Scrutinizing News Media Cooperation in Facebook and Twitter

In recent times, news media avail oneself of online social media platforms for news promotion, sharing, and commentary to a large extent mainly in Twitter, Facebook, and Reddit. Therefore, in this paper, researchers have been used machine learning and text mining techniques to attain useful insights from the news media data in social media in-order to understand the factors for gaining large audience attention. Different to the previous studies, analyses of the news media in this paper are based on a set of new features: content features such as the originality of a news item, context features such as time and circadian patterns of a news media, and reader reactions. Our dataset includes 238 K tweets and 128 K Facebook posts of 48 most popular news media shared during May–June 2017. In this paper, we explored news producers, news consumers, inter news production patterns, inter news dissemination behaviors, sharing similar news items within Twitter and Facebook (cross-posts), and news readers reactions on news items. In addition, we investigated the best time period to receive the highest readers’ attention toward their news items as this information is useful for other news media to understand the best time duration to publish news items. Finally, we proposed a predictive model to increase news media popularity among readers and the results manifested that news media should disperse its own content and need to publish at first before other news media publish the same content in social media in order to be popular and attract the attention of readers.