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Sci-Fin: Visual Mining Spatial and Temporal Behavior Features from Social Media.

Jiansu Pu1, Zhiyao Teng2, Rui Gong3

  • 1School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China. jiansu.pu@uestc.edu.cn.

Sensors (Basel, Switzerland)
|December 22, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces Social Check-in Fingerprinting (Sci-Fin), a visual analytics system for analyzing complex user check-in data. Sci-Fin helps uncover spatial-temporal behavior patterns for social analysis and business intelligence.

Keywords:
Internet of thingsbig data analysissocial mediaspatial and temporal behaviorsvisual mining

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Area of Science:

  • Computer Science
  • Data Visualization
  • Human-Computer Interaction

Background:

  • User check-in data from social services offers insights into spatial-temporal behaviors, crucial for social analysis and business intelligence.
  • Analyzing this data is challenging due to its complexity and incompleteness.
  • Existing methods lack effective tools for visualizing and mining these intricate behavioral patterns.

Purpose of the Study:

  • To present Social Check-in Fingerprinting (Sci-Fin), a novel visual analytics system designed to facilitate the analysis and visualization of social check-in data.
  • To address the challenges posed by the complexity and incompleteness of check-in records.
  • To enable intuitive representation and mining of high-dimensional user behavior attributes.

Main Methods:

  • Developed a visual analytics system, Sci-Fin, focusing on location, activity, and profile components of user check-in data.
  • Designed visual fingerprints for intuitive representation of high-dimensional attributes.
  • Integrated WorldMapper and Voronoi Treemap into glyph-like designs for visual mining of behavior features.

Main Results:

  • Visual fingerprints effectively summarize interesting features and patterns from diverse check-in locations, activities, and user groups.
  • The system demonstrates effectiveness and usability through extensive case studies on real microblogging data.
  • Novel insights into user behavior patterns were uncovered and discussed.

Conclusions:

  • Sci-Fin provides an effective approach for analyzing and visualizing social check-in data, overcoming common challenges.
  • The system's visual fingerprinting method offers a powerful tool for understanding complex user behaviors.
  • The study highlights the potential of visual analytics in social data mining and business intelligence.