Using Neural Network for Identifying Clickbaits in Online News Media
This addresses the issue of misleading headlines for online media users, but it is incremental as it builds on existing machine learning methods for clickbait detection.
The researchers tackled the problem of detecting clickbait headlines in online news media by proposing a deep learning model, which achieved first rank in the Clickbait Challenge 2017 with a Mean Squared Error metric.
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.