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Tuesday, January 24 • 4:30pm - 6:00pm
Poster: Identify Major Topics Among Negative Information towards Human Papillomavirus Vaccination on Twitter Using Support Vector Machines and Biterm Topic Model

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HPV vaccines refusal is a serious public health issue. Negative information on Twitter have been found influential to potential consumers on vaccination behaviours. In this work, we presented hybrid machine learning approaches identify major topics among the negative Twitter information on HPV vaccines.
Support Vector Machines (SVM) model was firstly applied identify tweets containing negative information and then Biterm Topic Model (BTM) was leveraged to explore major topics among the negative tweets. 319,612 English tweets that contains HPV vaccines related keywords were collected during study period (11/03/2015 - 11/02/2016). SVM models have identified 133,506 tweets that contains negative information. BTM on the negative tweets generated 15 sets of tweets tokens.
Following manual review of those tokens sets and their associated tweets identified 9 major topics including “pediatricians warnings”, “general adverse reactions”, “ovaries harm”, “death cases”, “studies evidence”, “necessity issues”, “scandal and fraud”, “lawsuits” and “protest and complains”.
A word cloud was used to visualize those topic in the end.

Tuesday January 24, 2017 4:30pm - 6:00pm
BioScience Research Collaborative Event Hall 6500 Main Street, Houston, TX 77030-1402

Attendees (1)