@phdthesis{DeAngelis2011,
abstract = {In concept, online communities allow people to access the wide range of knowledge and abilities of a heterogeneous group of users. In reality, current implementations of various online communities suffer from a lack of participation by the most qualified users. The participation of qualified users, or experts, is crucial to the social welfare and widespread adoption of such systems. This research proposes techniques for identifying the most valuable contributors to several classes of online communities, including question and answer (QA) forums and other content-oriented social networks. Once these target users are identified, content recommendation and novel quantitative in- centives can be used to encourage their participation. This research represents an in-depth investigation into QA systems, while the major findings are widely applicable to online communities in general. An algorithm for recommending content in a QA forum is introduced which can route questions to the most appropriate responders. This increases the efficiency of the system and reduces vi the time investment of an expert responder by eliminating the need to search for potential questions to answer. This recommender is analyzed using real data captured from Yahoo! Answers. Additionally, an incentive mechanism for QA systems based on a novel class of incentives is developed. This mechanism relies on systemic rewards, or rewards that have tangible value within the framework of the online community. This research shows that human users have a strong preference for reciprocal systemic rewards over traditional rewards, and a simulation of a QA system based on an incentive that utilizes these reciprocal rewards outperforms a leading incentive mechanism according to expert participation. An architecture is developed for a QA system built upon content recommendation and this novel incentive mechanism. This research shows that it is possible to identify the most valuable contributors to an online community and motivate their participation through a novel incentive mechanism based on meaningful rewards.},
author = {DeAngelis, David},
booktitle = {Ph.D. Dissertation},
file = {:home/dave/Documents/References/DeAngelis - 2011 - Encouraging expert participation in online communities.pdf:pdf},
pages = {1--177},
school = {The University of Texas at Austin},
title = {Encouraging expert participation in online communities},
type = {Ph.D. Dissertation},
url = {http://repositories.tdl.org/tdl-ir/handle/2152/ETD-UT-2011-08-4161},
year = {2011}
}

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This research represents an in-depth investigation into QA systems, while the major findings are widely applicable to online communities in general. An algorithm for recommending content in a QA forum is introduced which can route questions to the most appropriate responders. This increases the efficiency of the system and reduces vi the time investment of an expert responder by eliminating the need to search for potential questions to answer. This recommender is analyzed using real data captured from Yahoo! Answers. Additionally, an incentive mechanism for QA systems based on a novel class of incentives is developed. This mechanism relies on systemic rewards, or rewards that have tangible value within the framework of the online community. This research shows that human users have a strong preference for reciprocal systemic rewards over traditional rewards, and a simulation of a QA system based on an incentive that utilizes these reciprocal rewards outperforms a leading incentive mechanism according to expert participation. An architecture is developed for a QA system built upon content recommendation and this novel incentive mechanism. 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The participation of qualified users, or experts, is crucial to the social welfare and widespread adoption of such systems. This research proposes techniques for identifying the most valuable contributors to several classes of online communities, including question and answer (QA) forums and other content-oriented social networks. Once these target users are identified, content recommendation and novel quantitative in- centives can be used to encourage their participation. This research represents an in-depth investigation into QA systems, while the major findings are widely applicable to online communities in general. An algorithm for recommending content in a QA forum is introduced which can route questions to the most appropriate responders. This increases the efficiency of the system and reduces vi the time investment of an expert responder by eliminating the need to search for potential questions to answer. This recommender is analyzed using real data captured from Yahoo! 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