Computational Public Health Analytics

Computational Public Health Science Series:

Reality Mining and Social Media

Elmo Paige

Department of Information Science, CUNY-SPS

Series Abstract

Advancements in data mining and the proliferation of relevant data have led to the new informal field of computational public health science (CPHS) (Pentland, 2009). This field encompasses many different applications of data collection and analysis to further public health efficacy, particularly in epidemic intelligence, health campaigns, and resource planning. The growing diversity and depth of research within this field is a clear indication that it is here to stay, necessitating the need for protocols, policies, and regulatory frameworks to be developed to insure the utility of data, individual rights to privacy, and security of data. There are many areas of research in CPHS that are growing significant bodies of work, including; reality mining, social media mining for epidemic intelligence, public health- geographic information systems, social health campaign strategy analytics, and personal privacy/data ownership rights management.

Reality Mining

Reality mining is the field termed by the Human Dynamics division at MIT's Media Lab, which seeks to mine the growing data accumulating on individuals across all information generating platforms in order to build models of and understand human behavior and social dynamics. The myriad ways to collect data are growing as fast as technology, including; proximity data, motion data, geospatial data, speech data and many more gleaned from cell phones; social media posting data; volunteered purchasing data; survey data; and infinitely more as individuals and researchers dream up new quantities to record . These are all combined using statistical models and machine learning/data mining algorithms to delve into research on social networks, behavior, industrial psycho-dynamics and health issues (Green, 2008). Social network dynamics are crucially important to...