Trust and Reputation Mining in Professional Virtual Communities

Abstract

Abstract. Communication technologies, such as e-mail, instant messaging, discussion forums, blogs, and newsgroups connect people together, forming virtual communities. This concept is not only used for private purposes, but is also attracting attention in professional environments, allowing to consult a large group of experts. Due to the overwhelming size of such communities, various reputation mechanisms have been proposed supporting members with information about people's trustworthiness with respect to their contributions. However, most of today's approaches rely on manual and subjective feedback, suffering from unfair ratings, discrimination, and feedback quality variations over time.To this end, we propose a system which determines trust relationships between community members automatically and objectively by mining communication data. In contrast to other approaches which use these data directly, e.g., by applying natural language processing on log files, we follow a new approach to make contributions visible. We perform structural analysis of discussions, examine interaction patterns between members, and infer social roles expressing motivation, openness to discussions, and willingness to share data, and therefore trust.

Predicting Medical Roles in Online Health Fora

“…Indeed, trust models tested on other social media may be applied either by looking at the structure of the threads (computing scores based on the number of quotes, the number of likes, the number of posts between each post and its replies, etc.) [22], [23] or by inferring these information from the text [24]. In addition to these models, we plan to include the emotional reaction of users to a specific post while computing the trust scores (for example posts arousing the anger of the users).…”

Collaborative Content-Based Method for Estimating User Reputation in Online Forums

“…The first category focus on the structure of the website (including the number of postings, the distance between messages, quotes, citations, etc.) [10], while the second one use the textual content of messages to infer trust and reputation. For example Wanas et al [11] automatically score posts based on their textual content.…”

The Impact of Sentiment-driven Feedback on Knowledge Reuse in Online Communities

“…Researchers have found empathy to be essential in encouraging peers to work together online (Leimeister, Sidiras, & Krcmar, 2006;Maloney-Krichmar & Preece, 2005;Skopik, Truong, & Dustdar, 2009). In the case of online social production communities, the apparent lack of formal authority may be compensated by individuals who mentor and encourage each other towards contributing knowledge (Eseryel, 2009).…”