Social media users share billions of items per year, only a small fraction of which is geotagged. We present a datadriven approach for identifying non-geotagged content items that can be associated with a hyper-local geographic area by modeling the location distributions of n-grams that appear in the text. We explore the trade-off between accuracy and coverage of this method. Further, we explore differences across content received from multiple platforms and devices, and show, for example, that content shared via different sources and applications produces significantly different geographic distributions, and that it is preferred to model and predict location for items according to their source. Our findings show the potential and the bounds of a data-driven approach to assigning location data to short social media texts, and offer implications for all applications that use data-driven approaches to locate content.
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 present CityBeat 1 , a real-time visualization of hyper-local social media content for cities. The main objective of CityBeat is to provide users -with a specific focus on journalists -with information about the city's ongoings, and alert them to unusual activities. The system collects a stream of geo-tagged photos as input, uses time series analysis and classification techniques to detect hyper-local events, and compute trends and statistics. The demo includes a visualization of this information that is designed to be installed on a large-screen in a newsroom, as an ambient display.
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