New York: Using Artificial Intelligence (AI) and data from various social media sites, researchers tracked people’s physical activities, from bowling to crossfit, in a bid to inform future efforts to tackle health disparities. The researchers used machine learning to find and comb through exercise-related tweets from across the US, unpacking regional and gender differences in exercise types and intensity levels.
The researchers compared tweets by men and women and from four different regions of the country: the Northeast, the South, the Midwest and the West. According to the findings, the top exercise terms were “walk”, “dance”, “golf”, “workout”, “run”, “pool”, “hike”, “yoga”, “swim” and “bowl”. Walking was the most popular activity overall, but other activities varied by gender and region.