You Shall Know a Place by the Conversations it Seeds
Pages 359 - 362
Abstract
In this work, we look at problems of urban sensing from the lens of conversations (tweets) on Twitter. Using techniques from statistical natural language processing on geotagged tweets, we identify areas which exhibit similar aggregate behavior, infer the land-use of areas and predict types of individual establishments. We demonstrate our inferences using over two years of Twitter data, for a wide variety of spatial contexts and evaluate our results against existing open data sets. Our results are novel in extremely detailed resolution of their mapping, and demonstrate that tweets can be a very effective urban sensor and in many regards are superior to other data sources for studying urban spaces. Our techniques are language agnostic, and can be applied to any city where enough similar data is available.
References
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J. R. Firth, Papers in Linguistics 19341951. Oxford University Press, 1957.
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M. M. Haklay and P. Weber, "Openstreetmap: User-generated street maps," IEEE Pervasive Computing, vol. 7, no. 4, pp. 12--18, 2008. [Online]. Available: http://dx.doi.org/10.1109/MPRV.2008.80
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T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, "Distributed representations of words and phrases and their compositionality," in 27th Annual Conference on Neural Information Processing Systems 2013., 2013, pp. 3111--3119.
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- You Shall Know a Place by the Conversations it Seeds
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July 2017
698 pages
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Association for Computing Machinery
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Publication History
Published: 31 July 2017
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- Short-paper
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- Refereed limited
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ASONAM '17
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ASONAM '17: Advances in Social Networks Analysis and Mining 2017
July 31 - August 3, 2017
Sydney, Australia
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Overall Acceptance Rate 116 of 549 submissions, 21%
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