CSCW Papers with Georgia Tech Authors

Aging with Technology
Individually Vulnerable, Collectively Safe: The Security and Privacy Practices of Households with Older Adults
Savanthi Murthy, Karthik S Bhat, Sauvik Das, Neha Kumar

Older adults are especially vulnerable to online cybersecurity and privacy (SP) threats, such as phishing, ransomware, and targeted misinformation campaigns. Prior work has suggested that this vulnerability may be addressed with the design of social SP interfaces, such that groups of individuals might work together on behalf of one another to manage SP threats collectively. To this end, we present findings from a qualitative inquiry conducted with older adults and members of technology-rich middle-income households in urban India, where technology users have been shown to engage in relatively more social SP practices. Our research examines the collaborative behaviors enacted by different members of the household for protection from SP threats. In particular, we show how self-appointed family technology managers straddle the line between stewardship and paternalism in their efforts to protect older adults’ from perceived digital threats. We also offer design implications for supporting collaborative cybersecurity within households based on the insights derived from our analysis.

Algorithmic Auditing and Responsible AI
A Human-Centered Systematic Literature Review of the Computational Approaches for Online Sexual Risk Detection
Afsaneh Razi, Seunghyun Kim, Ashwaq Alsoubai, Gianluca Stringhini, Thamar Solorio, Munmun De Choudhury, Pamela J. Wisniewski

“In the era of big data and artificial intelligence, online risk detection has become a popular research topic. From detecting online harassment to the sexual predation of youth, the state-of-the-art in computational risk detection has the potential to protect particularly vulnerable populations from online victimization. Yet, this is a high-risk, high-reward endeavor that requires a systematic and human-centered approach to synthesize disparate bodies of research across different application domains, so that we can identify best practices, potential gaps, and set a strategic research agenda for leveraging these approaches in a way that betters society. Therefore, we conducted a comprehensive literature review to analyze 73 peer-reviewed articles on computational approaches utilizing text or meta-data/multimedia for online sexual risk detection. We identified sexual grooming (75%), sex trafficking (12%), and sexual harassment and/or abuse (12%) as the three types of sexual risk detection present in the extant literature. Furthermore, we found that the majority (93%) of this work has focused on identifying sexual predators after-the-fact, rather than taking more nuanced approaches to identify potential victims and problematic patterns that could be used to prevent victimization before it occurs. Many studies rely on public datasets (82%) and third-party annotators (33%) to establish ground truth and train their algorithms.
Finally, the majority of this work (78%) mostly focused on algorithmic performance evaluation of their model and rarely (4%) evaluate these systems with real users. Thus, we urge computational risk detection researchers to integrate more human-centered approaches to both developing and evaluating sexual risk detection algorithms to ensure the broader societal impacts of this important work.”

Antisocial Computing
A Human-Centered Systematic Literature Review of Cyberbullying Detection Algorithms
Seunghyun Kim, Afsaneh Razi, Gianluca Stringhini, Pamela J. Wisniewski, Munmun De Choudhury

Cyberbullying is a growing problem across social media platforms, inflicting short and long-lasting effects on victims. As such, research has looked into building automated systems, powered by machine learning, to detect cyberbullying incidents, or the involved actors like victims and perpetrators. In the past, systematic reviews have examined the approaches within this growing body of work, but with a focus on the computational aspects of the technical innovation, feature engineering, or performance optimization, without centering around humans’ roles, beliefs, desires, or expectations. In this paper, we present a human-centered systematic literature review of the past 10 years of research on automated cyberbullying detection. We analyzed 56 papers based on a three-prong human-centeredness algorithm design framework – spanning theoretical, participatory, and speculative design. We found that the past literature fell short of incorporating human-centeredness across multiple aspects, ranging from defining cyberbullying, establishing the ground truth in data annotation, evaluating the performance of the detection models, to speculating the usage and users of the models, including potential harms and negative consequences. Given the sensitivities of the cyberbullying experience and the deep ramifications cyberbullying incidents bear on the involved actors, we discuss takeaways on how incorporating human-centeredness in future research can aid with developing detection systems that are more practical, useful, and tuned to the diverse needs and contexts of the stakeholders.

