Explaining and predicting click-baitiness and click-bait virality

Abstract

PurposeIn the age of social media, when publishers are vying for consumer attention, click-baits have become very common. Not only viral websites but also mainstream publishers, such as news channels, use click-baits for generating traffic. Therefore, click-bait detection and prediction of click-bait virality have become important challenges for social media platforms to keep the platform click-bait free and give a better user experience. The purpose of this study is to try exploring how the contents of the social media posts and the article can be used to explain and predict social media posts and the virality of a click-bait.Design/methodology/approachThis study has used 17,745 tweets from Twitter with 4,370 click-baits from top 27 publishers and applied econometric along with machine learning methods to explain and predict click-baitiness and click-bait virality.FindingsThis study finds that language formality, readability, sentiment scores and proper noun usage of social media posts and various parts of the target article plays differential and important roles in click-baitiness and click-bait virality.Research limitations/implicationsThe paper contributes toward the literature of dark behavior in social media at large and click-bait prediction and explanation in particular. It focuses on the differential roles of the social media post, the article shared and the source in explaining click-baitiness and click-bait virality via psycho-linguistic framework. The paper also provides explanability to the econometric and machine learning predictive models, thus performing methodological contribution too.Practical implicationsThe paper helps social media managers create a mechanism to detect click-baits and also predict which ones of them can become viral so that corrective measures can be taken.Originality/valueTo the best of the authors’ knowledge, this is one of the first papers which focus on both explaining and predicting click-baitiness and click-bait virality.