16-01-2013, 12:48 PM
LEVERAGING SOCIAL CONTEXT FOR SEARCHING SOCIAL MEDIA
INTRODUCTION
The web is rapidly shifting from content contributed by nameless authors to a \social" web in which almost all content is linked to an author's name. This shift to social media requires a shift in the nature of the search tools needed to effectively extract value from these growing repositories. Users are shifting from just consuming information published by professional editors to contributing blog posts and twitter messages, updating their profiles on Face book and MySpace, asking and answering questions on Yahoo! Answers, authoring and editing articles in Wikipedia, tagging and rating pictures in Flickr and videos in YouTube, and voting for news items on Dig, etc.
Social media is composed of multiple \social" data structures that are often conflated. Social networking sites like Face book and MySpace have highlighted an emerging form of social context composed of a digital record of an individual's set of connections to other users of a given system. These forms of social network services have attracted attention to the idea of personal social networks, directed graphs connecting individuals to their friends, and how these networks have value for forging connections between users. Once generated, these personal social graphs can also serve as a novel form of reference set for collaborative filtering, replacing the association \people who like this also like that" with the association \people who like me also like this". We refer to this social context as the personal social context. The personal social network is only one part of the ways social contexts are relevant for the improvement of search over collectively authored content repositories. In many social media systems, as users interact with others users and their content, a set of linkages is created and recorded in the form of transaction records. When linkage data is extracted from these repositories a social graph can be constructed that reveals patterns associated with the social roles and dynamics of that community. These patterns can be lever- aged to identify key contributors and sources of value as well as the opposite. We refer to this social context as the community social context. Each social context may initially be independent but later gain data based on their interactions. The system can model connections between users with similar interests or behaviors providing guidance about which content is deserving of their trust, and which authors have gained reputations for different patterns of contribution within the community.
SOCIAL MEDIA
From a sociological perspective, social media can be described as \collective goods produced through computer-mediated collective action." For instance, in the case of Wikipedia, the collective goods are articles, and the collective action is the co-editing process of article writing. In the case of Dig, the collective goods are news stories, and the collective action is the effort of finding, voting for, and commenting on stories that pushes the most important stories (as determined by Dig users) to the most prominent position on the website (i.e. the front page). In the case of Face book, the collective goods are social capital, measured in the number and kinds of people active in the social network, and the collective action is the process of developing individual profiles and of the links between them. The term social media includes such a diverse a collection of tools and services that systems that should be further distinguished are often confused. Social media can vary along several dimensions. A key dimension is the size of the social groups that are producing and consuming the social media. How the different sized groups of producers and consumers generate different kinds of social media, from emails generated by dyadic communication to forum posts produced by individuals but consumed (read) by crowds, to collective search engine optimization both produced and consumed by large groups of people (searchers).
ROLES AND IDENTITY IN SOCIAL MEDIA
The different scales of producer and consumer groups for social media not only specify different types of media produced, they also induce differences in the composition and structure of communities that spring up around these artifacts. A small discussion board or email list is usually composed of individuals with strong mutual ties (friends, relatives, coworkers). Almost all participants on the board are likely to know everybody else, and this dense web of interconnections creates a strong social pressure for all members to act in a productive way. Spammers and trolls (individuals who post provocative messages on purpose) are rare in small communities, precisely because the people they would be spamming and trolling are their friends, and the bonds of mutual trust involved act as strong barriers to disruptive behavior. In social media like Wikipedia and Dig, however, the sheer number of producers and consumers makes it hard to form tightly knit groups. Anonymity is the norm in these large spaces, and the social pressure on community members to make useful contributions is relatively weak. In order to survive, therefore, social media that attract a lot of users need distinct mechanisms to promote constructive behavior and prevent spam and other forms of community vandalism.