Understanding Actionable Knowledge in Social Media: BBC Question Time and Twitter, a Case Study

Automatically extracting actionable knowledge from on-line social media has attracted a growing interest from both academia and the industry. However, little clarity exists in relation to what actionable knowledge is, whether it can be measured and where it is more likely to be found. This paper makes an attempt at answering the above questions by gaining a better understanding of actionable knowledge in Twitter.

[1]  Mario Cataldi,et al.  Emerging topic detection on Twitter based on temporal and social terms evaluation , 2010, MDMKDD '10.

[2]  Danah Boyd,et al.  Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter , 2010, 2010 43rd Hawaii International Conference on System Sciences.

[3]  J. Sadock.  Speech acts , 2007 .

[4]  Jon Whittle,et al.  VoiceYourView: collecting real-time feedback on the design of public spaces , 2010, UbiComp.

[5]  Susan C. Herring,et al.  Beyond Microblogging: Conversation and Collaboration via Twitter , 2009, 2009 42nd Hawaii International Conference on System Sciences.

[6]  Graham Neubig,et al.  Safety Information Mining — What can NLP do in a disaster— , 2011, IJCNLP.

[7]  Timothy W. Finin,et al.  Why we twitter: understanding microblogging usage and communities , 2007, WebKDD/SNA-KDD '07.

[8]  Mary Beth Rosson,et al.  How and why people Twitter: the role that micro-blogging plays in informal communication at work , 2009, GROUP.

[9]  Paul Rayson,et al.  Classification of Short Text Comments by Sentiment and Actionability for VoiceYourView , 2010, 2010 IEEE Second International Conference on Social Computing.

[10]  Gary King,et al.  A Method of Automated Nonparametric Content Analysis for Social Science , 2010 .

[11]  Galina B. Bolden.  Implementing incipient actions: The discourse marker ‘so’ in English conversation , 2009 .

[12]  Chengqi Zhang,et al.  Domain-Driven Actionable Knowledge Discovery in the Real World , 2006, PAKDD.

[13]  Jenny A. Thomas.  Meaning in Interaction: An Introduction to Pragmatics , 1995 .

[14]  R. Preston McAfee,et al.  Incentivizing high-quality user-generated content , 2011, WWW.

[15]  Lillian Lee,et al.  Opinion Mining and Sentiment Analysis , 2008, Found. Trends Inf. Retr..

[16]  Paul Rayson,et al.  From key words to key semantic domains , 2008 .

[17]  Chris H. Q. Ding,et al.  Automatic topic identification using webpage clustering , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[18]  Gilad Mishne,et al.  Finding high-quality content in social media , 2008, WSDM '08.