Bursty and hierarchical structure in streams
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Parameter free bursty events detection in text streams
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Towards automatic extraction of event and place semantics from flickr tags
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Event detection from flickr data through wavelet-based spatial analysis
Proceedings of the 18th ACM conference on Information and knowledge management
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Is it really about me?: message content in social awareness streams
Proceedings of the 2010 ACM conference on Computer supported cooperative work
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
TwitterMonitor: trend detection over the twitter stream
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Proceedings of the 20th ACM international conference on Information and knowledge management
Discovering geographical topics in the twitter stream
Proceedings of the 21st international conference on World Wide Web
Harnessing the crowds for smart city sensing
Proceedings of the 1st international workshop on Multimodal crowd sensing
Geo-spatial event detection in the twitter stream
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
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An increasing number of location-annotated content available from social media channels like Twitter, Instagram, Foursquare and others are reflecting users' local activities and their attention like never before. In particular, we now have enough available data to start extracting real-time local information from social media. In this paper, we focus on the problem of hyper-local event detection, with the goal of enabling a monitoring and alerts system for public management officers, journalists and other users. We present a method for real-time hyper-local event detection from Instagram photos data, using two computational steps. We first use time series analysis to detect abnormal signals in a small region. We then use a classifier to decide if the detected activity corresponds to an actual event. Testing on a large-scale dataset of New York City photos, our system detects hyper-local events with high accuracy.