By Haizheng Zhang, Myra Spiliopoulou, Bamshad Mobasher, C. Lee Giles, Andrew McCallum, Olfa Nasraoui, Jaideep Srivastava, John Yen
This booklet constitutes the completely refereed post-workshop court cases of the ninth foreign Workshop on Mining internet info, WEBKDD 2007, and the first foreign Workshop on Social community research, SNA-KDD 2007, together held in St. Jose, CA, united states in August 2007 together with the thirteenth ACM SIGKDD overseas convention on wisdom Discovery and knowledge Mining, KDD 2007.
The eight revised complete papers provided including an in depth preface went via rounds of reviewing and development and have been rigorously chosen from 23 preliminary submisssions. the improved papers tackle all present matters in internet mining and social community research, together with conventional internet and semantic net functions, the rising functions of the internet as a social medium, in addition to social community modeling and analysis.
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Extra resources for Advances in Web Mining and Web Usage Analysis: 9th International Workshop on Knowledge Discovery on the Web, WebKDD 2007, and 1st International Workshop
Looking for Great Ideas: Analyzing the Innovation Jam 35 4. “ ... ” 5. “It would be nice if multiple movies could play on the same screen and the audience would wear special glasses so they could see only the movie they payed for. ” We can see that the discussion is signiﬁcantly focused on multiple ways of improving the theater experience. 6 Finding Great Ideas: Supervised Analysis Several features are extracted from the Jam data. More emphasis is given to the Phase 2 interactions because of the fact that the finalists were selected from Phase 2 of the Innovation Jam.
Unsupervised Content Analysis The high-level challenge of our analysis of the Innovation Jam data is to identify the keys to success of such an endeavor, in particular, what are the characteristics of discussion threads that lead to innovative and promising ideas? The major diﬀerences between the Jam data and a typical forum are: a) the topics are more concentrated and controlled; b) the contributors are mostly from one organization, and therefore share similar concepts on basic values and what are the “great” ideas; c) the discussion time spans a shorter time.
Sometimes, two contributors might not have any common personnel in the 3 4 5 5 Excluding the questions with less than 10 responses. Threads containing more than one message. For only those contributors whose organizational information was available. For few contributors, it was diﬃcult to obtain the organizational hierarchy information. These cases were eliminated during the computation. Index Description of the Feature T1 Total Number of messages for a particular big idea. T2 Total Number of messages which didn’t receive any further response.