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Extraction of Spatio-Temporal Data for Social Networks

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Book cover The Influence of Technology on Social Network Analysis and Mining

Part of the book series: Lecture Notes in Social Networks ((LNSN,volume 6))

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Abstract

It is often possible to understand group change over time through examining social network data in a spatial and temporal context. Providing that context via text analysis requires identifying locations and associating them with people. Our GeoRef algorithm too automatically does this person-to-place mapping. It involves the identification of location, and uses syntactic proximity of words in the text to link location to person’s name. We describe an application using the algorithm based upon data from the Sudan Tribune divided into three periods in 2006 for the Darfur crisis. Contributions of this paper are (1) techniques to mine for location from text (2) techniques to mine for social network edges (associations between location and person), (3) spatio-temporal maps made from mined data, and (4) social network analysis based on mined data.

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Notes

  1. 1.

    National Geospatial Intelligence Agency gazetteer is at http://earth-info.nga.mil/gns/html/

  2. 2.

    http://www.geonames.org

  3. 3.

    http://www.metacarta.com

  4. 4.

    http://developer.yahoo.com/geo/placemaker/guide

  5. 5.

    Recall that in 2005, a separate government formed in southern Sudan in opposition to the dominating regime, and in early 2006, Sudan rejected United Nations peacekeeping efforts. Many died during the conflict between north and south in 2006. Sudan accepted African Union peacekeeping help in November 2006, and then accepted United Nations peacekeepers in early 2007.

  6. 6.

    On the standard means of evaluation for retrieval sets, see Manning, Raghavan and Schütz [25].

  7. 7.

    The size quoted is as of November 2010.

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Acknowledgements

Thanks are due to Michael Bigrigg for his insights into the network analysis. This work was supported in part by the Air Force Office of Sponsored Research (MURI: Computational Modeling of Cultural Dimensions in Adversary Organizations, FA9550-05-1-0388), the Army Research Institute W91WAW07C0063, and the Army Research Office ERDC-TEC W911NF0710317. Additional support was provided by the Center for Computational Analysis of Social and Organizational Systems (CASOS) at Carnegie Mellon University. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Air Force Office of Sponsored Research, Army Research Institute, the Army Research Office, or the U.S. government.

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Authors and Affiliations

  1. School of Computer Science, Carnegie-Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA

    Judith Gelernter, Dong Cao & Kathleen M. Carley

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  1. Judith Gelernter
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  2. Dong Cao
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  3. Kathleen M. Carley
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Correspondence to Judith Gelernter .

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Editors and Affiliations

  1. Department of Computer Engineering, TOBB University, Sogutozu Cad No. 43, Sogutozu Ankara, Turkey

    Tansel Özyer

  2. Computer Science, University of Calgary, University Dr. NW 2500, Calgary, T2N 1N4, Canada

    Jon Rokne

  3. IPSC, European Commission Joint Research Cent., Via Enrico Fermi 2749, Ispra, 21027, Italy

    Gerhard Wagner

  4. De Wetstraat 16, Leiden, 2332 XT, Netherlands

    Arno H.P. Reuser

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Gelernter, J., Cao, D., Carley, K.M. (2013). Extraction of Spatio-Temporal Data for Social Networks. In: Özyer, T., Rokne, J., Wagner, G., Reuser, A. (eds) The Influence of Technology on Social Network Analysis and Mining. Lecture Notes in Social Networks, vol 6. Springer, Vienna. https://doi.org/10.1007/978-3-7091-1346-2_15

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  • DOI: https://doi.org/10.1007/978-3-7091-1346-2_15

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