Spam, Opinions, and Other Relationships: Towards a Comprehensive View of the Web Knowledge Discovery

Abstract

“Web mining” or “Web Knowledge Discovery” is the analysis of web resources with data-mining techniques such as classification, clustering, association-rule or graph-structure methods. Its applications pervade much of the software web users interact with on a daily basis: search engines’ indexing and ranking choices, recommender systems’ recommendations, targeted advertising, and many others. An understanding of this fast-moving field is therefore a key component of digital information literacy for everyone and a useful and fascinating extension of knowledge and skills for Information Retrieval researchers and practitioners. This chapter proposes an integrating model of learning cycles involving data, information and knowledge, explains how this model subsumes Information Retrieval and Knowledge Discovery and relates them to one another. We illustrate the usefulness of this model in an introduction to web content/text mining, using the model to structure the activities in this form of Knowledge Discovery. We focus on spam detection, opinion mining and relation mining. The chapter aims at complementing other books and articles that focus on the computational aspects of web mining, by emphasizing the often-neglected context in which these computational analyses take place: the full cycle of Knowledge Discovery, which ranges from application understanding via data understanding, data preparation, modeling and evaluation to deployment.