Event Detection and Recommendation Using Social Media for Hyperlocal Communities

Abstract

Individuals often rely on newspapers and television to gather information about the communities they live in to be aware of and to engage with their communities. Social media have become increasingly important as a new source of local information. Thus far, the literature on using social media to create desirable societal benefits, such as in civic awareness and engagement, is still in its infancy in the Information System (IS) field. One key challenge in this research stream is to timely and accurately distill local information from noisy Twitter streams to community members. In this work, we develop several machine learning techniques and a mobile application utilizing these techniques to facilitate information seeking of local communities on Twitter. Specifically, we first propose a novel local event detection algorithm which scans spatially and temporally to detect local events as unusual spikes from a large-scale Twitter feeds. Our algorithm significantly improves several competing methods with much higher precision and recall in detecting local events on a real-world dataset of millions of geotagged tweets. Next, the volume of detected local events can be enormous, especially in large and populous local areas. This motivates us to develop a personalized local event recommender system that systematically integrates several contextual cues such as topical, geographical and social proximity in a learning to rank framework. Based on a comprehensive evaluation with two large-scale datasets of local events, the results show that our approach significantly outperforms competitive methods in precision as well as its robustness. Finally, we build a mobile application to demonstrate the practical value of our event detection and recommendation techniques. This application automatically infers and recommends neighborhood-specific information based on Twitter feeds. We test its effectiveness and usefulness through a comprehensive user study. The outcomes demonstrate that most participants prefer it over Twitter as it is easier to use for exploring their neighborhoods and provides better relevant information about their neighborhoods.