Antisocial Computing
Evaluating the Effectiveness of Deplatforming as a Moderation Strategy on Twitter
Shagun Jhaver, Christian Boylston, Diyi Yang, Amy Bruckman

Deplatforming refers to the permanent ban of controversial public figures with large followings on social media sites. In recent years, platforms like Facebook, Twitter and YouTube have deplatformed many influencers to curb the spread of offensive speech. We present a case study of three high-profile influencers who were deplatformed on Twitter—Alex Jones, Milo Yiannopoulos, and Owen Benjamin. Working with over 49M tweets, we found that deplatforming significantly reduced the number of conversations about all three individuals on Twitter. Further, analyzing the Twitter-wide activity of these influencers’ supporters, we show that the overall activity and toxicity levels of supporters declined after deplatforming. We contribute a methodological framework to systematically examine the effectiveness of moderation interventions and discuss broader implications of using deplatforming as a moderation strategy.

Antisocial Computing
RECAST: Enabling User Recourse and Interpretability of Toxicity Detection Models with Interactive Visualization
Austin P. Wright, Omar Shaikh, Haekyu Park, Will Epperson, Muhammed Ahmed, Stephane Pinel, Duen Horng Chau, Diyi Yang

With the widespread use of toxic language online, platforms are increasingly using automated systems that leverage advances in natural language processing to automatically flag and remove toxic comments. However, most automated systems—while detecting and moderating toxic language—do not provide feedback to their users, let alone provide an avenue of recourse for users to make actionable changes. We present our work, RECAST, an interactive, open-sourced web tool for visualizing these models’ toxic predictions, while providing alternative suggestions for flagged toxic language and a new path of recourse for users. RECAST highlights text responsible for classifying toxicity, and allows users to interactively substitute potentially toxic phrases with neutral alternatives. We examined the effect of RECAST via two large-scale user evaluations, and find that RECAST was highly effective at helping users reduce toxicity as detected through the model, and users gain a stronger understanding of the underlying toxicity criterion used by black-box models, enabling transparency and recourse. In addition we found that when users focus on optimizing language for these models instead of their own judgement (which is the implied incentive and goal of deploying such models at all) these models cease to be effective classifiers of toxicity compared to human annotations. This opens a discussion for how toxicity detection models work and should work, and their effect on future discourse.

Care and Caregiving
Shared Understanding in Care Coordination for Children’s Behavioral Health
Olivia K. Richards, Adrian Choi, Gabriela Marcu

Care coordination involves crossing boundaries to connect services in support of the health and well-being of an individual. In this paper, we describe how care coordination depends on the ability to develop shared understanding of care goals and progress. A distributed group of professionals and non-professional caregivers need to share information to provide consistent and holistic support across settings. We conducted fieldwork comprising of 20 interviews and 51 hours of observation across three different programs focused on children’s behavioral health. From this empirical investigation of practices used by distributed care teams, we generated a conceptual framework of shared understanding in care coordination. We identified barriers to shared understanding, as well as nine practices that contribute to its development via two key mechanisms: (1) building relationships across boundaries, and (2) sharing actionable information. We conclude with design implications for enhancing the collaborative practices of members of a care team to cross boundaries despite the barriers that are common in behavioral health and other contexts requiring complex care coordination.

Educational Technology and Environments
The Pandemic Shift to Remote Learning under Resource Constraints
Prerna Ravi, Azra Ismail, Neha Kumar

The COVID-19 pandemic has forced the transition of workflows across sectors to digital platforms. In educational contexts, teachers and schools previously reluctant to integrate computing technology in the classroom now find themselves with little choice but to embrace it. This move to the digital brings additional challenges in underserved contexts with limited, intermittent, and shared access to mobile or computing devices and the internet. In this rapidly evolving digital landscape, we investigate how educational institutions (schools and non-profit organizations) working with underserved populations in India are managing the transition to online or remote learning. We conducted twenty remote interviews with students, teachers, and administrators from underserved contexts across India. We found that online learning efforts in this setting relied on a resilient human infrastructure comprised of students, teachers, parents, administrators, and non-profit organizations, to help navigate and overcome the limitations of available technical infrastructure. Our research aims to articulate lessons for educational technology design in the post-COVID period, outlining areas for the improvement in the design of online learning platforms in resource-constrained settings, and identifying elements of online learning that could be retained to strengthen the education system overall.

