Assessing Antibiotic Sentiments with Online Social Media

Understanding how social network dynamics affect public health outcomes is essential in today’s big data age. This can applied in especially controversial and hot topics such as antibiotic use, where the threat of super bugs loom.  In my project I will analyze sentiment of antibiotic use in social media outlets using machine learning.  Data will be scraped from Twitter and Facebook, ranging from about 30,000-50,000 data points, and analyzed using machine learning algorithms. Positive or negative sentiments on antibiotics will be confirmed with antibiotic usage trends in America. If time permits, the social network analysis (SNA) will show how information flows between each user concerning antibiotic posts.  If the sentiment appears negative, it should correlate with an overall decline in antibiotic use in America. If positive, then it should correlate with the great quantities of antibiotics ingested in today’s age.

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