University of Alberta computing scientists develop machine learning tool to harness the power of Twitter for understanding health and wellness.
A tool based on new machine learning is using Twitter to understand trends in health and wellness, according to a new paper by University of Alberta computing scientists. The tool, called Grebe, used aggregated data from Twitter to better understand health and wellness in Alberta, and across Canada.
“We use machine learning to determine where the location tweets refer to, the dimension of health they are related to, and the emotions expressed in each tweet,” said Osmar Zaiane, professor in the Department of Computing Science. “If we can do this properly, we can get a better understanding of what it’s actually like to live in a particular place, in terms of health and wellness.”
Across Canada and around the world, public health professionals are interested in understanding trends of health and wellness in particular cities or provinces. Data generally comes in the form of self reports or in data provided by health care providers, such as doctor’s offices and hospitals.
Grebe harnesses the power of machine learning to aid the work of health monitoring networks such as those of thePublic Health Agency of Canada and the Centers for Disease Control and Prevention (CDC) in the United States.
“Public health experts are interested in knowing what’s happening in a particular city or province,” said Zaiane. “While surveys are useful forms of gathering information, self-reports can also be unreliable or inaccurate. This type of tool allows public health experts to study people’s behaviour, in addition to their self-reports.”
The scientists used machine learning to identify six different dimensions of health—physical, emotional, occupational, social, spiritual, and intellectual—as well as the emotions expressed in each tweet and the relevant location. The project began with Edmonton, then Alberta, and has since been applied to all Canadian provinces.
“Our goal wasn’t to find the trends themselves. Rather, our goal was to build a tool that will let public health professionals and sociologists analyze these trends,” explained Zaiane. “This tool allows experts to go through another medium—in this case Twitter—to verify trends that they’ve found elsewhere, such as through surveys, as well as verifying other research.”
Once complete, Grebe will be made publicly available as well as open access. The paper, “Context Prediction in the Social Web Using Applied Machine Learning: A Study of Canadian Tweeters,” was presented at the 2018 IEEE/WIC/ACM International Conference on Web Intelligence (doi: 10.1109/WI.2018.00-85).