The complex dynamics of collaborative tagging
Proceedings of the 16th international conference on World Wide Web
ePart '09 Proceedings of the 1st International Conference on Electronic Participation
Massive Social Network Analysis: Mining Twitter for Social Good
ICPP '10 Proceedings of the 2010 39th International Conference on Parallel Processing
Breaking News Detection and Tracking in Twitter
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Sentiment knowledge discovery in twitter streaming data
DS'10 Proceedings of the 13th international conference on Discovery science
Semantic twitter: analyzing tweets for real-time event notification
BlogTalk'08/09 Proceedings of the 2008/2009 international conference on Social software: recent trends and developments in social software
Semantic Pattern Transformation: Applying Knowledge Discovery Processes in Heterogeneous Domains
Proceedings of the 13th International Conference on Knowledge Management and Knowledge Technologies
Exploring the use of new technologies in participation practices in legislation
Journal of E-Governance
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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 25th and resulting in the ousting of Hosni Mubarak on February 11th 2011. Based on these results and the lessons learned from previous knowledge extraction tasks, we identify key requirements for extracting semantic knowledge from Twitter.