Date of Award

6-2022

Document Type

Open Access

Degree Name

Bachelor of Science

Department

Computer Science

First Advisor

Nicholas Webb

Keywords

NLP, Xenophobia, Twitter

Abstract

Social media is a major driver of political thought, with platforms like Facebook, Twitter, and TikTok having a massive impact on how people think and vote. For this reason we should take seriously any large shifts in the language used to describe issues or groups on social media, as these are likely to either denote a change in political thought or even forecast the same. Of particular interest, given the international reach of social media, is the way that discussions around foreign relations and immigration play out. In the United States of America online spaces have become the default space for the ongoing discourse around immigration policy and more generally the home of both pro and anti-immigrant activists. Because of the sheer size of social media however, understanding how this discussion actually plays out is very difficult, and being able to meaningfully track changes in the way individuals talk about immigration is even more so. Computational techniques, especially those developed by the subfield of Natural Language Processing (NLP) may be able to fill this gap, and I have spent the last two terms testing various methods for identifying changes in the language used on twitter when discussing immigration. This work has centered around the social media environment of the 2016 Presidential election, which featured bitter arguments around immigration policy. The result of my experimentation has been the ability to discover, under certain conditions, changes in the most important terminology used to discuss immigration as well the identification of what may be a generally useful technique for tracking language change on social media for any subject matter.

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Rights Statement

In Copyright - Educational Use Permitted.