Filter Bubbles have been explored from many angles, usually from a user or its network perspective. This study explores the content of tweets from two distinct groups over the same time period, from September 9th of 2020 to January 8th of 2021. The studied groups are: the 2020 United States of America presidential candidates, Donald Trump and Joe Biden, and users that cited either one. In total, 39,052 tweets were collected, cleaned and treated for topic modelling, which was capable of telling apart topics related to the election runoff, but also tweets about health care, second amendment, big tech and climate change. A data set of 82,097 tweets from 30 chosen users from both 2016 and 2020 elections were sentimentally analysed, searching for signs of polarization. Although it was not possible to understand how users with opposing views understood the same topics, and despite the fact that the signs of polarization could only suggest that the 2020 election was more polarized than the one in 2016, a methodology for text processing was developed and presented - and its results were analysed and discussed.