Social network analysis of the West African Ebola Outbreak INTRODUCTION The Ebola outbreak in West Africa has drawn the World’s attention given the spread and the severity of the disease. The outbreak started in December 2013 and the statistics as of August 2014 were alarming. The outbreak burdened 4 West African countries out of which Guinea, Sierra Leone and Liberia were the most affected. Since November 2014 when this study began, Ebola has already claimed 4,960 deaths out of a total of 13,268 cases. The goals of this study was to: analyze people’s sentiment, emotion and polarity towards the Ebola outbreak analyze and visualize the Ebola twitter data network We used the hashtag #Ebola to construct a corpus of tweets on the disease. METHODS Data Extraction We used NodeXL excel template to retrieve data from We subsequently analyzed the data with the statistical software R version Data extraction: November 8 th, Twitter data from December 1 st, 2013 up to November 8 th, Number of tweets limited to about 1,000 tweets. Sentiment, Emotion and Polarity Analysis Opinion lexicon of positive and negative words Sentiment score for each individual tweet: score= positive score – negative score The simple voter algorithm was used to classify the tweets by emotions and polarities Text Analysis Wordcloud of 100 frequently used terms in the tweets Wordcloud of the 50 most tweeted hashtags along with #Ebola Network Analysis and visualization

Mild shift of the distribution towards negative score with the majority of the tweets receiving a score of zero. Anger (87% of the tweets) as the strongest emotion on twitter. Majority of the tweets classified as neutral in terms of polarities, violating the first two assumptions. RESULTS: Emotion, Polarity and Text Analysis Wordcloud of 100 most frequent terms used in the tweets 50 most tweeted hashtags along with the hashtag #Ebola

CONCLUSION This study used Twitter data to perform a sentiment and polarity analysis on the West African Ebola outbreak. Ebola-related tweets were collected from the social network using #Ebola. This information could help health authorities or organizations such as the Center for Disease Control and Prevention (CDC) or WHO respond quickly to perceived concerns or misinformation in the public about the Ebola outbreak. Our third assumption holds since major health organizations were identified in the #Ebola Twitter network. The presence of organizations’ Twitter accounts like the ‘msf_espana’ or ‘tamu’ (Texas A&M University) and ‘tamuvet1’ (and Texas A&M University Veterinary team) can be explained by the identification of Ebola cases in Spain and in Texas. The network map also displays Twitter users who retweeted major health organizations like MSF Spain, USAID or WHO. RESULTS: Network Analysis and Visualization