Multiscale event detection in social media
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10618_2015_421_ReferencePDF.pdf
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Author(s)
Dong, Xiaowen
Mavroeidis, Dimitrios
Calabrese, Francesco
Frossard, Pascal
Date Issued
June 2015
Journal
Data Mining and Knowledge Discovery
Publisher
Springer US
Citation
Dong, Xiaowen, Dimitrios Mavroeidis, Francesco Calabrese, and Pascal Frossard. “Multiscale Event Detection in Social Media.” Data Min Knowl Disc 29, no. 5 (June 13, 2015): 1374–1405.
Version
Author's final manuscript
Abstract
Event detection has been one of the most important research topics in social media analysis. Most of the traditional approaches detect events based on fixed temporal and spatial resolutions, while in reality events of different scales usually occur simultaneously, namely, they span different intervals in time and space. In this paper, we propose a novel approach towards multiscale event detection using social media data, which takes into account different temporal and spatial scales of events in the data. Specifically, we explore the properties of the wavelet transform, which is a well-developed multiscale transform in signal processing, to enable automatic handling of the interaction between temporal and spatial scales. We then propose a novel algorithm to compute a data similarity graph at appropriate scales and detect events of different scales simultaneously by a single graph-based clustering process. Furthermore, we present spatiotemporal statistical analysis of the noisy information present in the data stream, which allows us to define a novel term-filtering procedure for the proposed event detection algorithm and helps us study its behavior using simulated noisy data. Experimental results on both synthetically generated data and real world data collected from Twitter demonstrate the meaningfulness and effectiveness of the proposed approach. Our framework further extends to numerous application domains that involve multiscale and multiresolution data analysis.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1007/s10618-015-0421-2
