Week 10 Readings

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This week, there are 3 relatively short papers to read and prepare to discuss.  The focus in two is on using twitter as data to answer questions and in the other on developing a visual analytics methods and demonstrating its application.  As you will see, two of the papers are about using twitter data to address empirical questions about human behavior, one is a visual analytics paper (not related to social media or social data). For the papers about using twitter data to answer questions, consider how methods from the visual analytics paper and/or other visual analytics papers we have read (particularly those from last week), might be adapted to the analytical tasks.

  • Crandall, D.J., Backstrom, L., Cosley, D., Suri, S., Huttenlocher, D. and Kleinberg, J. 2010: Inferring social ties from geographic coincidences. Proceedings of the National Academy of Sciences 107, 22436-22441. {This paper presents a statistical modeling-based approach to answering the question of whether proximity in time and space is evidence of social ties.  There is no visual analytics, but there is a clear space and time component to analysis and the potential for visual analytics to be applied.}.  Suggested by: Mo Yu
  • Paul, M.J. and Dredze, M. 2011: You are what you tweet: Analyzing Twitter for public health. Fifth International AAAI Conference on Weblogs and Social Media (ICWSM 2011).  {This paper focused on leveraging twitter as input to research and practice in public health. A specific focus is on what the authors call their Ailment Topic Aspect Model, which is used to create structured information from tweets that is applied to creating public health metrics.}. Suggested by: Beatrice Abiero
  • Sips, M., Kothur, P., Unger, A., Hege, H.-C. and Dransch, D. 2012: A Visual Analytics Approach to Multiscale Exploration of Environmental Time Series. IEEE VAST, Seattle: IEEE, 2899-2907. {This paper, just presented last week at IEEE VAST focuses on a new method for understanding temporal information.  The application domain is geographic, specifically environmental science; but the method has the potential to be broadly applicable}.  Suggested by:  Sam Stehle

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