Extracting Semantic Knowledge from Twitter

  • Peter Teufl
  • Stefan Kraxberger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6847)


Twitter is the second largest social network after Facebook and currently 140 millions Tweets are posted on average each day. Tweets are messages with a maximum number of 140 characters and cover all imaginable stories ranging from simple activity updates over news coverage to opinions on arbitrary topics. In this work we argue that Twitter is a valuable data source for e-Participation related projects and describe other domains were Twitter has already been used. We then focus on our own semantic-analysis framework based on our previously introduced Semantic Patterns concept. In order to highlight the benefits of semantic knowledge extraction for Twitter related e-Participation projects, we apply the presented technique to Tweets covering the protests in Egypt starting at January 25 th and resulting in the ousting of Hosni Mubarak on February 11 th 2011. Based on these results and the lessons learned from previous knowledge extraction tasks, we identify key requirements for extracting semantic knowledge from Twitter.


Semantic Patterns Twitter Mining e-Participation Semantic Analysis Trend Analysis Semantic Search Machine Learning Social Network Analysis 


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Copyright information

© IFIP International Federation for Information Processing 2011

Authors and Affiliations

  • Peter Teufl
    • 1
  • Stefan Kraxberger
    • 1
  1. 1.IAIKGraz University of TechnologyGrazAustria

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