Paper Info
Title
GeoScope: online detection of geo-correlated information trends in social networks
Abstract
The First Law of Geography states \"Everything is related to everything else, but near things are more related than distant things\". This spatial significance has implications in various applications, trend detection being one of them. In this paper we propose a new algorithmic tool, GeoScope, to detect geo-trends. GeoScope is a data streams solution that detects correlations between topics and locations in a sliding window, in addition to analyzing topics and locations independently. GeoScope offers theoretical guarantees for detecting all trending correlated pairs while requiring only sub-linear space and running time. We perform various human validation tasks to demonstrate the value of GeoScope. The results show that human judges prefer GeoScope to the best performing baseline solution 4:1 in terms of the geographical significance of the presented information. As the Twitter analysis demonstrates, GeoScope successfully filters out topics without geo-intent and detects various local interests such as emergency events, political demonstrations or cultural events. Experiments on Twitter show that GeoScope has perfect recall and near-perfect precision.
Year
DOI
Venue
2013
10.14778/2732240.2732242
PVLDB
Field
DocType
Volume
Data mining,Data stream mining,Social network,Sliding window protocol,Computer science,Trend detection,Tobler's first law of geography,Database
Journal
7
Issue
ISSN
Citations 
4
2150-8097
29
PageRank 
References 
Authors
0.85
32
4
Name
Order
Citations
PageRank
Ceren Budak11096.60
Theodore Georgiou2442.90
Divyakant Agrawal382011674.75
Amr El Abbadi467671569.95