Uncovering aspects of places for fitness activities through social media

Abstract

Nowadays, a growing number of people publicly share information about their fitness activities on social media platforms like Twitter or Facebook. These social networks can furnish people with useful information to get an overview of different geographic areas where people can practice different sport-related activities. In this study, we analyze 14 million tweets to identify places to perform fitness activities and uncovering their aspects from twitterers’ opinions. To this end, we apply clustering analysis to uncover places where twitterers perform fitness activities, and then train a text classifier that achieves a score F1 of 76% to discriminate the aspects of fitness places. Using this information, recommender systems can provide useful information to local people or tourists that look for places to do exercise.