In the lead up to the 2016 election, fake news often “outperformed” actual news in users'
social media feeds. This paper attempts to analyze the process in which fake news
proliferates social networking sites and presents a method of understanding and articulating
ways in which personalized feeds are shaped by algorithm-based user feedback. The
algorithm systems are embedded programs that analyze past user data and search history
in combination with other users' searches and history to calculate digital outcomes …