Quantifying Changes in Social Polarization Over Time and Region
| dc.contributor.author | Edwards, David Linville | en |
| dc.contributor.committeechair | Leman, Scotland C. | en |
| dc.contributor.committeemember | Datta, Jyotishka | en |
| dc.contributor.committeemember | Hawdon, James E. | en |
| dc.contributor.committeemember | Van Mullekom, Jennifer Huffman | en |
| dc.contributor.department | Statistics | en |
| dc.date.accessioned | 2024-07-30T08:00:19Z | en |
| dc.date.available | 2024-07-30T08:00:19Z | en |
| dc.date.issued | 2024-07-29 | en |
| dc.description.abstract | Recent studies indicate that Americans have grown increasingly divided and polarized in recent years cite{boxell2022cross}, cite{hawdon2020social}. This research aims to describe and measure polarization trends across a historical archive of US-based, primarily regional, newspapers. The newspapers chosen are from various US markets to capture any regional differences in the discussion of issues/topics. Our modeling approach employs the Structural Topic Model (STM) to identify topics within a given corpus and measure the tonal differences of articles discussing the same topic. Specifically, we use the STM to infer potentially related articles and a sentiment analyzer called VADER to identify topics with a high level of semantic disparity. Using this method, we assess the polarization of developing and evolving topics, such as sports, politics, and entertainment, and compare how polarization between and within these topics has changed over time. Through this, we create topic-specific sentiment distributions, referred to as polarization distributions. We conclude by demonstrating the usefulness of these distributions in identifying polarization and showing how high polarization aligns with significant social events. | en |
| dc.description.abstractgeneral | Most Americans have a sense that their nation is becoming more socially polarized. Numerous studies and anecdotal evidence supports this. Our aim with this work is develop a method to quantify polarization in text media and apply this method to news articles published in local and national newspapers. Using a statistical model we are able to group articles based on a common shared topic. We then analyze the sentiment of each article and evaluate how sentiments for a particular topic change over time. We then compare newspapers based on location, political endorsements, and ownership groups. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:41271 | en |
| dc.identifier.uri | https://hdl.handle.net/10919/120751 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Clustering | en |
| dc.subject | Topic Matching | en |
| dc.subject | Sentiment Differences | en |
| dc.title | Quantifying Changes in Social Polarization Over Time and Region | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Statistics | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |
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