Context Prediction in the Social Web Using Applied Machine Learning: A Study of Canadian Tweeters

In this ongoing work, we present the Grebe social data aggregation framework for extracting geo-fenced Twitter data for analysis of user engagement in health and wellness topics. Grebe also provides various visualization tools for analyzing temporal and geographical health trends. Grebe currently has over 18 million indexed public tweets, and is the first of its kind for Canadian researchers. The large dataset is used for analyzing three types of contexts: geographical context via prediction of user location using supervised learning, topical context via determining health-related tweets using various learning approaches, and affective context via sentiment analysis of tweets using rule-based methods. For the first, we define user location as the position from which users are posting a tweet and use standard precision metrics for evaluation with promising results for predicting provinces and cities from tweet text. For the second, we use a broader definition of health using the six dimensions of wellness model and evaluate using manually annotated documents with good results using supervised and semi-supervised machine learning. For the third, we use the indexed tweets to show current trends in emotions and opinions and demonstrate trends in polarity and emotions across various Canadian provinces. The combination of these contexts provides useful insights for digital epidemiology. Ultimately, the vision of Grebe is to provide researchers with Canada-specific social web datasets through an open source platform with an accessible RESTful API, and this paper showcases Grebe's potential and presents our progress towards achieving these goals.