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  1. Proceedings of the 5th ACM SIGSPATIAL International Workshop on Location-Based Social Networks (LBSN '12)
  2. Traffic observatory: a system to detect and locate traffic events and conditions using Twitter
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The anatomy of Sindbad: a location-aware social networking system
Traffic observatory: a system to detect and locate traffic events and conditions using Twitter
TweoLocator: a non-intrusive geographical locator system for Twitter
Parallelization of ensemble neural networks for spatial land-use modeling
Can off-the-shelf object detectors be used to extract geographic information from geo-referenced social multimedia?
Multimodal geo-tagging in social media websites using hierarchical spatial segmentation
SNAIR: a framework for personalised recommendations based on social network analysis
Exploratory analysis on heterogeneous tag-point patterns for ranking and extracting hot-spot related tags
Public checkins versus private queries: measuring and evaluating spatial preference

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Traffic observatory: a system to detect and locate traffic events and conditions using Twitter

Content Provider ACM Digital Library
Author Gonçalves, Tatiana S. Pappa, Gisele L. Ribeiro, Sílvio S. Oliveira, Diogo Rennó R. Davis, Clodoveu A. Meira, Wagner
Abstract Twitter has become one of the most popular platforms for sharing user-generated content, which varies from ordinary conversations to information about recent events. Studies have already showed that the content of tweets has a high degree of correlation with what is going on in the real world. A type of event which is commonly talked about in Twitter is traffic. Aiming to help other drivers, many users tweet about current traffic conditions, and there are even user accounts specialized on the subject. With this in mind, this paper proposes a method to identify traffic events and conditions in Twitter, geocode them, and display them on the Web in real time. Preliminary results showed that the method is able to detect neighborhoods and thoroughfares with a precision that varies from 50 to 90%, depending on the number of places mentioned in the tweets.
Starting Page 5
Ending Page 11
Page Count 7
File Format PDF
ISBN 9781450316989
DOI 10.1145/2442796.2442800
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2012-11-06
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Traffic Twitter Geocoding
Content Type Text
Resource Type Article
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