Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/107874
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Type: Conference paper
Title: Sense and focus: towards effective location inference and event detection on Twitter
Author: Zhang, Y.
Szabo, C.
Sheng, Q.
Citation: Proceedings of the 16th International Conference Web Information Systems Engineering, 2015 / vol.Part I, pp.463-477
Publisher: Springer
Issue Date: 2015
Series/Report no.: Lecture Notes in Computer Science vol. 9418
ISBN: 9783319261898
ISSN: 0302-9743
1611-3349
Conference Name: 16th International Conference Web Information Systems Engineering (WISE) (01 Nov 2015 - 03 Nov 2015 : Miami, FL)
Statement of
Responsibility: 
Yihong Zhang, Claudia Szabo, and Quan Z. Sheng
Abstract: Twitter users post observations about their immediate environment as a part of the 500 million tweets posted everyday. As such, Twitter can become the source for invaluable information about objects, locations, and events, which can be analyzed and monitored in real time, not only to understand what is happening in the world, but also an event’s exact location. However, Twitter data is noisy as sensory values, and information such as the location of a tweet may not be available, e.g., only 0.9% of tweets have GPS data. Due to the lack of accurate and fine-grained location information, existing Twitter event monitoring systems focus on city-level or coarser location identification, which cannot provide details for local events. In this paper, we propose SNAF (Sense and Focus), an event monitoring system for Twitter data that emphasizes local events. We increase the availability of the location information significantly by finding locations in tweet messages and users’ past tweets.We apply data cleaning techniques in our system, and with extensive experiments, we show that our method can improve the accuracy of location inference by 5% to 20% across different error ranges. We also show that our prototype implementation of SNAF can identify critical local events in real time, in many cases earlier than news reports.
Keywords: Twitter; Microblog content classification; Location inference; Event detection
Rights: © Springer International Publishing Switzerland
RMID: 0030041184
DOI: 10.1007/978-3-319-26190-4_31
Appears in Collections:Computer Science publications

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