PARC's Augmented Social Cognition researchers -- who come from cognitive psychology, computer science, HCI, CSCW, and other disciplines -- focus on understanding how to "enhance a group of people's ability to remember, think, and reason". Through Social Web systems like social bookmarking sites, blogs, Wikis, and more, we can finally study, in detail, these types of enhancements on a very large scale.
Here we summarize recent work and early findings such as: (1) how conflict and coordination have played out in Wikipedia, and how social transparency might affect reader trust; (2) how decreasing interaction costs might change participation in social tagging systems; and (3) how computation can help organize user-generated content and metadata.}, acmid = {1559959}, address = {New York, NY, USA}, author = {Chi, Ed H.}, booktitle = {Proceedings of the 2009 ACM SIGMOD International Conference on Management of data}, doi = {10.1145/1559845.1559959}, interhash = {d24a64ce5e95bae4de9329a467342dee}, intrahash = {d09b484b1036ca8273743cac1992dd7f}, isbn = {978-1-60558-551-2}, location = {Providence, Rhode Island, USA}, numpages = {12}, pages = {973--984}, publisher = {ACM}, title = {Augmented social cognition: using social web technology to enhance the ability of groups to remember, think, and reason}, url = {http://doi.acm.org/10.1145/1559845.1559959}, year = 2009 } @inproceedings{donato2010notes, abstract = {Addressing user's information needs has been one of the main goals of Web search engines since their early days. In some cases, users cannot see their needs immediately answered by search results, simply because these needs are too complex and involve multiple aspects that are not covered by a single Web or search results page. This typically happens when users investigate a certain topic in domains such as education, travel or health, which often require collecting facts and information from many pages. We refer to this type of activities as "research missions". These research missions account for 10% of users' sessions and more than 25% of all query volume, as verified by a manual analysis that was conducted by Yahoo! editors.
We demonstrate in this paper that such missions can be automatically identified on-the-fly, as the user interacts with the search engine, through careful runtime analysis of query flows and query sessions.
The on-the-fly automatic identification of research missions has been implemented in Search Pad, a novel Yahoo! application that was launched in 2009, and that we present in this paper. Search Pad helps users keeping trace of results they have consulted. Its novelty however is that unlike previous notes taking products, it is automatically triggered only when the system decides, with a fair level of confidence, that the user is undertaking a research mission and thus is in the right context for gathering notes. Beyond the Search Pad specific application, we believe that changing the level of granularity of query modeling, from an isolated query to a list of queries pertaining to the same research missions, so as to better reflect a certain type of information needs, can be beneficial in a number of other Web search applications. Session-awareness is growing and it is likely to play, in the near future, a fundamental role in many on-line tasks: this paper presents a first step on this path.}, acmid = {1772724}, address = {New York, NY, USA}, author = {Donato, Debora and Bonchi, Francesco and Chi, Tom and Maarek, Yoelle}, booktitle = {Proceedings of the 19th international conference on World wide web}, doi = {10.1145/1772690.1772724}, interhash = {3f9d8d91da75a41a35f8ba521beb559d}, intrahash = {2e2c2c1d1b7fcd30f11cbde5729f554e}, isbn = {978-1-60558-799-8}, location = {Raleigh, North Carolina, USA}, numpages = {10}, pages = {321--330}, publisher = {ACM}, title = {Do you want to take notes?: identifying research missions in Yahoo! search pad}, url = {http://doi.acm.org/10.1145/1772690.1772724}, year = 2010 } @article{chen2007reputation, abstract = {In this paper, we propose a user reputation model and apply it to a user-interactive question answering system. It combines the social network analysis approach and the user rating approach. Social network analysis is applied to analyze the impact of participant users' relations to their reputations. User rating is used to acquire direct judgment of a user's reputation based on other users' experiences with this user. Preliminary experiments show that the computed reputations based on our proposed reputation model can reflect the actual reputations of the simulated roles and therefore can fit in well with our user-interactive question answering system. Copyright © 2006 John Wiley & Sons, Ltd.