TY - CONF T1 - Knowledge Sharing and Yahoo Answers: Everyone Knows Something T2 - Proceedings of the 17th International Conference on World Wide Web Y1 - 2008 A1 - Lada A. Adamic A1 - Zhang, Jun A1 - Bakshy, Eytan A1 - Mark S. Ackerman KW - expertise finding KW - expertise sharing KW - help seeking KW - knowledge sharing KW - online communities KW - Q&A communities KW - QA communities KW - question answering KW - social network analysis AB -

Yahoo Answers (YA) is a large and diverse question-answer forum, acting not only as a medium for sharing technical knowledge, but as a place where one can seek advice, gather opinions, and satisfy one's curiosity about a countless number of things. In this paper, we seek to understand YA's knowledge sharing and activity. We analyze the forum categories and cluster them according to content characteristics and patterns of interaction among the users. While interactions in some categories resemble expertise sharing forums, others incorporate discussion, everyday advice, and support. With such a diversity of categories in which one can participate, we find that some users focus narrowly on specific topics, while others participate across categories. This not only allows us to map related categories, but to characterize the entropy of the users' interests. We find that lower entropy correlates with receiving higher answer ratings, but only for categories where factual expertise is primarily sought after. We combine both user attributes and answer characteristics to predict, within a given category, whether a particular answer will be chosen as the best answer by the asker.

JF - Proceedings of the 17th International Conference on World Wide Web UR - Complete ER - TY - CONF T1 - CommunityNetSimulator: Using simulations to study online community networks T2 - Communities and Technologies 2007 Y1 - 2007 A1 - Zhang, Jun A1 - Mark S. Ackerman A1 - Lada A. Adamic KW - community dynamics KW - community strucure KW - incentive structures KW - online communities KW - Q&A communities KW - QA communities KW - reward structures KW - simulation AB -

Help-seeking communities have been playing an increasingly critical role the way people seek and share information online, forming the basis for knowledge dissemination and accumulation. Consider:

❑ About.com, a popular help site (http://about.com), boasts 30 million distinct users each month

❑ Knowledge-iN, a Korean site (http://kin.naver.com/), has accumulated 1.5 million question and answers.

Many additional sites exist from online stock trading discussions to medicaladvice communities. These range from simple text-based newsgroups to intricate immersive virtual reality multi-user worlds. Unfortunately, the very size of these communities may impede an individual’s ability to find relevant answers or advice. Which replies were written by experts and which by novices? As these help-seeking communities are also often primitive technically, they often cannot help the user distinguish between e.g. expert and novice advice. We would therefore like to find mechanisms to augment their functionality and social life. Research is proceeding to make use of the available structure in online communities to design new systems and  algorithms (e.g., [4], [10]). These are largely focused on social network characteristics of these communities.

However, differing network structures and dynamics will affect possible algorithms that attempt to make use of these networks, but little is known of these impacts.

Accordingly, we developed a CommunityNetSimulator (CNS), a simulator that combines various network models, as well as various new social network analysis techniques that are useful to study online community (or virtual organization) network formation and dynamics.

JF - Communities and Technologies 2007 PB - Springer UR - Complete ER -