People frequently supplement or have replaced their consumption of news from traditional print, radio, or television news sources with social news consumption from online social media platforms such as Facebook, Twitter, or Reddit. Reliance on social media sites as primary sources of news and information continues to grow and shows little sign of decreasing in the future. Tasked with curating an ever-increasing amount of content, providers leverage user interaction feedback to make decisions about which content to display, highlight, and hide. The sheer volume of new information being produced and consumed only increases the reliance that individuals place on anonymous others to curate and sort the massive amounts of information.  Here, I describe several analyses and predictive models of user-behavior in social news platforms such as: user-interactions that rely on or influence the aggregate, anonymous crowd-ratings used to identify news-worthy content and user-interactions with news sources of varied credibility in particular. The central focus of this work is to understand not only how individuals consume social news, but also how they contribute to the spread and reception of credible news and misinformation. Experimental results and predictive models demonstrate the influence of algorithmic biases on social news consumption patterns and the distinctions in the consumption of, response to, and propagation of information from news sources of varied credibility.