By Haizheng Zhang, Myra Spiliopoulou, Bamshad Mobasher, C. Lee Giles, Andrew McCallum, Olfa Nasraoui, Jaideep Srivastava, John Yen
This ebook constitutes the completely refereed post-workshop court cases of the ninth foreign Workshop on Mining net facts, 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 at the side of the thirteenth ACM SIGKDD overseas convention on wisdom Discovery and knowledge Mining, KDD 2007.
The eight revised complete papers awarded including a close preface went via rounds of reviewing and development and have been conscientiously chosen from 23 preliminary submisssions. the improved papers tackle all present concerns in internet mining and social community research, together with conventional internet and semantic internet functions, the rising purposes of the internet as a social medium, in addition to social community modeling and analysis.
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Additional 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
For example, a questioner may post once in a group and never Looking for Great Ideas: Analyzing the Innovation Jam 23 again, while an answer person may respond to a large number of questions, but never participate in continuing dialogues. A group of conversationalists can be characterized by a large number of posts and replies to each other. While the social network analysis of Innovation Jam participants is beyond the scope of the paper, it is important to note that the social structure of the Jam has an eﬀect on its outcomes.
T5 Total Number of questions asked in that particular idea. T6 Mean of the number of messages for all questions 2 . T7 Standard deviation of the number of messages for all questions 2 . T8 Weighted number of overlapping contributors involved in other big ideas. C1 Mean of the pairwise cosine similarity scores between the threads 3 . C2 Standard deviation of the pairwise scores between the threads 3 . C3 Total number of pairwise scores between all threads. C4 Maximum pairwise score between the threads.
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. A total of eighteen features (three diﬀerent categories) were obtained: 1. Topological Features: Features T1-T8 described in Table 3 correspond to topological features. These features will give some basic intuition about the Phase 2 of the Innovation Jam. It contains information regarding the topology of the messaging including number of messages, number of contributors, number of questions in a given idea, number of responses for each question and so on.