Authors Xia C., Schwartz, R., Xie K., Krebs A., Langdon A., Ting J. and Naaman M. Abstract With the increasing volume of location-annotated content from various social media platforms like Twitter, Instagram and Foursquare, we now have real-time access to people’s daily documentation of local activities, interests and attention. In this demo paper, we presentCityBeat1,
CategoryResearch
Ensemble: Exploring Complementary Strengths of Leaders and Crowds in Creative Collaboration
A story scene showing (a) the winning draft for a scene. (b) Tabs can be used to switch to different drafts, with the winning draft being shown by default. (c) The scene prompt helps focus contributors on specific writing goals. (d) Contributors can make comments discussing the scene at a high level. Authors Joy Kim,
Robust detection of hyper-local events from geotagged social media data
Architecture of our local event detection system. Including data collector, time-series builder, Gaussian Process regression model, alert engine and classifier. Arrows indicate input and output flow of each module. Authors Xie K., Xia C., Grinberg N., Schwartz R., and Naaman M. Abstract An increasing number of location-annotated content available from social media channels like Twitter,
Making Sense of Cities Using Social Media: Requirements for Hyper-Local Data Aggregation Tools
Examples of geo-tagged social media data visualizations mockups. Clockwise: geo-tagged topic groupings, keywords appearance graphs, volume graph and heat map Authors Schwartz, R., Naaman M., Matni, Z. Abstract As more people tweet, check-in and share pictures and videos of their daily experiences in the city, new opportunities arise to understand urban activity. When aggregated, these