Challenge 2017, and uses data analysis for deeper insights.

Contribution

It introduces a novel deep learning approach for clickbait detection and demonstrates its superiority through winning the Clickbait Challenge 2017.

Findings

Achieved first rank in the Clickbait Challenge 2017 based on Mean Squared Error.

Utilized data analytics and visualization to gain insights from the dataset.

Proved the effectiveness of deep learning methods in clickbait detection.

Abstract

Online news media sometimes use misleading headlines to lure users to open the news article. These catchy headlines that attract users but disappointed them at the end, are called Clickbaits. Because of the importance of automatic clickbait detection in online medias, lots of machine learning methods were proposed and employed to find the clickbait headlines. In this research, a model using deep learning methods is proposed to find the clickbaits in Clickbait Challenge 2017's dataset. The proposed model gained the first rank in the Clickbait Challenge 2017 in terms of Mean Squared Error. Also, data analytics and visualization techniques are employed to explore and discover the provided dataset to get more insight from the data.