CityBeat is multi-platform application for newsrooms and journalists that sources, monitors and analyzes hyper-local information from multiple social media platforms such as Instagram, Twitter and Foursquare, in real time. We use public, geo-tagged, real time data shared via social media services, in order to trace a city’s happenings and dynamics.
During the first year of the Magic grant, we have addressed several complicated technical and journalistic challenges. We generated design requirements from news editors and reporters (Schwartz et al., 2013), developed new algorithms (Xie et al., 2013), and built a fully functional large screen ambient display that is currently in the first phases of deployment (Xia et al., 2014).
The alpha version of CityBeat received a warm welcome. After only a few public presentations in small exclusive forums such as the Hacks and Hackers NYC meetup, we received several invitations from media outlets offering their newsrooms as the testing grounds for our system. Most notably, the New York Times metro desk, Buzzfeed and the Gothamist requested us to deploy a live version in their offices across several screens and projectors.
During the first year of our Magic grant, we worked closely with The New York World editors and reporters. This provided us with indispensable editorial direction for the development of CityBeat, including shaping training data to help the CityBeat algorithm identify true events and reject false events; and making sure the ambient display meets the needs of newsrooms seeking to discover untapped information and images. The World has also used CityBeat to curate coverage of the mayor’s inauguration and find images and sources for news events. CityBeat is on constant display in the newsroom, provoking ongoing discussion and feedback in a live context, and guiding ongoing project development.
Published Research:
Xia C., Schwartz R., Xie K., Krebs A., Langdon A., Ting J., Naaman M., CityBeat: Real-time Social Media Visualization of Hyper-local City Data, In Proceedings of WWW 2014, 23rd International World Wide Web Conference, Seoul, Korea, 2014.
Xie K., Xia C., Grinberg N., Schwartz R., and Naaman M., Robust detection of hyper-local events from geotagged social media data. In Proceedings of the 13th Workshop on Multimedia Data Mining in KDD, 2013.
