Understanding the Production and Consumption of Clickbaits in Twitter

With the growing shift towards news consumption, primarily through social media sites like Twitter, most of the traditional as well as new-age media houses are promoting their news stories by tweeting about them. The competition for user attention in such mediums has led many media houses to use catchy sensational form of tweets to attract more users – a process known as clickbaiting.  Examples of clickbaits include “17 Reasons You Should Never Ever Use Makeup”, “These Dads Quite Frankly Just Don’t Care What You Think”, or “10 reasons why Christopher Hayden was the worst ‘Gilmore Girls’ character”.

On one hand, the success of such clickbaits in attracting visitors to the news websites has helped mushrooming of several digital media companies. However, on the other hand, there are concerns regarding the news value the articles offer, drawing the demand for their blanket ban from many quarters. We believe that we need to consider all associated angles, especially the clickbait readers, before enforcing any drastic ban.

In this paper, we analyze the readership of clickbaits in Twitter. We collect around 12 Million tweets over eight months covering both clickbait and non-clickbait (or traditional) tweets, and then attempt to investigate the following research questions:

  • How are clickbait tweets different from non-clickbait tweets?
  • How do clickbait production and consumption differ from non-clickbaits?
  • Who are the consumers of clickbait and non-clickbait tweets?
  • How do the clickbait and non-clickbait consumers differ as a group?
The presence of different entities in both clickbait and non-clickbait tweets.

Our investigation reveals several interesting insights on the production of clickbaits. For example, clickbait tweets include more entities such as images, hashtags, and user mentions, which help in capturing the attention of the consumers. Additionally, we find that a higher percentage of clickbait tweets convey positive sentiments as compared to non-clickbait tweets. As a result, clickbait tweets tend to have a wider and deeper reach in its consumer base than non-clickbait tweets.

We also make multiple interesting observations regarding the consumers of clickbaits. For example, clickbait tweets are consumed more by women than men, as well as by younger people compared to the consumers of non-clickbaits. Additionally, they have higher mutual engagement among each other. On the other hand, non-clickbait consumers are more reputed in the community, and have relatively higher follower base than clickbait consumers.

Overall, we make two major contributions in this paper: (i) to our knowledge, this is the first attempt to understand the consumers of clickbaits, and (ii) while doing so, we also make the first effort to contextualize the rise of clickbaits with the tabloidization of news. We believe that this paper can foster further research going beyond only negative aspects of clickbaits, and help bring in a more holistic view of the online news spectrum.

For more, see our full paper, Tabloids in the Era of Social Media? Understanding the Production and Consumption of Clickbaits in Twitter, at CSCW 2018.

Abhijnan ChakrabortyIndian Institute of Technology Kharagpur, India
Rajdeep Sarkar,  Indian Institute of Technology Kharagpur, India
Ayushi Mrigen, Indian Institute of Technology Kharagpur, India
Niloy GangulyIndian Institute of Technology Kharagpur, India