Publication Date

2019

Document Type

Thesis

Committee Members

Tanvi Banerjee, Ph.D. (Advisor); William Romine, Ph.D. (Committee Member); Travis Doom, Ph.D. (Committee Member)

Degree Name

Master of Science in Computer Engineering (MSCE)

Abstract

Educating people about vaccination tends to target vaccine acceptance and reduction of hesitancy. Social media provides a promising platform for studying public perception regarding vaccination. In this study, we harvested tweets over a year related to vaccines from February 2018 to January 2019. We present a two-stage classifier to: (1) classify the tweets as relevant or non-relevant and (2) categorize them in terms of pro-vaccination, anti-vaccination, or neutral outlook. We found that the classifier was able to distinguish clearly between anti-vaccination and pro-vaccination tweets, but also misclassified many of these as neutral. Using Latent Dirichlet Allocation, we found that two topics were sufficient to describe the corpus of tweets. These dealt with: (1) consequences of vaccination/non-vaccination, and (2) promotion of vaccination/non-vaccination. Finally, using the NRC emotion lexicon, we found practically significant differences in emotions expressed about vaccination between vaccine outlooks, but no practically significant temporal differences across a year.

Page Count

109

Year Degree Awarded

2019


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