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DTSTART:20191103T020000
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UID:calendar.377976.field_event_date.0@www.wright.edu
DTSTAMP:20260220T000909Z
CREATED:20191114T210421Z
DESCRIPTION:Committee:  Drs. Tanvi Banerjee\, Co-Advisor\, William Romine\,
  Co-Advisor\, and Travis DoomABSTRACT:Educating people about vaccination t
 ends to target vaccine acceptance and reduction of hesitancy Social media 
 provides a promising platform for studying public perception regarding vac
 cination. In this study\, we harvested tweets over a year related to vacci
 nes 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 t
 hese as neutral. Using Latent Dirichlet Allocation\, we found that two top
 ics were sufficient to describe the corpus of tweets. These dealt with: (1
 ) consequences of vaccination/non-vaccination\, and (2) promotion of vacci
 nation/non-vaccination. Finally\, using the NRC emotion lexicon\, we found
  practically significant differences in emotions expressed about vaccinati
 on between vaccine outlooks\, but no practically significant temporal diff
 erences across a year.
DTSTART;TZID=America/New_York:20191119T100000
DTEND;TZID=America/New_York:20191119T120000
LAST-MODIFIED:20191118T125910Z
LOCATION:304 Russ Engineering
SUMMARY:Masters Thesis Defense “Analyzing Public View towards Vaccination u
 sing Twitter” By Rutuja Mahajan
URL;TYPE=URI:/events/masters-thesis-defense-%E2%80%9C
 analyzing-public-view-towards-vaccination-using-twitter%E2%80%9D-rutuja
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