Information and Communication Technologies and Informal Scholarly Communication: A Review of the Social Oriented Research

This article reviews and analyzes findings from research on computer mediated informal scholarly communication. Ten empirical research papers, which show the effects and influences of information & communication technologies (ICTs), or the effects of social contexts on ICTs use in informal scholarly communication, were analyzed and compared. Types of ICTs covered in those studies include e-mails, collaboratories, and electronic forums. The review shows that most of the empirical studies examined the ICTs use effects or consequences. Only a few studies examined the social shaping of ICTs and ICT uses in informal scholarly communication. Based on comparisons of the empirical findings this article summarizes the ICT use effects/consequences as identified in the studies into seven categories and discusses their implications

Soft Methodology for Cost-and-error Sensitive Classification

Many real-world data mining applications need varying cost for different types of classification errors and thus call for cost-sensitive classification algorithms. Existing algorithms for cost-sensitive classification are successful in terms of minimizing the cost, but can result in a high error rate as the trade-off. The high error rate holds back the practical use of those algorithms. In this paper, we propose a novel cost-sensitive classification methodology that takes both the cost and the error rate into account. The methodology, called soft cost-sensitive classification, is established from a multicriteria optimization problem of the cost and the error rate, and can be viewed as regularizing cost-sensitive classification with the error rate. The simple methodology allows immediate improvements of existing cost-sensitive classification algorithms. Experiments on the benchmark and the real-world data sets show that our proposed methodology indeed achieves lower test error rates and similar (sometimes lower) test costs than existing cost-sensitive classification algorithms. We also demonstrate that the methodology can be extended for considering the weighted error rate instead of the original error rate. This extension is useful for tackling unbalanced classification problems.Comment: A shorter version appeared in KDD '1