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 ads 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:
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 a number of 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.
Key concepts
Social Recommender Systems Social overload corresponds to being imposed to high amount of information and interaction on social web. Social overload causes some challenges from the aspect of both social media websites and their users. Users need to deal with high volume of information and to make decisions among different social network applications whereas social network sites try to keep their existing users and make their sites interesting to users. To overcome social overload, social recommender systems has been utilized to engage users in social media websites in a way that users receive more personalized content using recommendation techniques. Social recommender systems are specific types of recommendation systems being designed for social media and utilizing new sort of data brought by it, such as likes, comments, tags and so on, to improve effectiveness of recommendations. Recommendation in social media have several aspects like recommendation of social media content, people, groups and tags.
Content Social media lets users to provide feedback on the content produced by users of social media websites, by means of commenting on or liking the content shared by others and annotating their own-created content via tagging. This newly introduced metadata by social media helps to obtain recommendations for social media content with improved effectiveness. Also, social media lets to extract the explicit relationship between users such as friendship and people followed/followers. This provides further improvement on collaborative filtering systems because now users can have judgement on the recommendations provided based on the people they have relationships. There have been studies showing the effectiveness of recommendation systems which utilize relationships among users on social media compared to traditional collaborative filtering based systems, specifically for movie and book recommendation. Another improvement brought by social media to recommender systems is solving the cold start problem for new users. Some key application areas of social media content recommendation are blog and blog post recommendation, multimedia content recommendation such as YouTube videos, question and answer recommendation to question askers and answerers on social question-and-answer websites, job recommendation (LinkedIn), news recommendation on social new aggregator sites (like Digg, GoogleReader, Reddit etc.), short message recommendations on microblogs (such as Twitter).
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