The shift from traditional print media to online platforms has revolutionized the way people consume and engage with current events. To enhance user involvement, these platforms typically employ personalization algorithms like recommendation systems, that learn about users’ preferences from their past interactions and suggest relevant content. Nevertheless, the use of such algorithms may result in biased engagement patterns caused by data that was influenced by the recommendation system itself, leading to concerns about "filter bubbles" and "echo chambers". Such entities cause users to be over-exposed to information that conforms with their pre-existing beliefs while limiting exposure to opposing viewpoints. As a result, these types of news consumption habits can bias users, leading to negative consequences such as the hyper- partisanship, online polarization, and the spread of misinformation. In this dissertation we aim to better understand factors that affect short-term and long-term news engagement behavior on social media. To achieve this, we conduct simulation studies to understand which aspects of recommendation systems contribute to filter bubble formation. We propose attention-based neural networks to mitigate these effects in content-based recommenders. In addition, long-term news engagement behavior is examined by analyzing observational data collected from Twitter over a decade. Our analysis focuses on a specific type of engagement behavior where users exhibit distrust towards the news media they engage with and examine its impact on engagement diversity. Finally, we propose forecasting methods to predict future news engagement behavior of users which reveal factors that shape long-term news consumption habits on social media.