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.
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Notes
Throughout the paper, we use “scales” and “resolutions” interchangeably.
Since we are interested in local clusters, we apply the non-recursive version of the Louvain method which stops after the first iteration.
One may think of applying LED with small values for \(T_t\) and \(T_d\) before grouping similar clusters together using a second clustering step. In fact, the second and further iterations of the Louvain method already offers such a grouping. Alternatively, a hierarchical clustering algorithm can be applied to the clusters obtained by LED. However, such further grouping process does not usually lead to a clear interpretation in terms of the spatiotemporal scales of the resulting event clusters, and it is often difficult to decide when to stop the recursive process and output the eventual clusters.
When two tweets come from the same geographical cell, they would share the same time series for any common term. In this case, the correlation of DWT coefficients would always be 1 regardless of the level at which we compute the transform (or the temporal scale). This special case can be interpreted as only keeping the spatial constraint in LED but relaxing the temporal constraint.
The direct usage of the CSR tests for the whole input tweet stream would not be particularly informative since both of our algorithms construct a similarity graph between tweets where the edge weights (i.e., the similarities between tweets) are based on the terms that two tweets have in common. In this case, noise or event-irrelevant tweets would affect the construction of the graph only when two “noise” tweets have a term in common (i.e., resulting in the formation of an edge that connects event-irrelevant tweets in the tweet similarity graph).
F-measure is computed as \((1+\beta ^2) \cdot \frac{\textit{Precision} \cdot \textit{Recall}}{(\beta ^2 \cdot \textit{Precision}) + \textit{Recall}}.\)
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Acknowledgments
X. Dong is supported by a Swiss National Science Foundation Mobility Fellowship. This work was done while X. Dong and D. Mavroeidis were at IBM Research - Ireland.
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Responsible editors: Joao Gama, Indre Zliobaite, Alipio Jorge, and Concha Bielza.
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Dong, X., Mavroeidis, D., Calabrese, F. et al. Multiscale event detection in social media. Data Min Knowl Disc 29, 1374–1405 (2015). https://doi.org/10.1007/s10618-015-0421-2
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DOI: https://doi.org/10.1007/s10618-015-0421-2