Future of Work
Exploring the Tensions between the Owners and the Drivers of Uber Cars in Urban Bangladesh
S M Taiabul Haque, Rayhan Rashed, Mehrab Bin Morshed, Md Main Uddin Rony, Naeemul Hassan, Syed Ishtiaque Ahmed

Most scholarly discussions around ridesharing applications center on the experiences of the drivers and the riders (passengers), and thus the role of the owners of the cars, if they are different from the drivers, remain understudied. However, in many countries in the Global South, the car owners are often different from the car drivers, and the tensions between them often shape the experience with these ridesharing apps in those countries. In this paper, we address this issue based on our interview-based study in Dhaka, Bangladesh, which incorporates semi-structured interviews of 31 Uber drivers and 10 car owners. From our interviews, we identify the contract models that facilitate the partnership between prospective Uber drivers without a car and car owners seeking to rent their cars for Uber, describe the tensions between these two parties, provide a nuanced cultural portrayal of their negotiation mechanisms, and highlight the reasons for which the driver or the owner leaves Uber. Our analysis reveals how the local adoption of sharing economy amplifies existing inequalities and disrupts the prevailing social dynamics. We also connect our findings to the broader interests of CSCW around work, privacy, power and discuss their implications for design and policy formulations.

Infrastructuring Telehealth in (In)Formal Patient-Doctor Contexts
Karthik S Bhat, Mohit Jain, Neha Kumar

Telehealth technologies have long remained on the peripheries of healthcare systems that prioritize in-person healthcare provision. The spread of the COVID-19 pandemic has foregrounded the need to formalize telehealth infrastructures, particularly teleconsultations, to ensure continued care provision through remote mechanisms. In the Indian healthcare context, prior to the pandemic, teleconsultations have been used to substitute for in-person consultations when possible, and to facilitate remote follow-up care without exacerbating pressures on limited personal resources. We conducted a survey and interview study to investigate doctors’ and patients’ perceptions, experiences, and expectations around teleconsultations, and how these contribute towards supplementing healthcare infrastructures in India, focusing on the changes brought about by the COVID-19 pandemic. In this paper, we describe the efforts of our participants towards infrastructuring telehealth, examining how technologies were adapted to support teleconsultation, how expectations shifted, and how the dynamics of caregiving evolved through this transition. We present implications for the future design and uptake of telehealth, arguing that COVID-19’s impact on teleconsultations lays the foundation for new telehealth infrastructures for more inclusive and equitable care.

Interpreting and Explaining AI
Co-Designing AI Literacy Exhibits for Informal Learning Spaces
Duri Long, Takeria S. Blunt, Brian Magerko

AI is becoming increasingly integrated in common technologies, which suggests that learning experiences for audiences seeking a “casual” understanding of AI—i.e. understanding how a search engine works, not necessarily understanding how to program one—is an increasingly important design space. Informal learning spaces like museums are particularly well-suited for such public science communication efforts, but there is little research investigating how to design AI learning experiences for these spaces. This paper explores how to design museum experiences that communicate key concepts about AI, using collaboration, creativity, and embodiment as inspirations for design. We present the design of five low-fidelity AI literacy exhibit prototypes and results from a thematic analysis of participant interactions during a co-design workshop in which family groups interacted with the prototypes and designed exhibits of their own. Our findings suggest new topics and design considerations for AI-related exhibits and directions for future research.

Methods and Design Approaches
Reflections on Assets-Based Design: A Journey Towards A Collective of Assets-Based Thinkers
Marisol Wong-Villacres, Aakash Gautam, Deborah Tatar, Betsy DiSalvo

The field of Computer-Supported Cooperative Work (CSCW) has long recognized a socio-technical gap complicating the design of technologies that can sustainably meet social needs. In response, a growing body of research advocates for assets-based design, an approach that seeks to build upon what the individuals and community already have. The emphasis on positioning assets rather than needs at the center of the process can complicate designers’ decisions on what activities to foster, how to conduct them, and what outcomes to expect. In this paper, we reflect on two different assets-based design endeavors with vulnerable populations. Our reflections present assets-based design as an ongoing process that prioritizes the formation and evolution of a collective of assets-based thinkers who continually learn about their assets and how to use them to attain desirable change. From that reflection, we contribute three methodological commitments for assets-based design to the growing CSCW scholarship on supporting vulnerable communities to attain emancipatory transformations: (1) embedding trust-building elements throughout the journey;(2) facilitating the formation of an interdependent collective; and (3) making moves towards incremental transformations. Further, we contribute a discussion on the change of perspective that entails for researchers and designers interested in undertaking assets-based design. In particular, we underscore the need to recognize the value of work before the work, to see technology as an intermediary rather than an inevitable end, and embrace impact in the shape of slow incremental transformation.

