Social Media Clues
While Kolachalama goes small, Elaine Nsoesie is thinking big. The School of Public Health assistant professor of global health is using geotagged tweets to study various aspects of health in different neighborhoods. Nsoesie, a Data Science Faculty Fellow in the Data Science Initiative at the Hariri Institute for Computing and Computational Science & Engineering, was part of a team that mapped 80 million geotagged tweets from more than 600,000 Twitter users to census tracts and zip codes across the United States to develop indicators of happiness, food, and physical activity. She and SPH postdoctoral associate Nina Cesare hope to better understand how discussions of health behaviors on social media differ across demographic groups. They believe that their social media data can lead to new and better ways to assess health indicators in communities across the United States. After all, she says, that information is timely, and collecting it is much less costly than using surveys.
Nsoesie is also working with Margrit Betke, a College of Arts & Sciences professor of computer science, on a project aimed at understanding Kenyan dietary preferences. She and Betke are analyzing four million Instagram images posted by people in the East African country, and hoping to learn if, and in what parts of the country, Kenyans prefer discussing western foods to their native diets. One challenge, says Betke, has been teaching computers to recognize African foods. When they can do that, she says, they can begin to learn how “green” or how “greasy” the Kenyan diet is, and if it varies in urban and rural areas.
Like Nsoesie, Betke works simultaneously on several health-related research projects. One of her most promising, conducted with Terry Ellis (MED’05), a Sargent College of Health & Rehabilitation Sciences assistant professor of physical therapy and director of the Center for Neurorehabilitation, provides home-based physical therapy support to people with Parkinson’s disease. A camera-based AI system in the patient’s home tracks the reach and speed of the patient’s movements and compares them to ideal parameters. Software then sends an assessment to a healthcare provider, who can advise the patient to move faster or slower or further extend their movements. All because the data told them to.
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