Towards a Model of Understanding Social Search

Search engine researchers typically depict search as the solitary activity of an individual searcher. In contrast, results from our critical-incident survey of 150 users on Amazon's Mechanical Turk service suggest that social interactions play an important role throughout the search process. Our main contribution is that we have integrated models from previous work in sensemaking and information seeking behavior to present a canonical social model of user activities before, during, and after search, suggesting where in the search process even implicitly shared information may be valuable to individual searchers.Comment: Presented at 1st Intl Workshop on Collaborative Information Seeking, 2008 (arXiv:0908.0583

Towards Social Information Seeking and Interaction on the Web

User generated content is one of the key concepts of the social web (a. k. a “Web 2.0”) and enables users to search and interact with information that has been created (e.g. blogs) or annotated by other users (e.g. in tagging systems). Consequently, information seeking and interaction have been extended by a social dimension. The interaction can be social in so far that user generated content is searched and retrieved or, in a more direct manner that social interactions are carried out before, during or after search by communicating through Web 2.0 features like (micro-)blog posts, comments, and ratings. This paper focuses on social interactions during the search process by combining a model introduced by Shneiderman (2002) which attempts to describe human motivation for collaboratively using computers with an explorative model for social search by Evans and Chi (2008)

Engineering professional identity practices : Investigating use of web search in collaborative decision making

Collaborative learning and problem solving are important aspects of engineering professional practice that need to be addressed in preparing competent engineering graduates and forming their professional identities. Taking the learning as becoming a professional perspective, we illustrate the diversity of engineering practices in a collaborative decision-making episode, where students’ participation in the activity is mediated by their use of web search. We present how our development of the implied identity approach could help to understand how technology mediates collaborative sense making in relation to professional practices and identities. We illustrate this by providing examples of ways in which students use web information to justify their decision making

Exploitation de signaux sociaux pour estimer la pertinence a priori d'une ressource

National audienceDans cet article nous proposons une approche de recherche d'information (RI) qui prend en compte le contenu social associé à une ressource pour mesurer sa pertinence a priori vis-à-vis d'une requête. Nous démontrons comment ces caractéristiques, qui sont sous forme d'actions relevant d'activités sociales (signaux sociaux) tels que le nombre de "j'aime" et de "partage", peuvent être combinées pour quantifier des propriétés sociales telles que la popularité et la réputation. Nous proposons de modéliser ces propriétés comme des probabilités a priori que nous intégrons dans un modèle de langue. Nous avons évalué l'efficacité de notre approche sur la collection d'IMDb contenant 32706 documents et leurs caractéristiques sociales collectées sur plusieurs réseaux sociaux. Nos résultats expérimentaux sont très prometteurs et montrent l'intérêt de l'intégration des propriétés sociales dans un modèle de recherche pour améliorer la RI

Search delegation, synthesists and expertise on social media

Background. Social media often adds a layer of intermediation between sources and information consumer, with users outsourcing some of the information work to others. Social media “synthesists” have been identified as a group of volunteer information providers fulfilling this role.Approach. Through a review of evidence from philosophy, information science and knowledge management, this paper explores the implications of cognitive outsourcing, presents quality standards for synthesis and asks how well synthesists meet these. In the process, the role of intermediary is discussed, along with the non-specialist status of the synthesist.Results. Findings show that social media synthesists fulfil a useful role and that their importance in terms of knowledge translation is clear. While their synthesis quality may fall far short of LIS standards, there are a number of ways that some quality issues can be addressed, including the involvement of the information profession itself on the same social platforms.Contribution. Through a comparison of synthesis best practice with current informal information behaviour on social media, the paper draws attention to quality issues and new opportunities to address them. This represents an attempt to identify ways to bridge formal and emerging, informal information markets

Soylent: A Word Processor with a Crowd Inside

This paper introduces architectural and interaction patterns for integrating crowdsourced human contributions directly into user interfaces. We focus on writing and editing, com-plex endeavors that span many levels of conceptual and pragmatic activity. Authoring tools offer help with prag-matics, but for higher-level help, writers commonly turn to other people. We thus present Soylent, a word processing interface that enables writers to call on Mechanical Turk workers to shorten, proofread, and otherwise edit parts of their documents on demand. To improve worker quality, we introduce the Find-Fix-Verify crowd programming pat-tern, which splits tasks into a series of generation and re-view stages. Evaluation studies demonstrate the feasibility of crowdsourced editing and investigate questions of relia-bility, cost, wait time, and work time for edits.National Science Foundation (U.S.) (Grant No. IIS-0712793

Tie strength in question answer on social network sites

Asking friends, colleagues, or other trusted people to help answer a question or find information is a familiar and tried-and-true concept. Widespread use of online social networks has made social information seeking easier, and has provided researchers with opportunities to better observe this process. In this paper, we relate question answering to tie strength, a metric drawn from sociology describing how close a friendship is. We present a study evaluating the role of tie strength in question answers. We used previous research on tie strength in social media to generate tie strength information between participants and their answering friends, and asked them for feedback about the value of answers across several dimensions. While sociological studies have indicated that weak ties are able to provide better information, our findings are significant in that weak ties do not have this effect, and stronger ties (close friends) provide a subtle increase in information that contributes more to participants' overall knowledge, and is less likely to have been seen before