Social information processing Wikipedia. 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. Collaboration tools e. Wikipedia. Translating tools Duolingo, re. Tvs Msp 250 Star Printer Driver Windows 7. CAPTCHATagging systems social bookmarking e. Flickr, Cite. ULike. Social networking e. Facebook, My. Space, Essembly. Collaborative filtering e. Collective Intelligence Toby Segaran Pdf' title='Collective Intelligence Toby Segaran Pdf' />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 1. Goldberg Manual Of The General Health Questionnaire. The creation of the Oxford English Dictionary in the 1. Current state of knowledgeeditThe website for the AAAI 2. Spring Symposium on Social Information Processing suggested the following topics and questions 2Tagging 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 hopewho 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 contentSocial networks. While users create social networks for a variety of reasons e. 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 communitiesLintelligence collective dsigne les capacits cognitives dune communaut rsultant des interactions multiples entre ses membres ou agents. La connaissance. SPARQL pronounced sparkle, a recursive acronym for SPARQL Protocol and RDF Query Language is an RDF query language, that is, a semantic query language for. Social information processing is an activity through which collective human actions organize knowledge. It is the creation and processing of information by a group. Install python from http Run distributesetup. Vbs Fish Games more. Then, well have easyinstall under Scripts directory. Install NeworkX package for. 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. 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 conceptseditSocial Recommender SystemseditSocial 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. ContenteditSocial 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 followedfollowers. 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 You. Tube videos, question and answer recommendation to question askers and answerers on social question and answer websites, job recommendation Linked. In, news recommendation on social new aggregator sites like Digg, Google. Reader, Reddit etc., short message recommendations on microblogs such as Twitter. Also known as social matching the term is proposed by Terveen and Mc. Donald, people recommender systems deal with recommending people to people on social media. Aspects making people recommender systems distinct from traditional recommender systems and require special attention are basically privacy, trust among users, and reputation. There are several factors which effect the choice of recommendation techniques for people recommendation on social networking sites SNS. Those factors are related to types of relationships among people on social networking sites, such as symmetric vs asymmetric, ad hoc vs long term, and confirmed vs nonconfirmed relationships. The scope of people recommender systems can be categorized into three 3 recommending familiar people to connect with, recommending people to follow and recommending strangers. Recommending strangers is seen as valuable as recommending familiar people because of leading to chances such as exchanging ideas, obtaining new opportunities, and increasing ones reputation. ChallengeseditHandling with social streams is one of the challenges social recommender systems face with. Social stream can be described as the user activity data pooled on newsfeed on social media websites. Social stream data has unique characteristics such as rapid flow, variety of data only text content vs heterogenous content, and requiring freshness. Those unique properties of stream data compared to traditional social media data impose challenges on social recommender systems. Another challenge in social recommendation is performing cross domain recommendation, as in traditional recommender systems. The reason is that social media websites in different domains include different information about users, and merging information within different contexts may not lead to useful recommendations.