<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lada A. Adamic</style></author><author><style face="normal" font="default" size="100%">Zhang, Jun</style></author><author><style face="normal" font="default" size="100%">Bakshy, Eytan</style></author><author><style face="normal" font="default" size="100%">Mark S. Ackerman</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Knowledge Sharing and Yahoo Answers: Everyone Knows Something</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 17th International Conference on World Wide Web</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">expertise finding</style></keyword><keyword><style  face="normal" font="default" size="100%">expertise sharing</style></keyword><keyword><style  face="normal" font="default" size="100%">help seeking</style></keyword><keyword><style  face="normal" font="default" size="100%">knowledge sharing</style></keyword><keyword><style  face="normal" font="default" size="100%">online communities</style></keyword><keyword><style  face="normal" font="default" size="100%">Q&amp;A communities</style></keyword><keyword><style  face="normal" font="default" size="100%">QA communities</style></keyword><keyword><style  face="normal" font="default" size="100%">question answering</style></keyword><keyword><style  face="normal" font="default" size="100%">social network analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">Complete</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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&#039;s curiosity about a countless number of things. In this paper, we seek to understand YA&#039;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&#039; 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.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zhang, Jun</style></author><author><style face="normal" font="default" size="100%">Mark S. Ackerman</style></author><author><style face="normal" font="default" size="100%">Lada A. Adamic</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">CommunityNetSimulator: Using simulations to study online community networks</style></title><secondary-title><style face="normal" font="default" size="100%">Communities and Technologies 2007</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">community dynamics</style></keyword><keyword><style  face="normal" font="default" size="100%">community strucure</style></keyword><keyword><style  face="normal" font="default" size="100%">incentive structures</style></keyword><keyword><style  face="normal" font="default" size="100%">online communities</style></keyword><keyword><style  face="normal" font="default" size="100%">Q&amp;A communities</style></keyword><keyword><style  face="normal" font="default" size="100%">QA communities</style></keyword><keyword><style  face="normal" font="default" size="100%">reward structures</style></keyword><keyword><style  face="normal" font="default" size="100%">simulation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">Complete</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">295–321</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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:&lt;/p&gt;&lt;p&gt;❑ About.com, a popular help site (http://about.com), boasts 30 million distinct users each month&lt;/p&gt;&lt;p&gt;❑ Knowledge-iN, a Korean site (http://kin.naver.com/), has accumulated 1.5 million question and answers.&lt;/p&gt;&lt;p&gt;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 &amp;nbsp;algorithms (e.g., [4], [10]). These are largely focused on social network characteristics of these communities.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zhang, Jun</style></author><author><style face="normal" font="default" size="100%">Mark S. Ackerman</style></author><author><style face="normal" font="default" size="100%">Lada A. Adamic</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Expertise networks in online communities: structure and algorithms</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 16th international conference on World Wide Web (WWW&#039;07)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">expert locators</style></keyword><keyword><style  face="normal" font="default" size="100%">expertise finding</style></keyword><keyword><style  face="normal" font="default" size="100%">help seeking</style></keyword><keyword><style  face="normal" font="default" size="100%">online communities</style></keyword><keyword><style  face="normal" font="default" size="100%">simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">social network analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">05/2007</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">Complete</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pages><style face="normal" font="default" size="100%">221–230</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Web-based communities have become important places for people to seek and share expertise. We find that networks in these communities typically differ in their topology from other online networks such as the World Wide Web. Systems targeted to augment web-based communities by automatically identifying users with expertise, for example, need to adapt to the underlying interaction dynamics. In this study, we analyze the Java Forum, a large online help-seeking community, using social network analysis methods. We test a set of network-based ranking algorithms, including PageRank and HITS, on this large size social network in order to identify users with high expertise. We then use simulations to identify a small number of simple simulation rules governing the question-answer dynamic in the network. These simple rules not only replicate the structural characteristics and algorithm performance on the empirically observed Java Forum, but also allow us to evaluate how other algorithms may perform in communities with different characteristics. We believe this approach will be fruitful for practical algorithm design and implementation for online expertise-sharing communities.&lt;br&gt;&amp;nbsp;&lt;/p&gt;</style></abstract></record></records></xml>