}, author = {Chen, Wei and Zeng, Qingtian and Wenyin, Liu and Hao, Tianyong}, doi = {10.1002/cpe.1142}, interhash = {c304f655ee6ee183e07192b9fed0d618}, intrahash = {858df3646b706ce6308a12cbf1585d58}, issn = {1532-0634}, journal = {Concurrency and Computation: Practice and Experience}, number = 15, pages = {2091--2103}, publisher = {John Wiley & Sons, Ltd.}, title = {A user reputation model for a user-interactive question answering system}, url = {http://dx.doi.org/10.1002/cpe.1142}, volume = 19, year = 2007 } @incollection{yu2000social, abstract = {Trust is important wherever agents must interact. We consider the important case of interactions in electronic communities, where the agents assist and represent principal entities, such as people and businesses. We propose a social mechanism of reputation management, which aims at avoiding interaction with undesirable participants. Social mechanisms complement hard security techniques (such as passwords and digital certificates), which only guarantee that a party is authenticated and authorized, but do not ensure that it exercises its authorization in a way that is desirable to others. Social mechanisms are even more important when trusted third parties are not available. Our specific approach to reputation management leads to a decentralized society in which agents help each other weed out undesirable players.}, address = {Berlin/Heidelberg}, affiliation = {Department of Computer Science, North Carolina State University, Raleigh, NC 27695-7534, USA}, author = {Yu, Bin and Singh, Munindar}, booktitle = {Cooperative Information Agents IV - The Future of Information Agents in Cyberspace}, doi = {10.1007/978-3-540-45012-2_15}, editor = {Klusch, Matthias and Kerschberg, Larry}, interhash = {1065a4963600ef4f9b4c034d3bbd9a50}, intrahash = {337afcb67138b927b27a9687199e8568}, isbn = {978-3-540-67703-1}, keyword = {Computer Science}, pages = {355--393}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {A Social Mechanism of Reputation Management in Electronic Communities}, url = {http://dx.doi.org/10.1007/978-3-540-45012-2_15}, volume = 1860, year = 2000 } @inproceedings{yu2003searching, abstract = {A referral system is a multiagent system whose member agents are capable of giving and following referrals. The specific cases of interest arise where each agent has a user. The agents cooperate by giving and taking referrals so each can better help its user locate relevant information. This use of referrals mimics human interactions and can potentially lead to greater effectiveness and efficiency than in single-agent systems.Existing approaches consider what referrals may be given and treat the referring process simply as path search in a static graph. By contrast, the present approach understands referrals as arising in and influencing dynamic social networks, where the agents act autonomously based on local knowledge. This paper studies strategies using which agents may search dynamic social networks. It evaluates the proposed approach empirically for a community of AI scientists (partially derived from bibliographic data). Further, it presents a prototype system that assists users in finding other users in practical social networks.}, acmid = {860587}, address = {New York, NY, USA}, author = {Yu, Bin and Singh, Munindar P.}, booktitle = {Proceedings of the second international joint conference on Autonomous agents and multiagent systems}, doi = {10.1145/860575.860587}, interhash = {1d5f1932e29ea02f82948d4efd12a0ad}, intrahash = {c6b422948459e04a86e766055608e55e}, isbn = {1-58113-683-8}, location = {Melbourne, Australia}, numpages = {8}, pages = {65--72}, publisher = {ACM}, title = {Searching social networks}, url = {http://doi.acm.org/10.1145/860575.860587}, year = 2003 } @inproceedings{jurczyk2007discovering, abstract = {Question-Answer portals such as Naver and Yahoo! Answers are quickly becoming rich sources of knowledge on many topics which are not well served by general web search engines. Unfortunately, the quality of the submitted answers is uneven, ranging from excellent detailed answers to snappy and insulting remarks or even advertisements for commercial content. Furthermore, user feedback for many topics is sparse, and can be insufficient to reliably identify good answers from the bad ones. Hence, estimating the authority of users is a crucial task for this emerging domain, with potential applications to answer ranking, spam detection, and incentive mechanism design. We present an analysis of the link structure of a general-purpose question answering community to discover authoritative users, and promising experimental results over a dataset of more than 3 million answers from a popular community QA site. We also describe structural differences between question topics that correlate with the success of link analysis for authority discovery.