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Master of Science / Department of Computing and Information Sciences / Doina Caragea / As the web pages continuously change and their number grows exponentially, the need for genre classification of web pages also increases. One simple reason for this is given by the need to group web pages into various genre categories in order to reduce the complexities of various web tasks (e.g., search). Experts unanimously agree on the huge potential of genre classification of web pages. However, while everybody agrees that genre classification of web pages is necessary, researchers face problems in finding enough labeled data to perform supervised classification of web pages into various genres. The high cost of skilled manual labor, rapid changing nature of web and never ending growth of web pages are the main reasons for the limited amount of labeled data. On the contrary unlabeled data can be acquired relatively inexpensively in comparison to labeled data. This suggests the use of semi-supervised learning approaches for genre classification, instead of using supervised approaches. Semi-supervised learning makes use of both labeled and unlabeled data for training - typically a small amount of labeled data and a large amount of unlabeled data. Semi-supervised learning have been extensively used in text classification problems. Given the link structure of the web, for web-page classification one can use link features in addition to the content features that are used for general text classification. Hence, the feature set corresponding to web-pages can be easily divided into two views, namely content and link based feature views. Intuitively, the two feature views are conditionally independent given the genre category and have the ability to predict the class on their own. The scarcity of labeled data, availability of large amounts of unlabeled data, richer set of features as compared to the conventional text classification tasks (specifically complementary and sufficient views of features) have encouraged us to use co-training as a tool to perform semi-supervised learning. During co-training labeled examples represented using the two views are used to learn distinct classifiers, which keep improving at each iteration by sharing the most confident predictions on the unlabeled data. In this work, we classify web-pages of .eu domain consisting of 1232 labeled host and 20000 unlabeled hosts (provided by the European Archive Foundation [Benczur et al., 2010]) into six different genres, using co-training. We compare our results with the results produced by standard supervised methods. We find that co-training can be an effective and cheap alternative to costly supervised learning. This is mainly due to the two independent and complementary feature sets of web: content based features and link based features.

Information systems researchers typically use self-report measures, such as questionnaires to study consumers’ online risk perception. The self-report approach captures the conscious perception of online risk but not the unconscious perception that precedes and dominates human being’s decision-making. A theoretical model in which implicit risk perception precedes explicit risk evaluation is proposed. The research model proposes that implicit risk affects both explicit risk and the attitude towards online purchase. In a direct path, the implicit risk affects attitude towards purchase. In an indirect path, the implicit risk affects explicit risk, which in turn affects attitude towards purchase. The stimulus used was a questionable web site offering pre-paid credit card services. Data was collected from 150 undergraduate students enrolled in a university. Implicit risk was measured using methods developed in social psychology, namely, single category-implicit association test. Explicit risk and attitude towards purchase were measured using a well-known instrument in the e-commerce risk literature. Preliminary, unconditioned analysis suggested that (a) implicit risk does not affect explicit risk, (b) explicit risk does not affect attitude to purchase, and (c) implicit risk does not affect attitude towards purchase.