Rich Gazan

Social Q&A

This article presents a review and analysis of the research literature in social Q&A (SQA), a term describing systems where people ask, answer, and rate content while interacting around it. The growth of SQA is contextualized within the broader trend of user-generated content from Usenet to Web 2.0, and alternative definitions of SQA are reviewed. SQA sites have been conceptualized in the literature as simultaneous examples of tools, collections, communities, and complex sociotechnical systems. Major threads of SQA research include user-generated and algorithmic question categorization, answer classification and quality assessment, studies of user satisfaction, reward structures, and motivation for participation, and how trust and expertise are both operationalized by and emerge from SQA sites. Directions for future research are discussed, including more refined conceptions of SQA site participants and their roles, unpacking the processes by which social capital is achieved, managed, and wielded in SQA sites, refining question categorization, conducting research within and across a wider range of SQA sites, the application of economic and game-theoretic models, and the problematization of SQA itself.

Specialists and synthesists in a question answering community

The most sustainable online communities are those that allow and encourage their users to have a voice in how the community evolves. The proliferation of online communities with collaborative filtering mechanisms, where user feedback is aggregated to shape future interactions, makes it necessary to understand why participants in online communities value the content they do. Building on the concepts of users as specialists and synthesists developed in previous research, this study examines Answerbag, an online question answering community where users rate one another’s answers to provide collaborative filtering. In this environment, specialists are operationalized as those who claim expertise in a given topic and answer questions without referencing other sources, and synthesists as those who include one or more references to external sources in their answers. The results of the study suggest that within the Answerbag community as a whole, the answers of synthesists tended to be rated more highly than those of specialists, though answers provided by specialists were rated more highly within certain categories. The consequences of differences in the perceived value of information provided by specialists and synthesists are examined, and avenues for future research are discussed.

Seekers, sloths and social reference: homework questions submitted to a question-answering community

An increasing number of students are seeking homework help outside library structures and systems, such as on social reference sites, where questions are answered by online community members who rate one anothers answers and provide collaborative filtering in place of traditional expertise. This paper reports the preliminary results of a participant observation and content analysis of homework questions submitted to Answerbag, a social reference site with over one million unique visitors per month. The results suggest that members of the online community are able to distinguish between questions submitted by Seekers—those who interact with the community and engage in conversation about their questions—and Sloths, those who post their homework questions apparently verbatim and interact no further. How the community reacts to these distinct types of questioners reflects values similar to those of professional reference providers, and the community structure also allows members to educate questioners about community standards and the ethics of information seeking.

Microcollaborations in a social Q&A community

Most social Q&A sites are designed to support solo searchers who access the aggregated opinions of other users, and ask and answer questions of their own. The purpose of this paper is to show how users in one social Q&A community defy system constraints to engage in brief, informal episodes of collaborative information seeking called microcollaborations. A brief literature review is presented, suggesting a view of information seeking as a combination of problem-centered information seeking, technological affordances and constraints, and social and affective factors. The results of content and transaction log analyses of user interactions suggest that topics of collaboration share a common threshold of complexity and invite responses containing both fact and opinion. Analysis also revealed that key elements in predicting a collaborative instance involve social capital and affective factors unrelated to the topic of the collaboration. Suggestions for supporting future lightweight microcollaborations, and implications for future research, are discussed.

When Online Communities Become Self-Aware

Evidence from a long-term participant observation suggests that a critical point in the evolution of an online community occurs when participants begin to focus less on topical content and more on one another. When content restrictions were removed from a question answering community and social technologies were introduced, the proportion of factual content on the site steadily diminished in favor of more social content: questions specifically about site users and appropriate behavior, suggesting an awareness of themselves as a community. Positive effects of self-aware behavior included increased site participation, social support and open normative debates. Negative effects included increased conflict, rogue behaviors and factionalism.

Assessing researcher interdisciplinarity: a case study of the University of Hawaii NASA Astrobiology Institute

In this study, we combine bibliometric techniques with a machine learning algorithm, the sequential information bottleneck, to assess the interdisciplinarity of research produced by the University of Hawaii NASA Astrobiology Institute (UHNAI). In particular, we cluster abstract data to evaluate Thomson Reuters Web of Knowledge subject categories as descriptive labels for astrobiology documents, assess individual researcher interdisciplinarity, and determine where collaboration opportunities might occur. We find that the majority of the UHNAI team is engaged in interdisciplinary research, and suggest that our method could be applied to additional NASA Astrobiology Institute teams in particular, or other interdisciplinary research teams more broadly, to identify and facilitate collaboration opportunities.

Redesign as an act of violence: disrupted interaction patterns and the fragmenting of a social Q&A community

The worst-case scenario for the redesign of an established online community is a subsequent mass migration of its core members to other sites. Using data from transaction logs, content analysis and participant observation, this paper presents a descriptive analysis of the fragmentation of a social question answering (Q&A) community in the immediate aftermath of a fundamental redesign, where site-based communication mechanisms no longer functioned. The ways in which the community and its diaspora reacted, reconnected and resettled on other sites provides empirical data to support recent research on the life cycle of online communities. The results suggest that many of the same processes that help social Q&A sites generate content and motivate participation can work to dismantle an established community if communications between members are even temporarily disrupted. Modeling a redesign as an attack on a community can help future designers anticipate alternative paths of communication and information flows.

Unsupervised classification and visualization of unstructured text for the support of interdisciplinary collaboration

We present a computer supported tool for cooperative work in interdisciplinary fields, which we tested within the area of astrobiology. Our document classification and visualization system is fully automated and data driven, based on unsupervised learning algorithms and network visualization tools. A new feature selection algorithm was created to aid this process that indicates which words should be used for mutual information-based clustering. Our system can extract information about collaborations from unstructured databases with no meta-data and reveals structure that can aid the planning of collaborative research. We analyzed publications produced by researchers from NASAs Astrobiology Institute. We presented this analysis as a cultural probe and recorded reactions from researchers that indicated that our method can help scientists from different disciplines to work together. We have made an interactive version of our visualization and analysis available as a website for long-term use.

Use Scenarios in the Development of the Alexandria Digital Earth Prototype (ADEPT)

A user-centered, iterative design philosophy requires a common language between users, designers and builders to translate user needs into buildable specifications. This paper details the rationale, evolution and implementation of use scenarios—structured narrative descriptions of envisioned system use—in the development of the Alexandria Digital Earth Prototype. This paper discusses the strengths of the scenario approach, obstacles to their use, and lessons learned in the overall development process.

First-Mover Advantage in a Social QaA Community

Aggregate answer ratings serve as a metric of collective intelligence in social Q&A communities. The patterns by which participants in a social Q&A community rate and recommend answers are analyzed through the lens of first-mover advantage, to address the question of whether the first answer posted has a ratings advantage over those subsequently submitted. As part of a long-term participant observation, ratings for answers submitted to the Answer bag social Q&A site were compared by order of submission and normalized for page views and answer quality. The results suggest that the first-submitted answer consistently accumulates roughly 17% more rating points than the second answer submitted, and that the rating points of each subsequent answer tend to decline. Social factors influencing rating activity and implications for interpreting future social Q&A data are discussed.