Examining patterns of influenza vaccination in social media
Traditional data on influenza vaccination has several limitations: high cost, limited coverage of underrepresented groups, and low sensitivity to emerging public health issues. Social media, such as Twitter, provide an alternative way to understand a population's vaccination-related opinions and behaviors. In this study, we build and employ several natural language classifiers to examine and analyze behavioral patterns regarding influenza vaccination in Twitter across three dimensions: temporality (by week and month), geography (by US region), and demography (by gender). Our best results are highly correlated official government data, with a correlation over 0.90, providing validation of our approach. We then suggest a number of directions for future work.
AAAI Workshop - Technical Report
Huang, X., Smith, M., Paul, M., Ryzhkov, D., Quinn, S., & Broniatowski, D. (2017). Examining patterns of influenza vaccination in social media. AAAI Workshop - Technical Report, WS-17-01 - WS-17-15, 542-546. Retrieved from https://digitalcommons.memphis.edu/facpubs/2797