This thesis makes use of existing research and data to create a clearer understanding of the impact that bias in news media reporting as well as biased social media algorithms can have on political polarization within the United States. Data is used to highlight the fact that the United States is in a state of polarization that has not been seen before with individuals moving further right and left every year. Furthermore, sharing scores of United States congressmen and women are used to show this news media bias and which major party is most closely aligned with a number of news sources. Furthermore, an examination of existing literature on social media algorithms is used to understand whether social media algorithms are shaping the beliefs of individuals within the United States subconsciously.

Based on the results, this thesis was able to accept one of the hypotheses that biased news reporting has contributed to the increase in polarization of the United States. It appears as though partisan individuals are watching news sources that are biased and becoming even more polarized. A microcosm of this is noted and discussed as the Fox News Effect. The other hypothesis in this thesis is about whether social media algorithms impact polarization. This is a relatively new area of research and the data indicated a picture that was not clear, therefore a conclusion was not reached. However, multiple Pew Research Center studies provided impactful information about the current state and potential power of social media platforms.

This thesis advances existing literature in multiple ways. First, it operates under the premise of the news media and social media algorithms being related. Most research has separated the two as independent topics yet, the lines are consistently being blurred on how to separate the two domains. Additionally, it shows the great power and extent to which the news media and social media algorithms can impact polarization within the nation. Finally, this thesis combines multiple studies in one central location which allows for readers to form a more clear picture of the existing environment of the news media and social media algorithms.