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  Uncovering the Spatiotemporal Patterns of Collective Social Activity

Jankowiak, M., & Gomez Rodriguez, M. (2017). Uncovering the Spatiotemporal Patterns of Collective Social Activity. Retrieved from http://arxiv.org/abs/1701.02740.

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arXiv:1701.02740.pdf (Preprint), 5MB
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File downloaded from arXiv at 2018-03-16 12:31 To appear at the 2017 SIAM International Conference on Data Mining (SIAM SDM)
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 Creators:
Jankowiak, Martin1, Author
Gomez Rodriguez, Manuel2, Author           
Affiliations:
1External Organizations, ou_persistent22              
2Group M. Gomez Rodriguez, Max Planck Institute for Software Systems, Max Planck Society, ou_2105290              

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Free keywords: cs.SI,Computer Science, Data Structures and Algorithms, cs.DS
 Abstract: Social media users and microbloggers post about a wide variety of (off-line) collective social activities as they participate in them, ranging from concerts and sporting events to political rallies and civil protests. In this context, people who take part in the same collective social activity often post closely related content from nearby locations at similar times, resulting in distinctive spatiotemporal patterns. Can we automatically detect these patterns and thus provide insights into the associated activities? In this paper, we propose a modeling framework for clustering streaming spatiotemporal data, the Spatial Dirichlet Hawkes Process (SDHP), which allows us to automatically uncover a wide variety of spatiotemporal patterns of collective social activity from geolocated online traces. Moreover, we develop an efficient, online inference algorithm based on Sequential Monte Carlo that scales to millions of geolocated posts. Experiments on synthetic data and real data gathered from Twitter show that our framework can recover a wide variety of meaningful social activity patterns in terms of both content and spatiotemporal dynamics, that it yields interesting insights about these patterns, and that it can be used to estimate the location from where a tweet was posted.

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Language(s): eng - English
 Dates: 2017-01-102017
 Publication Status: Published online
 Pages: 22 p.
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 Table of Contents: -
 Rev. Type: -
 Identifiers: arXiv: 1701.02740
URI: http://arxiv.org/abs/1701.02740
BibTex Citekey: Jankowiak2017
 Degree: -

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