Computational linguistics using social media to understand immigrant sentiment in the United States
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] International migration is increasing throughout the world, including migration to the United States. This increase corresponds with a global rise in support for anti-immigrant, nationalist political parties. Supporters of these ideologies cite being fearful of perceived economic and violence threats posed by international migrant populations. Many of these suspicions are a form of prejudice: feelings or opinions toward a group of people that are not rooted in actual experience. Prejudice often results in hostility. One way in which anti-migrant hostility has manifested is in the form of angry rhetoric on social media platforms, such as Twitter. Chapter 2 examines the demographic landscape of migrant and language speaking communities in cities across the United States. Conforming to other characteristics of cities, these demographic indicators exhibit patterns consistent with overall population growth, such that larger cities have proportionally more migrant and minority language speaking communities than smaller cities. Chapter 3 introduces a model for detecting anger in tweets using a gated recurrent unit neural network. Tweets about immigration were used to assess the performance of the model. Results indicate that certain words used in reference to migrant populations attach different levels of anger. Chapter 4 tests two popular social scientific theories of prejudice: namely, contact and group threat theories. These tests used city-level demographic data along with the distance to the U.S.-Mexico border as predictors of anger in tweets about migrant populations in the United States. Results show that both theories could be operating such that cities with more migrants are less angry overall with respect to immigration. However, when holding the other predictors constant, anger increases in cities the closer they are to the border.
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