Eric P. S. Baumer

Foundational CSCW research positions the design of novel technologies to support collaborative work as the raison d’etre for the field. While sensible during CSCW’s genesis in the late 1980s, an orientation that emphasizes novel technology needs updating for a world already suffused with collaboration systems. As an example of the approach for which this paper advocates, we describe a long-term engagement with a legal non-profit called Themis. Although initially focused on the design of a novel technological system, over time our efforts shifted to helping redesign some of this organization’s core information management systems and practices. This design process neither produced a novel computing system, nor implications for building such a system. However, it did result in a reconfiguration of existing technologies and work practices that ultimately proved beneficial for Themis. We suggest this kind of an approach, which we refer to as artful reconfiguration , as an alternative mode of engagement that can ensure CSCW’s continued usefulness in a technology-saturated world.

Introduction. The COVID-19 pandemic brought about new and far-ranging uses of technology that have engendered numerous privacy concerns among the public. This study examines discussions in r/privacy, a privacy-focused online community, to gain insight into how the pandemic influenced privacy concerns and behaviours among privacy-interested people. Method. Our research design included a mixed-method comparative analysis of 540 coded r/privacy posts from three time periods: immediately before the pandemic, during the emergence of COVID-19, and two years after the onset of the pandemic. Analysis. We performed an inductive, qualitative analysis to understand themes in the data, as well as a statistical analysis to identify key trends and differences in the frequency of themes in the three time periods. Results. We observed that while some changes were temporary (e.g., viewing the government as a key privacy threat during the pandemic onset), others were long-lasting and increased over time (e.g., seeking privacy-related support). Conclusions. Our work contributes to the growing literature on how people use social media to collectively make sense of major events by exploring online discourse about privacy issues and offers insights to designers and regulators of new technologies on specific privacy concerns associated with a period of massive sociotechnical upheaval.

This article considers how subjectivities are enlivened in algorithmic systems. We first review related literature to clarify how we see “subjectivities” as emerging through a tangled web of processes and actors. We then offer two case studies exemplifying the emergence of algorithmic subjectivities: one involving computational topic modeling of blogs written by parents with children on the autism spectrum and one involving algorithmic moderation of social media content. Drawing on these case studies, we then articulate a series of qualities that characterizes algorithmic subjectivities. We also compare and contrast these qualities with a number of related concepts from prior literature to articulate how algorithmic subjectivities constitute a novel theoretical contribution, as well as how it offers a focal lens for future empirical investigation and for design. In short, this article points out how certain worlds are being made and/or being made possible via algorithmic systems, and it asks Human–Computer Interaction (HCI) to consider what other worlds might be possible.

This study addresses two currently open questions about how behaviors of online community members influence others’ responses to misinformation. First, in contrast to prior work, it directly measures norm perception to address whether (1) norm perception actually acts as a mediator, (2) others’ behaviors directly influence individuals’ responses to misinformation, (3) both direct and mediated effects occur. Second, it investigates norm perceptions about a behavior that is not readily observable in online communities, but is prone to misinformation, specifically, vaccination. To do so, it experimentally manipulates the prevalence of communicating about vaccination (an unobservable behavior) within an online community. The results demonstrate no evidence of a direct effect—the causal relationship between prevalence of communicating a behavior and intentions to respond to misinformation only occurs via norm perception as a mediator. The paper highlights implications of these findings for designing community-centered interventions to influence perceived norms, thereby mitigating misinformation spread and impacts.

How do privacy experts and lay persons differ? We investigate this question using data about the use of privacy-enhancing technologies and strategies from 128 surveys and 17 follow-up interviews with two populations: privacy experts (i.e., privacy researchers and professionals) and privacy laypersons. Findings reveal that both experts and laypersons use common privacy strategies, but experts employ a broader range of strategies, favor open source technologies, and possess a more technical understanding of internet privacy risks and technologies such as onion routing and tracking algorithms. We characterize these differences in terms of sociological habitus, where experts and laypersons differ not only in technical skill but also in broader conceptualizations and practices. From this characterization, we make recommendations for technology design practices, as well as legal and pedagogical implications, that can help decenter the experiences of privacy experts and accommodate privacy laypersons' preferences and habits.

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.