Misinformation, Conspiracies, and Manipulations
Sensemaking and the Chemtrail Conspiracy on the Internet: Insights from Believers and Ex-believers
Sijia Xiao, Coye Cheshire, Amy Bruckman

How do people come to believe conspiracy theories, and what role does the internet play in this process as a socio-technical system? We explore these questions by examining online participants in the “chemtrails” conspiracy, the idea that visible condensation trails behind airliners are deliberately sprayed for nefarious purposes. We apply Weick’s theory of sensemaking to examine the role of people’s frames (beliefs and worldviews), as well as the socio-technical contexts (social interactions and technological affordances) for processing informational cues about the conspiracy. Through an analysis of in-depth interviews with thirteen believers and seven ex-believers, we find that many people become curious about chemtrails after consuming rich online media, and they later find welcoming online communities to support shared beliefs and worldviews. We discuss how the socio-technical context of the internet may inadvertently trap people in a perpetual state of ambiguity that becomes reinforced through a collective sensemaking process. In addition, we show how the conspiracy offers a way for believers to express their dissatisfaction with authority, enjoy a sense of community, and find some entertainment along the way. Finally, we discuss how people’s frames and the various socio-technical contexts of the internet are important in the sensemaking of debunking evidence, and how such factors may function in the rejection of conspiratorial beliefs.

Open Collaboration
Open Data Settings: A Conceptual Framework Explored Through the Map Room Project
Yanni Alexander. Loukissas, Jude Mwenda. Ntabathia

In recent years, researchers have sought more effective ways of making data “open,” for purposes of accountability, engagement, and reuse. Often, such efforts focus on making existing data sets available to broad audiences. The expression data set itself suggests something discrete, complete, and easily transferable. But data are none of those things. In this paper, we argue that open data projects could benefit from a more contextual understanding of what open means. Instead of focusing on open data sets, researchers can seek to create and understand open data settings: contexts in which things of public significance can be presented as evidence. We share our experiences creating and analyzing open data settings for the Map Room Project, a research through design initiative to establish local spaces for collaborative data exploration and mapping. Our contribution is to offer a conceptual framework through which researchers, as well as designers, might think about the openness of data settings. This framework comes out of a situational analysis of comparative empirical case studies. In data settings, we find that open can mean accessible, inclusive, or indeterminate. Practices of contextualization, such as configuring, convening, and claim-making, shape these dimensions of openness by defining all of the following: where data can work, who is empowered to use them, and what can count as data.

Personal and Mental Health
A Social Media Study on Mental Health Status Transitions Surrounding Psychiatric Hospitalizations
Sindhu Kiranmai Ernala, Kathan H. Kashiparekh, Amir Bolous, Asra Ali, John M. Kane, Michael L. Birnbaum, Munmun De Choudhury

For people diagnosed with a mental illness, psychiatric hospitalization is one step in a long journey, consisting of clinical recovery such as removal of symptoms, and social reintegration involving resuming social roles and responsibilities, overcoming stigma and self-maintenance of the condition. Both clinical recovery and social reintegration need to go hand-in-hand for the overall well-being of individuals. However, research exploring social media for mental health has considered narrower, disjoint conceptualizations of people with mental illness – either as a patient or as a support-seeker. In this paper, we combine medical records with social media data of 254 consented individuals who have experienced a psychiatric hospitalization to address this gap. Adopting a theory-driven, Gaussian Mixture modeling approach, we provide a taxonomy of six heterogeneous behavioral patterns characterizing peoples’ mental health status transitions around hospitalizations. Then we present an empirically derived framework, based on feedback from clinical researchers, to understand peoples’ trajectories around clinical recovery and social reintegration. Finally, to demonstrate the utility of this taxonomy and the empirical framework, we assess social media signals that are indicative of individuals’ reintegration trajectories post-hospitalization. We discuss the implications of combining peoples’ clinical and social experiences in mental health care and the opportunities this intersection presents to post-discharge support and technology-based interventions for mental health.

Personal and Mental Health
Misfires, Missed Data, Misaligned Treatment: Disconnects in Collaborative Treatment of Eating Disorders
Lauren C. Taylor, Kelsie Belan, Munmun De Choudhury, Eric P. S. Baumer

Technology bears important relationships to our health and wellness and has been utilized over the past two decades as an aid to support both self-management goals as well as collaboration among treatment teams. However, when chronic illnesses such as eating disorders (ED) are managed outside of institutionalized care settings, designing effective technology to support collaboration in treatment necessitates that we understand the relationships between patients, clinicians, and support networks. We conducted in-depth, semi-structured, interviews with 9 ED patients and 10 clinicians to understand the ED journey through the lens of collaborative efforts, technology use, and potential detriments. Based on our analysis of these 19 interviews, we present novel findings on various underlying disconnects within the collaborative ED treatment process – disconnects among clinicians, between treatment foci, among preferences in tracking, within support networks, and in patients’ own identities. Our findings highlight how these various disconnects are concomitant with and gaps can stem from a lack of collaboration between different stakeholders in the ED journey. We also identify methods of facilitating collaboration in these disconnects through technological mediators.

