Integrating web-based intelligence retrieval and decision-making from the twitter trends knowledge base

  • Authors:

  • Marc Cheong;Vincent Lee

  • Affiliations:

  • Monash University, Melbourne, Australia;Monash University, Melbourne, Australia

  • Venue:
  • Proceedings of the 2nd ACM workshop on Social web search and mining
  • Year:
  • 2009

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Abstract

Twitter as a microblogging platform has vast potential to become a collective source of intelligence that can be used to obtain opinions, ideas, facts, and sentiments. This paper addresses the issue on collective intelligence retrieval with activated knowledge-base decision making. Our methodology differs from the existing literature in the sense that we are doing analysis on Twitter microblog messages as opposed to traditional blog analysis in the literature which deals with the conventional 'blogosphere'. Another key difference in our methodology is that we apply visualization techniques in conjunction with artificial intelligence-based data mining methods to classify messages dealing with the trend topic. Our methodology also analyzes demographics of the authors of such Twitter messages and attempt to map a Twitter trend into what's going on in the real world. Our findings reveal a pattern behind trends on Twitter, enabling us to see how it 'ticks' and evolves though visualization methods. Our findings also enable us to understand the underlying characteristics behind the 'trend setters', providing us a new perspective on the contributors of a trend.