<p dir="ltr">Easy access, variety of content, and fast widespread interactions are some of the reasons that have made social media increasingly popular in our society. This has lead to many people use social media everyday for a variety of reasons, such as interacting with friends or consuming news content. Thus, understanding content on social media is more important than ever.</p><p dir="ltr">An increased understanding on social media can lead to improvements on a large number of important tasks. In this work, we particularly focus on fake news detection and political bias detection. Fake news, text published by news sources with an intent to spread misinformation and sway beliefs, is ever prevalent in today's society. Detecting it is an important and challenging problem to prevent large scale misinformation and maintain a healthy society. In a similar way, detecting the political bias of news content can provide insights about the different perspectives on social media.</p><p dir="ltr">In this work, we view the problem of understanding social media as reasoning over the relationships between sources, the articles they publish, and the engaging users. We start by analyzing these relationships in a graph-based framework, and then use Large Language Models to do the same. We hypothesize that the key to understanding social media is understanding these relationships, such as identifying which users have similar perspectives, or which articles are likely to be shared by similar users.</p><p dir="ltr">Throughout this thesis, we propose several frameworks to capture the relationships on social media better. We initially tackle this problem using supervised learning systems, improving them to achieve strong performance. However, we find that automatedly modeling the complexities of the social media landscape is challenging. On the contrary, having humans analyze and interact with all news content to find relationships, is not scalable. Thus, we then propose to approach enhance our supervised approaches by approaching the social media understanding problem \textit{interactively}, where humans can interact to help an automated system learn a better social media representation quality.</p><p dir="ltr">On real world events, our experiments show performance improvements in detecting the factuality and political bias of news sources, both when trained with and without minimal human interactions. We particularly focus on one of the most challenging setups of this task, where test data is unseen and focuses on new topics when compared with the training data. This realistic setting shows the real world impact of our work in improving social media understanding.</p>