  The purpose of this study was to do a dataset distribution analysis, a
classification performance analysis, and a topical analysis concerning what
people are tweeting about four disease characteristics: symptoms, transmission,
prevention, and treatment. A combination of natural language processing and
machine learning techniques were used to determine what people are tweeting
about Zika. Specifically, a two-stage classifier system was built to find
relevant tweets on Zika, and then categorize these into the four disease
categories. Tweets in each disease category were then examined using latent
dirichlet allocation (LDA) to determine the five main tweet topics for each
disease characteristic. Results 1,234,605 tweets were collected. Tweets by
males and females were similar (28% and 23% respectively). The classifier
performed well on the training and test data for relevancy (F=0.87 and 0.99
respectively) and disease characteristics (F=0.79 and 0.90 respectively). Five
topics for each category were found and discussed with a focus on the symptoms
category. Through this process, we demonstrate how misinformation can be
discovered so that public health officials can respond to the tweets with
misinformation.
