Analyzing Sensationalism in News on Twitter (X): Clickbait Journalism by Legacy vs. Online-Native Outlets and the Consequences for User Engagement

News consumption patterns have evolved as people increasingly get news through social media. A by-product of this change has been the rise of “clickbaits”—a form of journalism that utilizes sensationalist techniques to lure readers into clicking on links. Although clickbaits have become common, not much is known about the use of this journalistic practice. This study empirically investigates whether online-native news outlets utilize more sensational features than legacy news outlets to promote their news stories on Twitter (now X). The impact of sensationalism on subsequent user engagement metrics is also tested. First, a manual content analysis of 1,440 tweets by news media organizations was conducted, identifying sensationalism through ten features. Second, an automated content analysis was conducted on a dataset of 25,600 tweets, replicating the manual coding process. The results confirm that online-native outlets (Huffington Post, BuzzFeed) utilized more sensationalism than online legacy outlets (USA Today, L.A. Times). Furthermore, a positive relationship was found between the number of sensationalist features employed and the favorite count, confirming the effectiveness of clickbaits. The automated content analysis by-and-large corroborated the findings of the manual annotation, and additionally revealed a positive effect of sensationalism on the number of retweets.