Online peer production communities offer scalable alternatives to traditional knowledge production but often struggle with long-term sustainability. This study explores how internal contradictions shape community development, using Activity Theory as a lens. We conducted a 20-month investigation of the Mathematics Stack Exchange community through participant observation, analysis of 1,942 governance discussion threads, and interviews with 28 contributors. We identify four key contradictions: between tools and objectives, rules and objectives, division of labor and objectives, and within the objectives themselves. Thematic analysis reveals three resolution mechanisms: user-created tools to overcome technical limitations, flexible governance through tagging systems, and evolved moderation roles that balance quality control and newcomer support. These findings show how contradictions drive adaptation and innovation. We offer design recommendations, such as graduated moderation and separate user spaces, to support community sustainability. Our work demonstrates the value of Activity Theory for analyzing and improving online peer production systems.

Facial recognition technology (FRT) has become widespread in society, and its adoption in organizational settings is viewed as a natural continuation of established surveillance practices. However, concerns and resistance to FRT often arise due to controversial applications and improper use. This study contributes to the understanding of FRT in organizational contexts by conducting semi-structured interviews in a university that illustrates the larger themes of organizational justice and the ethical complexities surrounding the deployment of this technology. Drawing on organizational justice theory, we analyze users’ attitudes towards FRT deployment, their perceptions of organizational justice, and if and how the deployment adhered to or violated organizational justice principles. Our study revealed that this implementation case violated all aspects of organizational justice. Users expressed concerns regarding face capture, potential discrimination, authoritarian practices, and harm to community norms. We also provide recommendations to address organizational justice issues associated with implementing new, controversial technologies, considering technology acceptability perspectives.

Technology-based tools for mental health support, powered by certain algorithms, often become the first point of contact for people looking for mental health resources, especially in underserved communities. The different ways people understand and talk about their mental distress significantly affect the resources these tools recommend or exclude. Using the Google search symptom dataset, we looked into how people from socioeconomically different counties in Alabama search for mental health symptoms. We found that individuals in better-off areas mostly conducted searches using specific clinical terms, while those in less advantaged areas mostly used general mental health symptom terms. A similar trend was seen for physical symptoms, with people in disadvantaged areas often using general and pain-related terms, possibly indicating a lack of access to specialized care or wrong clinical treatments and raising concerns about opioid misuse. Also, counties with mainly African American populations had fewer mental health-related searches, suggesting there might be cultural or linguistic barriers. We discuss what these findings mean for designing algorithms focusing on health fairness. This study helps suggest how search engine algorithm designers can be aware of the societal factors affecting how people express distress and adjust search engine algorithms and interfaces to help reduce gaps in healthcare access.

Online peer production communities, such as Wikipedia, Yahoo Answers, and Stack Overflow, not only generate knowledge resources with low cost access, but also have tremendous potential to complement both online and offline movement of societies. As peer production communities flourished, merely describing successful cases has become less useful. Instead, scholars must identify the dynamics, structures, and conditions that contribute to or impede that success, including how communities change, what makes some communities succeed while others fail, and how and when to intervene successfully. In this dissertation project I combine Activity Theory, computational techniques, and qualitative and quantitative methods to understand the differences of dynamics, structures, and conditions of two social Q&A communities that produced contrasting results within the same context, based on the community life cycle.

With the increasing number of multilingual resources on the Internet, cross-language information retrieval has become an important research topic. In cultures where people speak both Chinese and English, using mixed language in oral speaking and web searching is a common phenomenon. While queries are the key element of information retrieval process, mixed-language queries have not yet been adequately studied. This study use query log analysis to examine the query reformulation patterns regarding Chinese-English mixed language queries, and how search intents may affect the query reformulation types users employ. The results can inform IR system designers to enhance cross-language controlled vocabularies and develop discovery platforms for multilingual content, and improve search engines to provide users with more relevant, personalized search results. The findings could also be expanded to other language combinations that would improve search engine designs.