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Social information processing is "an activity through which collective human actions organize knowledge."〔 〕 It is the creation and processing of information by a group of people. As an academic field Social Information Processing studies the information processing power of networked social systems. Typically computer tools are used such as: * Authoring tools: e.g., blogs * Collaboration tools: e.g., wikis, in particular, e.g., Wikipedia * Translating tools: Duolingo, reCAPTCHA * Tagging systems (social bookmarking): e.g., del.icio.us, Flickr, CiteULike * Social networking: e.g., Facebook, MySpace, Essembly * Collaborative filtering: e.g., Digg, the Amazon Product Recommendation System, Yahoo! Answers, Urtak Although computers are often used to facilitate networking and collaboration, they are not required. For example the ''Trictionary'' in 1982 was entirely paper and pen based, relying on neighborhood social networks and libraries. The creation of the ''Oxford English Dictionary'' in the 19th century was done largely with the help of anonymous volunteers organized by help wanted adds in newspapers and slips of paper sent through the postal mail. ==Current state of knowledge== The website for the AAAI 2008 Spring Symposium on Social Information Processing suggested the following topics and questions:〔http://www.isi.edu/~lerman/sss07/〕 ; Tagging : Tagging has already attracted the interest of the AI community. While the initial purpose of tagging was to help users organize and manage their own documents, it has since been proposed that collective tagging of common documents can be used to organize information via an informal classification system dubbed a folksonomy. There is hope that folksonomies will eventually help fulfill the promise of the Semantic Web. ; Human-based computation and collective intelligence : What type of problems are amenable to human swarm computing approaches? How can we design the "wisdom of crowds" effect to benefit our problem solving needs? ; Incentives to participation : How to elicit quality metadata and content from users? How can users resistant to tagging be encouraged to tag content? ; Social networks : While users create social networks for a variety of reasons --- e.g., to track lives of friends or work or opinions of the users they respect --- network information is important for many applications. Globally, an information ecosystem may arise through the interactions among users, and between users and content. A community of users interested in a specific topic may emerge over time, with linkages to other communities giving insight into relationships between topics. ; Evolution of social media and information ecosystems : How does content, and its quality, change in time? There is increasing interest in peer-production systems, for example in how and why some open-source projects like Linux and Wikipedia are successful. Under what circumstances are user-generated content sites likely to succeed and what implications does this have for information-sharing and learning within communities? ; Algorithms : Before we can harness the power of the social information processing, we need new approaches to structured data analysis, specifically algorithms for synthesizing various types of metadata: e.g., social networks and tagging. Research in this area will provide a principled foundation for the development of new algorithms for social search, information discovery and personalization and other approaches that exploit the power of the social information processing. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「social information processing」の詳細全文を読む スポンサード リンク
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