}, acmid = {1321575}, address = {New York, NY, USA}, author = {Jurczyk, Pawel and Agichtein, Eugene}, booktitle = {Proceedings of the sixteenth ACM conference on Conference on information and knowledge management}, doi = {10.1145/1321440.1321575}, interhash = {1c2953be3517384681b6ac831da2c766}, intrahash = {35394620d2654db8543d5da60f6f00dc}, isbn = {978-1-59593-803-9}, location = {Lisbon, Portugal}, numpages = {4}, pages = {919--922}, publisher = {ACM}, title = {Discovering authorities in question answer communities by using link analysis}, url = {http://doi.acm.org/10.1145/1321440.1321575}, year = 2007 } @inproceedings{agichtein2008finding, abstract = {The quality of user-generated content varies drastically from excellent to abuse and spam. As the availability of such content increases, the task of identifying high-quality content sites based on user contributions --social media sites -- becomes increasingly important. Social media in general exhibit a rich variety of information sources: in addition to the content itself, there is a wide array of non-content information available, such as links between items and explicit quality ratings from members of the community. In this paper we investigate methods for exploiting such community feedback to automatically identify high quality content. As a test case, we focus on Yahoo! Answers, a large community question/answering portal that is particularly rich in the amount and types of content and social interactions available in it. We introduce a general classification framework for combining the evidence from different sources of information, that can be tuned automatically for a given social media type and quality definition. In particular, for the community question/answering domain, we show that our system is able to separate high-quality items from the rest with an accuracy close to that of humans}, acmid = {1341557}, address = {New York, NY, USA}, author = {Agichtein, Eugene and Castillo, Carlos and Donato, Debora and Gionis, Aristides and Mishne, Gilad}, booktitle = {Proceedings of the international conference on Web search and web data mining}, doi = {10.1145/1341531.1341557}, interhash = {72c7bf5d1c983c47bfc3c6cc9084c26c}, intrahash = {29c5c74d95dce215a9692b94fc619839}, isbn = {978-1-59593-927-2}, location = {Palo Alto, California, USA}, numpages = {12}, pages = {183--194}, publisher = {ACM}, title = {Finding high-quality content in social media}, url = {http://doi.acm.org/10.1145/1341531.1341557}, year = 2008 } @article{zheng2011collaborative, abstract = {Objective A full-text search engine can be a useful tool for augmenting the reuse value of unstructured narrative data stored in electronic health records (EHR). A prominent barrier to the effective utilization of such tools originates from users' lack of search expertise and/or medical-domain knowledge. To mitigate the issue, the authors experimented with a ‘collaborative search’ feature through a homegrown EHR search engine that allows users to preserve their search knowledge and share it with others. This feature was inspired by the success of many social information-foraging techniques used on the web that leverage users' collective wisdom to improve the quality and efficiency of information retrieval.Design The authors conducted an empirical evaluation study over a 4-year period. The user sample consisted of 451 academic researchers, medical practitioners, and hospital administrators. The data were analyzed using a social-network analysis to delineate the structure of the user collaboration networks that mediated the diffusion of knowledge of search.Results The users embraced the concept with considerable enthusiasm. About half of the EHR searches processed by the system (0.44 million) were based on stored search knowledge; 0.16 million utilized shared knowledge made available by other users. The social-network analysis results also suggest that the user-collaboration networks engendered by the collaborative search feature played an instrumental role in enabling the transfer of search knowledge across people and domains.Conclusion Applying collaborative search, a social information-foraging technique popularly used on the web, may provide the potential to improve the quality and efficiency of information retrieval in healthcare.