Robust detection of hyper-local events from geotagged social media data

Abstract

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.

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