An Empirical Study on the Clickbait of Data Science Articles in the WeChat Official Accounts

Abstract

In the Internet age, clickbait is an effective method to attract people’s attention, which usually uses some ways to achieve, such as exaggerating, omitting the details or using punctuation exceedingly. In order to attract the readers to click on the link, some news aggregator sites or social media will choose to use the clickbait. If the clickbait applied to scientific articles, not only will affects the article quality, but also will affects the development of relevant subjects. Thus, the purpose of this research is to explore whether there is a clickbait in the data science articles of WeChat official accounts. This paper collects the relevant data by using the shenjianshou platform, and then uses some steps to analyze data, including cleaning data, doing word segmentation, extracting keywords and building a regression model. According to the adjusted r-square value in the regression model, the model can only explains the change of 3.17% page views, which means that the clickbait phenomenon is not prominent in the data science articles of WeChat official accounts. Finally, the regression analysis results are discussed from subject perspective, writer perspective and reader perspective.