Sentiment analysis and information diffusion on social media: The case of the Zika virus

Abstract

First identified 50 years ago, the Zika virus has recently made global headlines due to a high profile outbreak in Brazil coinciding with the Olympics. Mentions of Zika on social media platforms exploded following initial reports of the out-break, and this unprecedented surge of heterogeneous data can be processed using Big Data analysis techniques to ac-quire further insights and knowledge into general public opinion. Crowd-sourced opinion, in the form of individual tweets, is expected to present a more accurate representation of events than traditional surveys. Twitter data streams have previously been used to predict outcomes of real world events. Twitter data filtered for the keyword "Zika" was subjected to analysis using a sentiment analysis lexicon-based framework to establish the polarity of the messages. This research applies big data analysis techniques to Twitter feeds to examine the diffusion of important information related to the Zika virus.