Social Media
Exploring the Utility Versus Intrusiveness of Dynamic Audience Selection on Facebook
Sindhu Kiranmai Ernala, Stephanie S. Yang, Yuxi Wu, Rachel Chen, Kristen Wells, Sauvik Das

In contrast to existing, static audience controls that map poorly onto users’ ideal audiences on social networking sites, dynamic audience selection (DAS) controls can make intelligent inferences to help users’ select their ideal audience given context and content. But does this potential utility outweigh its potential intrusiveness? We surveyed 250 participants to identify model users’ ideal versus their chosen audiences with static controls and found a significant misalignment, suggesting that DAS might provide utility. We then designed a sensitizing prototype that allowed users to select audiences based on personal attributes, content, or context constraints. We evaluated DAS vis-a-vis Facebook’s existing audience selection controls through a counterbalanced summative evaluation. We found that DAS’s expressiveness, customizability, and scalability made participants feel more confident about the content they shared on Facebook. However, low transparency, distrust in algorithmic inferences, and the emergence of privacy-violating side channels made participants find the prototype unreliable or intrusive. We discuss factors that affected this trade-off between DAS’s utility and intrusiveness and synthesize design implications for future audience selection tools.

Social Support and Intervention
“The Smartest Decision for My Future“: Social Media Reveals Challenges and Stress During Post-College Life Transition
Crystal Gong, Koustuv Saha, Stevie Chancellor

The post-college transition is a critical period where individuals experience unique challenges and stress before, during, and after graduation. Individuals often use social media to discuss and share information, advice, and support related to post-college challenges in online communities. These communities are important as they fill gaps in institutional support between college and post-college plans. We empirically study the challenges and stress expressed on social media around this transition as students graduate college and move into emerging adulthood. We assembled a dataset of about 299,000 Reddit posts between 2008 and 2020 about the post-college transition from 10 subreddits. We extracted top concerns, challenges, and conversation points using unsupervised Latent Dirichlet Allocation (LDA). Then, we combined the results of LDA with binary transfer learning to identify stress expressions in the dataset (classifier performance at F1=0.94). Finally, we explore temporal patterns in stress expressions and the variance of per-topic stress levels throughout the year. Our work highlights a more deliberate and focused understanding of the post-college transition, as well as useful research and design impacts to study transient cohorts in need of support.

Workplace Challenges and Digital Wellbeing
A Social Media Study on Demographic Differences in Perceived Job Satisfaction
Koustuv Saha, Asra Yousuf, Louis Hickman, Pranshu Gupta, Louis Tay, Munmun De Choudhury

Effective ways to measure employee job satisfaction are fraught with problems of scale, misrepresentation, and timeliness. Current methodologies are limited in capturing subjective differences in expectations, needs, and values at work, and they do not lay emphasis on demographic differences, which may vary people’s perceptions of job satisfaction. This study proposes an approach to assess job satisfaction by leveraging large-scale social media data. Starting with an initial Twitter dataset of 1.5M posts, we examine two facets of job satisfaction, pay and supervision. By adopting a theory-driven approach, we first build machine learning classifiers to assess perceived job satisfaction with an average AUC of 0.84. We then study demographic differences in perceived job satisfaction by geography, sex, and race in the U.S. For geography, we find that job satisfaction on Twitter exhibits insightful relationships with macroeconomic indicators such as financial wellbeing and unemployment rates. For sex and race, we find that females express greater pay satisfaction but lower supervision satisfaction than males, whereas Whites express the least pay and supervision satisfaction. Unpacking linguistic differences, we find contrasts in different groups’ underlying priorities and concerns, e.g., under-represented groups saliently express about basic livelihood, whereas the majority groups saliently express about self-actualization. We discuss the role of frame of reference and the “job satisfaction paradox”, conceptualized by organizational psychologists, in explaining our observed differences. We conclude with theoretical and sociotechnical implications of our work for understanding and improving worker wellbeing.