}, author = {Zheng, Kai and Mei, Qiaozhu and Hanauer, David A}, doi = {10.1136/amiajnl-2011-000009}, eprint = {http://jamia.bmj.com/content/18/3/282.full.pdf+html}, interhash = {413605f4ed8403324bf4c427e48faea0}, intrahash = {6648ce796254ed83fe853079b7bf2416}, journal = {Journal of the American Medical Informatics Association}, number = 3, pages = {282--291}, title = {Collaborative search in electronic health records}, url = {http://jamia.bmj.com/content/18/3/282.abstract}, volume = 18, year = 2011 } @article{broder2002taxonomy, abstract = {Classic IR (information retrieval) is inherently predicated on users searching for information, the so-called "information need". But the need behind a web search is often not informational -- it might be navigational (give me the url of the site I want to reach) or transactional (show me sites where I can perform a certain transaction, e.g. shop, download a file, or find a map). We explore this taxonomy of web searches and discuss how global search engines evolved to deal with web-specific needs.}, acmid = {792552}, address = {New York, NY, USA}, author = {Broder, Andrei}, doi = {10.1145/792550.792552}, interhash = {1bfc1fd93c01979b73e05ae519a46bce}, intrahash = {4b51890dd2fd0006042d50e73b725ff5}, issn = {0163-5840}, issue_date = {Fall 2002}, journal = {SIGIR Forum}, month = sep, number = 2, numpages = {8}, pages = {3--10}, publisher = {ACM}, title = {A taxonomy of web search}, url = {http://doi.acm.org/10.1145/792550.792552}, volume = 36, year = 2002 } @inproceedings{happel2010considering, abstract = {The notions of collaborative information seeking (CIS) and social search have extended the classical model of information seeking and retrieval. In its core, CIS and social search acknowledge the existence of multiple users and study their implicit and explicit interactions across various dimensions. In this paper, we argue to further extend the scope by introducing information providers as a separate role to complement the process of information seeking with information provision. We briefly describe prototype implementations and identify a number of future research challenges.}, author = {Happel, Hans-Jörg and Mazarakis, Athanasios}, booktitle = {Proceedings of the 2nd International Workshop on Collaborative Information Seeking}, interhash = {294126ce54a14361c63cccb176cb8e37}, intrahash = {d1f723d2636b2a2d3f0ed3e03e04a9a7}, title = {Considering Information Providers in Social Search}, url = {http://workshops.fxpal.com/cscw2010cis/submissions/tmp1C.pdf}, year = 2010 } @article{golovchinsky2009taxonomy, abstract = {People can help other people find information in networked information seeking environments. Recently, many such systems and algorithms have proliferated in industry and in academia. Unfortunately, it is difficult to compare the systems in meaningful ways because they often define collaboration in different ways. In this paper, we propose a model of possible kinds of collaboration, and illustrate it with examples from literature. The model contains four dimensions: intent, depth, concurrency and location. This model can be used to classify existing systems and to suggest possible opportunities for design in this space. }, author = {Golovchinsky, Gene and Pickens, Jeremy and Back, Maribeth}, interhash = {724088df605e0999c8c5d71ff522cc12}, intrahash = {af2e03a464063eb40a1e04389280608c}, journal = {CoRR}, title = {A Taxonomy of Collaboration in Online Information Seeking}, url = {http://arxiv.org/abs/0908.0704}, volume = {abs/0908.0704}, year = 2009 } @inproceedings{agrahri2008people, abstract = {Search engines are among the most-used resources on the internet. However, even today's most successful search engines struggle to provide high quality search results. According to recent studies as many as 50 percent of web search sessions fail to find any relevant results for the searcher. Researchers have proposed social search techniques, in which early searchers provide feedback that is used to improve relevance for later searchers. In this paper we investigate foundational questions of social search. In particular, we directly assess the degree of agreement among users about the relevance ranking of search results. We developed a simulated search engine interface that systematically randomizes Google's normal relevance ordering of the items presented to users. Our results show that (a) people are biased toward items in the top of the search lists, even if the list is randomized; (b) people explicit feedback is not biased and (c) people's shared preferences do not always agree with Google's result order. These results suggest that social search techniques might improve the effectiveness of web search engines.