A content analysis of online sex worker advertisements suggests specific terms, sources and patterns of behavior that may help identify potential sex trafficked victims within these virtual environments. While some ads are posted by independent sex workers, others may have been posted by traffickers or pimps, advertising the women they have under their control. A total of 600 ads from the Backpage “escort” services site were harvested and analyzed for information elements including location, age, name, area code, ethnicity and controlled/restricted movement. The results suggest that 75% of the ads contained one or more of the primary sex trafficking indicators, and 15% of the ads contained three or more indicators. The results suggest that some physical indicators should be adapted to the virtual environment, and that the presence of multiple indicators can be used to prioritize certain ads for further investigation by law enforcement. It is hoped that these indicators can inform the design of future systems to flag high-risk advertisements, and help identify and disrupt this covert network activity.

The use of social network analysis is used to identify possible victims of sex trafficking and the observation of domestic trafficking flows across the U.S. A novel approach to the analysis is presented with the understanding further development is needed. Online escort advertisements were analyzed for virtual indicators of sex trafficking and to identify patterns of activity that may be associated with criminal networks. The results suggest that 67% of the sample contained indicators of movement. Social network analysis methods were applied to identify movement trends across the US. The use of SNA methods allowed prominent hubs and circuits of this activity to be observed, by providing a tool to uncover covert network structures and activity, yielding a method to capture movement trends of potential trafficked persons. The integration of geospatial data allowed maps to be created in order to visualize movement patterns.

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 NASA's 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.

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.

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.