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dc.contributor.authorSavelyev, Alexander
dc.contributor.authorMacEachren, Alan M.
dc.date.accessioned2019-08-05T14:30:19Z
dc.date.available2019-08-05T14:30:19Z
dc.date.issued2018-12-04
dc.identifier.citationSavelyev, A., & MacEachren, A. M. (2018) Augmenting geovisual analytics of social media data with heterogeneous information network mining—Cognitive plausibility assessment. PLoS ONE 13(12): e0206906.en_US
dc.identifier.issn1932-6203
dc.identifier.urihttps://digital.library.txstate.edu/handle/10877/8450
dc.description.abstractThis paper investigates the feasibility, from a user perspective, of integrating a heterogeneous information network mining (HINM) technique into SensePlace3 (SP3), a webbased geovisual analytics environment. The core contribution of this paper is a user study that determines whether an analyst with minimal background can comprehend the network data modeling metaphors employed by the resulting system, whether they can employ said metaphors to explore spatial data, and whether they can interpret the results of such spatial analysis correctly. This study confirms that all of the above is, indeed, possible, and provides empirical evidence about the importance of a hands-on tutorial and a graphical approach to explaining data modeling metaphors in the successful adoption of advanced data mining techniques. Analysis of outcomes of data exploration by the study participants also demonstrates the kinds of insights that a visual interface to HINM can enable. A second contribution is a realistic case study that demonstrates that our HINM approach (made accessible through a visual interface that provides immediate visual feedback for user queries), produces a clear and a positive difference in the outcome of spatial analysis. Although this study does not aim to validate HINM as a data modeling approach (there is considerable evidence for this in existing literature), the results of the case study suggest that HINM holds promise in the (geo)visual analytics domain as well, particularly when integrated into geovisual analytics applications. A third contribution is a user study protocol that is based on and improves upon the current methodological state of the art. This protocol includes a hands-on tutorial and a set of realistic data analysis tasks. Detailed evaluation protocols are rare in geovisual analytics (and in visual analytics more broadly), with most studies reviewed in this paper failing to provide sufficient details for study replication or comparison worken_US
dc.formatText
dc.format.extent27 pages
dc.format.medium1 file (.pdf)
dc.language.isoen_USen_US
dc.publisherPLoS ONEen_US
dc.sourcePLoS ONE, 2018, Vol. 13, No. 12 : e0206906
dc.subjectGeovisual analytics
dc.subjectSocial media
dc.subjectNetwork mining
dc.subjectHeterogeneous information network mining
dc.subjectHINM
dc.subjectSensePlace3
dc.titleAugmenting Geovisual Analytics of Social Media Data with Heterogeneous Information Network Mining-Cognitive Plausibility Assessmenten_US
txstate.documenttypeArticle
dc.identifier.doihttps://doi.org/10.1371/ journal.pone.0206906
dc.rights.licenseThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source credited.
txstate.departmentGeography


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