Influence of online news features on user behavior for new media

In this study, we sought to elucidate the reading behaviors of users on an online domestic news platform. Our specific objective was to identify which features of online news positively impact the reading behaviors of users (i.e. click-through rate and news article popularity.) For this, we cooperated with Chinatimes.com, one of the largest news media companies in Taiwan. Two experiments were performed using data from the website analytics of Chinatimes.com and from Facebook's graph API: multiple linear regression and decision tree modeling. The results show that multiple linear regression is a significant predictor of news article popularity (R-squared = 0.7731) but not click-through rate. The decision tree model further revealed that users are more likely to read entertainment news articles when articles feature a greater number of images per page; political news is viewed more when the number of keywords is increased; travel, sports, and world news achieve article popularity through sharing on Facebook; and business news attracts more readers when the news source is Chinatimes.com.