This paper analyzes how different rhetorical attributes in news tweets, specifically analytical, clout, perceptual, and risk language, influence user engagement across publishers with different bias and reliability ratings. Using the LIWC framework to quantify these linguistic dimensions in a 5.5 million tweets dataset covering 1,553 news publishers and capturing over 480 million tweet interactions, we perform and present a category-based analysis that captures the relative impact that such features have on the user engagement rates associated with different political bias and reliability categories. While highly biased and unreliable publishers saw increased engagement for clout and risk language, confirming audience biases, the least biased ones benefited more from analytical language. Perception language, on the other hand, uniformly reduced engagement. These insights not only further our understanding of persuasion tactics but also have implications for curbing misinformation by aligning recommendations with audience veracity and impartiality preferences.

The significant impact of Twitter in news dissemination underscores the need to understand what drives tweet popularity. While the content of an article plays a role, several "content-agnostic" factors also influence tweet popularity. Previous studies have faced challenges in differentiating the effects of content-agnostic factors from content variations. To address this, the paper presents a comprehensive analysis of tweet popularity using a "clone-based" approach. The methodology involves identifying tweets linking the same or similar articles (clones) and studying the factors that make some tweets within clone sets more successful in attracting retweets. The analysis reveals insights into clone set characteristics, winners' success patterns, retweet dynamics over time, domain-based competition, and predictors of success. The findings shed light on the complex nature of popularity and success in social media, providing a deeper understanding of the content-agnostic factors that influence tweet popularity.