Semester

Spring

Date of Graduation

2021

Document Type

Thesis

Degree Type

MS

College

Reed College of Media

Department

Reed College of Media

Committee Chair

Bob Britten

Committee Member

Ashton Marra

Committee Member

John Temple

Committee Member

Rachael Woldoff

Abstract

January 6th, 2021 was a significant moment in the history of the United States of America. Protestors stormed the Capitol building over the results of the 2020 presidential election in which Joseph R. Biden defeated incumbent president Donald J. Trump. The Capitol riots were partially incited by the presence of misinformation on social media and was an example of the power misinformation has. This study presented two questions. Question one pertains to the sentiment analysis of verified Twitter users and their sentiment towards Trump. Question two pertains to analyzing tweets from verified accounts for misinformation between the dates of January 6th, 2021 and January 13th, 2021. To answer these questions, a machine learning sentiment analysis was conducted on 13 randomly selected Twitter accounts with noted liberal and conservative political leanings to assess their sentiment towards Trump. The accounts were analyzed and then categorized as being either anti-Trump or Trump-neutral. Once the accounts were appropriately categorized a collection of their tweets mentioning Trump were documented to create a consecutive day sample to examine their reporting and analyze how misinformation differed between the two. The results of this study show that one, sentiment analysis is a useful tool for examining and categorizing tweets and their overall accounts based on their sentiments and two, that there was a notable difference in the spread of misinformation between the two categories.

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