<?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%">Jiang Yang</style></author><author><style face="normal" font="default" size="100%">Morris, Meredith Ringel</style></author><author><style face="normal" font="default" size="100%">Jaime Teevan</style></author><author><style face="normal" font="default" size="100%">Lada A. Adamic</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%">Culture Matters: A Survey Study of Social Q&amp;A Behavior</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the International Conference on Weblogs and Social Media (ICWSM’11)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">collaborative help</style></keyword><keyword><style  face="normal" font="default" size="100%">collective help</style></keyword><keyword><style  face="normal" font="default" size="100%">intercultural</style></keyword><keyword><style  face="normal" font="default" size="100%">QA</style></keyword><keyword><style  face="normal" font="default" size="100%">social search</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">05/2010</style></date></pub-dates></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;Online social networking tools are used around the world by people to ask questions of their friends, because friends provide direct, reliable, contextualized, and interactive responses. However, although the tools used in different cultures for question asking are often very similar, the way they are used can be very different, reflecting unique inherent cultural characteristics. We present the results of a survey designed to elicit cultural differences in people’s social question asking behaviors across the United States, the United Kingdom, China, and India. The survey received responses from 933 people distributed across the four countries who held similar job roles and were employed by a single organization. Responses included information about the questions they ask via social networking tools, and their motivations for asking and answering questions online. The results reveal culture as a consistently significant factor in predicting people’s social question and answer behavior. The prominent cultural differences we observe might be traced to people’s inherent cultural characteristics (e.g., their cognitive patterns and social orientation), and should be comprehensively considered in designing social search systems.&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%">Starr, Brian</style></author><author><style face="normal" font="default" size="100%">Mark S. Ackerman</style></author><author><style face="normal" font="default" size="100%">Pazzani, Michael</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Do I Care?—Tell me what’s changed on the Web</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the AAAI Spring Symposium on Machine Learning in Information Access</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">agents</style></keyword><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%">social search</style></keyword><keyword><style  face="normal" font="default" size="100%">World Wide Web</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1996</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">Complete</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">119-121</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We describe the Do-I-Care agent, which uses machine learning to detect &quot;interesting&quot; changes to Web pages previously found to be relevant. Because this agent focuses on changes to known pages rather than discovering new pages, we increase the likelihood that the information found will be interesting. The agent’s accuracy in finding interesting changes and in learning is improved by exploiting regularities in how pages are changed. Additionally, these agents can be used collaboratively by cascading them and by propagating interesting findings to other users’ agents.&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%">Starr, Brian</style></author><author><style face="normal" font="default" size="100%">Mark S. Ackerman</style></author><author><style face="normal" font="default" size="100%">Pazzani, Michael</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Do-I-Care: A Collaborative Web Agent</style></title><secondary-title><style face="normal" font="default" size="100%">Conference on Human Factors in Computing Systems (CHI&quot;96)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">agents</style></keyword><keyword><style  face="normal" font="default" size="100%">collaboration</style></keyword><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%">machine learning</style></keyword><keyword><style  face="normal" font="default" size="100%">social filtering</style></keyword><keyword><style  face="normal" font="default" size="100%">social search</style></keyword><keyword><style  face="normal" font="default" size="100%">World Wide Web</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1996</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">Complete</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">v.2, 273–274</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Social filtering and collaborative resource discovery mechanisms often fail because of the extra burden, even tiny, placed on the user. This work proposes an innovative World Wide Web agent that uses a model of collaboration that leverages the natural incentives for individual users to easily provide for collaborative work.&lt;/p&gt;</style></abstract></record></records></xml>