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

We 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, clos...

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Veröffentlicht in:Computer graphics forum Jg. 31; H. 3pt4; S. 1275 - 1284
Hauptverfasser: Wang, X., Dou, W., Ma, Z., Villalobos, J., Chen, Y., Kraft, T., Ribarsky, W.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford, UK Blackwell Publishing Ltd 01.06.2012
Schlagworte:
Analysis
Architecture
Computer architecture
Computer graphics
Effectiveness
I.3.2 [Computer Graphics]: Graphics Systems-Distributed/network graphics
Interactive
Mathematical analysis
Media
Monitoring
Precursors
Social networks
Studies
Visual
ISSN:0167-7055, 1467-8659
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:We 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.
Bibliographie:istex:68ED4C674FE044F6F32BF1E181736350EDA74E94
ArticleID:CGF3120
ark:/67375/WNG-9CKM2K3F-Z
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ISSN:0167-7055
1467-8659
DOI:10.1111/j.1467-8659.2012.03120.x