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There have been a number of frameworks and models developed to

support different aspects of social computing. Some were developed to deal

with online interaction through the application of computer-mediated communications

tools, whereas others such as social network analyses and reputation

systems were more specific in their focus. While these methodologies are interrelated,

current social computing research has dealt with them as separate aspects.

This paper presents a comprehensive framework for social computing

that aims at integrating all these three aspects into a unified model so that discovery

and exploration of community members are not only made possible, but

also optimized. The novelty of the proposed approach stems from: (1) integration

of social interaction, social network analysis and social reputation domains;

(2) incorporating many flexible and practical features such as individual- and

group-level perceptions of trust in social relations for different social contexts;

and (3) using path-related algorithms for selection and discovery of target

community members. A description of the proposed model and its implementation

are presented. This research is expected to assist online community

members to make decisions that facilitate the discovery of people and their

connections while promoting increased awareness of community structure and

information exposure.