I-SI: Scalable Architecture of Analyzing Latent Topical-Level Information From Social Media Data

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dc.contributor.authorWang, Xiaoyuen_US
dc.contributor.authorDou, Wenwenen_US
dc.contributor.authorMa, Zhiqiangen_US
dc.contributor.authorVillalobos, Jeremyen_US
dc.contributor.authorChen, Yangen_US
dc.contributor.authorKraft, Thomasen_US
dc.contributor.authorRibarsky, Williamen_US
dc.contributor.editorS. Bruckner, S. Miksch, and H. Pfisteren_US
dc.date.accessioned2015-02-28T07:03:02Z
dc.date.available2015-02-28T07:03:02Z
dc.date.issued2012en_US
dc.description.abstractWe present a general visual analytics architecture that is designed and implemented to effectively analyze unstructured social media data on a large scale. Pipelined on a high-performance cluster configuration, MPI processing, and interactive visual analytics interfaces, our architecture, I-SI, closely integrates data-driven analytical methods and user-centered visual analytics. It creates a coherent analysis environment for identifying event structures, geographical distributions, and key indicators of emerging events. This environment supports monitoring, analyzing, and responding to latent information extracted from social media. We have applied the I-SI architecture to collect social media data, analyze the data on a large scale and uncover the latent social phenomena. To demonstrate the efficacy and applicability of I-SI, we describe several social media use cases in multiple domains that were evaluated by experts. The use cases demonstrate that I-SI can benefit a range of users by constructing meaningful event structures and identifying precursors to critical events within a rich, evolving set of topics.en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume31
dc.identifier.doi10.1111/j.1467-8659.2012.03120.x
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/j.1467-8659.2012.03120.xen_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltd.en_US
dc.titleI-SI: Scalable Architecture of Analyzing Latent Topical-Level Information From Social Media Dataen_US
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EuroVis12: Eurographics Conference on Visualization