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Topic modeling includes a variety of machine learning techniques for identifying latent themes in a corpus of documents. Generating an exact solution (i.e., finding global optimum) is often computationally intractable. Various optimization techniques (e.g., Variational Bayes or Gibbs Sampling) are employed to generate topic solutions approximately by finding local optima. Such an approximation often begins with a random initialization, which leads to different results with different initializations. The term “stability” refers to a topic model’s ability to produce solutions that are partially or completely identical across multiple runs with different random initializations. Although a variety of work has been done analyzing, measuring, or improving stability, no single paper has provided a thorough review of different stability metrics nor of various techniques that improved the stability of a topic model. This paper fills that gap and provides a systematic review of different approaches to measure stability and of various techniques that are intended to improve stability. It also describes differences and similarities between stability measures and other metrics (e.g., generality, coherence). Finally, the paper discusses the importance of analyzing both stability and quality metrics to assess and to compare topic models. 

The emergent, dynamic nature of privacy concerns in a shifting sociotechnical landscape creates a constant need for privacy-related resources and education. One response to this need is community-based privacy groups. We studied privacy groups that host meetings in diverse urban communities and interviewed the meeting organizers to see how they grapple with potentially varied and changeable privacy concerns. Our analysis identified three features of how privacy groups are organized to serve diverse constituencies: situating (finding the right venue for meetings), structuring (finding the right format/content for the meeting), and providing support (offering varied dimensions of assistance). We use these findings to inform a discussion of "privacy pluralism" as a perennial challenge for the HCI privacy research community, and we use the practices of privacy groups as an anchor for reflection on research practices. 

Eating disorders (EDs) constitute a mental illness with the highest mortality. Today, mobile health apps provide promising means to ED patients for managing their condition. Apps enable users to monitor their eating habits, thoughts, and feelings, and offer analytic insights for behavior change. However, not only have scholars critiqued the clinical validity of these apps, their underlying design principles are not well understood. Through a review of 34 ED apps, we uncovered 11 different data types ED apps collect, and 9 strategies they employ to support collection and reflection. Drawing upon personal health informatics and visualization frameworks, we found that most apps did not adhere to best practices on what and how data should be collected from and reflected to users, or how data-driven insights should be communicated. Our review offers suggestions for improving the design of ED apps such that they can be useful and meaningful in ED recovery.