}, acmid = {1454052}, address = {New York, NY, USA}, author = {Agrahri, Arun Kumar and Manickam, Divya Anand Thattandi and Riedl, John}, booktitle = {Proceedings of the 2008 ACM conference on Recommender systems}, doi = {10.1145/1454008.1454052}, interhash = {507646c9d3219ac67da5f03fb2db303c}, intrahash = {77b2f9a1033c50acd438a48a1ecc0fa0}, isbn = {978-1-60558-093-7}, location = {Lausanne, Switzerland}, numpages = {4}, pages = {283--286}, publisher = {ACM}, title = {Can people collaborate to improve the relevance of search results?}, url = {http://doi.acm.org/10.1145/1454008.1454052}, year = 2008 } @article{halvey2010asynchronous, abstract = {There are a number of multimedia tasks and environments that can be collaborative in nature and involve contributions from more than one individual. Examples of such tasks include organising photographs or videos from multiple people from a large event, students working together to complete a class project, or artists and/or animators working on a production. Despite this, current state of the art applications that have been created to assist in multimedia search and organisation focus on a single user searching alone and do not take into consideration the collaborative nature of a large number of multimedia tasks. The limited work in collaborative search for multimedia applications has concentrated mostly on synchronous, and quite often co-located, collaboration between persons. However, these collaborative scenarios are not always practical or feasible. In order to overcome these shortcomings we have created an innovative system for online video search, which provides mechanisms for groups of users to collaborate both asynchronously and remotely on video search tasks. In order to evaluate our system an user evaluation was conducted. This evaluation simulated multiple conditions and scenarios for collaboration, varying on awareness, division of labour, sense making and persistence. The outcome of this evaluation demonstrates the benefit and usability of our system for asynchronous and remote collaboration between users. In addition the results of this evaluation provide a comparison between implicit and explicit collaboration in the same search system.}, author = {Halvey, Martin and Vallet, David and Hannah, David and Feng, Yue and Jose, Joemon M.}, doi = {10.1016/j.ipm.2009.11.007}, interhash = {11f10c5e6d01e3256edc0eb01feebda0}, intrahash = {d042cdee618b3b362368ccc29d0c35ad}, issn = {0306-4573}, journal = {Information Processing & Management}, number = 6, pages = {733--748}, title = {An asynchronous collaborative search system for online video search}, url = {http://www.sciencedirect.com/science/article/pii/S0306457309001447}, volume = 46, year = 2010 } @incollection{shah2012toward, abstract = {Being a new and emerging area, CIS lacks a sophisticated and comprehensive set of theories and models that other fields such as IR and information seeking have enjoyed. Therefore, this chapter is not about definitive theories and models relating to CIS; rather, it shows how traditional information seeking models could help us create similar models for CIS. To situate the discussion on theories and models pertaining to CIS, this chapter provides a brief overview on various models developed for collaboration as well as information seeking. An attempt is then made to show how a model for CIS can be developed using Kuhlthau’s information search process (ISP) model. The success and shortcomings of this approach are shown using data from a user study. Further discussion is provided on understanding and incorporating an affective dimension to create a comprehensive model of CIS.}, address = {Berlin/Heidelberg}, affiliation = {School of Communication & Information, The State University of New Jersey, New Brunswick, NJ, USA}, author = {Shah, Chirag}, booktitle = {Collaborative Information Seeking}, doi = {10.1007/978-3-642-28813-5_5}, interhash = {d4cd17ecd20a58ea55afd3472e11f723}, intrahash = {73025848d0a5447a57d8be6570c907c9}, isbn = {978-3-642-28813-5}, keyword = {Computer Science}, pages = {61--86}, publisher = {Springer}, series = {The Information Retrieval Series}, title = {Toward a Model for CIS}, url = {http://dx.doi.org/10.1007/978-3-642-28813-5_5}, volume = 34, year = 2012 }