Learning Algorithms for Keyphrase Extraction
Information Retrieval
Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data
Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Social Computing: From Social Informatics to Social Intelligence
IEEE Intelligent Systems
Finding high-quality content in social media
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Online spam-blog detection through blog search
Proceedings of the 17th ACM conference on Information and knowledge management
Characterizing Network Motifs to Identify Spam Comments
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
Modeling and Data Mining in Blogosphere
Modeling and Data Mining in Blogosphere
How useful are your comments?: analyzing and predicting youtube comments and comment ratings
Proceedings of the 19th international conference on World wide web
Multi-perspective context modelling to augment adaptation in simulated learning environments
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Multinomial naive bayes for text categorization revisited
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
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Media resources in social Web spaces trigger social interactions, as they consist of motivating means to create and exchange user-generated content. The massive social content could provide rich resources towards deriving social profiles to augment user models and improve adaptation in simulated learning environments. However, potentially valuable social contributions can be buried within highly noisy content that is irrelevant or spam. This paper sketches a research roadmap toward augmenting user models with key user characteristics derived from social content. It then focuses on the first step: identifying relevant content to create data corpus about a specific activity. A novel, semantically enriched machine learning approach to filter out the noisy content from social media is described. An application on public comments in YouTube on job interview videos has been made to evaluate the approach. Evaluation results, which illustrate the ability of the approach to filter noise and identify relevant social media content, are analysed.