An ensemble based classification approach for credibility analysis of online news by detecting clickbait news headlines
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Date
2017-08
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G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand)
Abstract
The present work proposes a methodology for detecting clickbait news headlines in online news media using Ensemble based classification Technique. In this era of Digitization, presenting news now became online. Everyone is accessing online news by one or other medium. When online news is so popular and easily accessible, it also makes online news vulnerable too. Anyone can write anything in the name of news and it becomes viral whether it is informative or not. Due to the high competition and thrust of clicks, clickbait headlines are manufactured just to attract readers to click. These headlines generate enough curiosity by using some tactics so that readers compelled to click on the link to fill the knowledge gap. Clickbait headlines are compromising the meaning of true journalism. The present work is aimed to develop a clickbait detection system for analyzing the credibility of online news. So that the readers become aware and do not click on these links. News headlines are a piece of text, hence the proposed task is divided into two subtasks; Text analysis and classification. Text analysis is done for the transformation of text into numerical features usable for machine learning. These numerical features are then used for training the ensemble based classifier. The training dataset contains 10000 clickbait and 10000 non-clickbait headlines. Python 2.7 is used for the programming and system is tested for 10600 news headlines which are in an even distribution of 5800 clickbait and nonclickbait headlines and gained 93.13% accuracy. This system is also validated using k-fold cross validation technique.
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