This paper examines how non-resident Bangladeshis mobilized during the 2024 quota-reform turned pro-democracy movement, leveraging social platforms and remittance flows to challenge state authority. Drawing on semi-structured interviews, we identify four phases of their collective action: technology-mediated shifts to active engagement, rapid transnational network building, strategic execution of remittance boycott, reframing economic dependence as political leverage, and adaptive responses to government surveillance and information blackouts. We extend postcolonial computing by introducing the idea of “diasporic superposition," which shows how diasporas can exercise political and economic influence from hybrid positionalities that both contest and complicate power asymmetries. We reframe diaspora engagement by highlighting how migrants participate in and reshape homeland politics, beyond narratives of integration in host countries. We advance the scholarship on financial technologies by foregrounding their relationship with moral economies of care, state surveillance, regulatory constraints, and uneven international economic power dynamics. Together, these contributions theorize how transnational activism and digital technologies intersect to mobilize political change in Global South contexts.

Governments are the primary providers of essential public services and are responsible for delivering them effectively. In high-stakes decision-making domains such as child welfare (CW), agencies must protect children without unnecessarily prolonging a family’s engagement with the system. With growing optimism around AI, governments are pushing for its integration but concerns regarding feasibility and harms remain. Through collaborations with a large Canadian CW agency, we examined how LocalLLM and BERTopic models can track CW case progress. We demonstrate how the tools can potentially assist workers in opportunistically addressing gaps in their work by signaling case progress/deviations. And yet, we also show how they fail to detect case trajectories that require discretionary judgments grounded in social work training, areas where practitioners would actually want support to pre-emptively address substantive case concerns. We also provide a roadmap of future participatory directions to co-design language tools for/with the public sector.

Sociotechnical systems, such as language technologies, frequently exhibit identity-based biases. These biases exacerbate the experiences of historically marginalized communities and remain understudied in low-resource contexts. While models and datasets specific to a language or with multilingual support are commonly recommended to address these biases, this paper empirically tests the effectiveness of such approaches for gender, religion, and nationality-based identities in Bengali, a widely spoken but low-resourced language. We conducted an algorithmic audit of sentiment analysis models built on mBERT and BanglaBERT, which were fine-tuned using all Bengali sentiment analysis (BSA) datasets from Google Dataset Search. Our analyses showed that BSA models exhibit biases across different identity categories despite having similar semantic content and structure. We also examined the inconsistencies and uncertainties arising from combining pre-trained models and datasets created by individuals from diverse demographic backgrounds. We connected these findings to the broader discussions on epistemic injustice, AI alignment, and methodological decisions in algorithmic audits.

Language operates as a mechanism of both marginalization and resistance, especially for minority communities navigating insensitive and harmful speech online. As content moderation increasingly depends on large language models (LLMs), concerns arise about whether these systems can recognize culturally insensitive speech–language that disregards or marginalizes the cultural and religious perspectives of historically underrepresented communities, often through implicit erasure, misrepresentation, or normative framing, rather than overt hostility. Focusing on Bangladesh’s Hindu and Chakma communities – the country’s largest religious and Indigenous ethnic minorities, respectively – this paper investigates the epistemic limits of LLM-based moderation systems and explores methods for incorporating minority perspectives. We co-created a culturally grounded corpus of insensitive speech with community members and integrated their narratives into moderation pipelines using retrieval augmented generation (RAG). Our tool, Mod-Guide, improves LLM sensitivity to minority viewpoints by leveraging contextual cues derived from lived experience. Through mixed-method evaluations involving both minority and majority participants, we demonstrate that RAG-enhanced moderation responses are more contextually accurate and perceived differently across ethnic lines. This work advances research in human-computer interaction, AI ethics, and social computing by foregrounding restorative justice and hermeneutical inclusion in the design of content moderation systems.

This paper examines how algorithmic accountability in Canada's visa system is articulated institutionally and experienced by applicants across borders. We analyzed Immigration, Refugees and Citizenship Canada (IRCC)'s Algorithmic Impact Assessment (AIA) for the temporary resident visa (TRV) triage system using the algorithmic decision-making adapted for the public sector (ADMAPS) framework and analyzed Reddit discussions among applicants using a mixed-methods approach. We show that while institutional artifacts emphasize transparency, procedural safeguards, and bounded impacts, applicants engage in collective sensemaking to interpret opaque decisions, often relying on peer knowledge amid uncertainty. We identify three asymmetries between how institutional accountability is structured and how people perceive the process: epistemic asymmetry in access to decision logic, jurisdictional asymmetry in exposure shaped by geopolitical positioning, and temporal--relational asymmetry in how waiting and uncertainty are experienced. We emphasize why it is important to shift attention from institutional design to the uneven distribution of experiences with public-sector algorithmic governance. Together, these contributions demonstrate how algorithmic governance systems in the context of transnational migration produce structured asymmetries not captured by institutional disclosure frameworks, and how extending ADMAPS can account for those uneven translations of accountability.

As data-driven solutions become increasingly prevalent in society, embedding ethics and human-centered content in computing courses is crucial for educating responsible professionals. We conducted a pilot study embedding human-centered material and activities into a semester-long introductory programming course for information science graduate students. Through five semi-structured interviews, we found that integrating real-world datasets and reflections advanced both technical skills and awareness of societal impacts. However, participants expressed uncertainty about applying ethical decision-making principles in workplace contexts, particularly when confronting unethical practices by colleagues or superiors. Our findings suggest that while integrated human-centered content effectively complements technical education, additional scaffolding is needed to empower students for ethical action in professional settings. We outline future directions for embedding human-centered content throughout information science curricula to reinforce these competencies alongside technical skills.

Homelessness systems in North America adopt coordinated data-driven approaches to efficiently match support services to clients based on their assessed needs and available resources. AI tools are increasingly being implemented to allocate resources, reduce costs and predict risks in this space. In this study, we conducted an ethnographic case study on the City of Toronto's homelessness system's data practices across different critical points. We show how the City's data practices offer standardized processes for client care but frontline workers also engage in heuristic decision-making in their work to navigate uncertainties, client resistance to sharing information, and resource constraints. From these findings, we show the temporality of client data which constrain the validity of predictive AI models. Additionally, we highlight how the City adopts an iterative and holistic client assessment approach which contrasts to commonly used risk assessment tools in homelessness, providing future directions to design holistic decision-making tools for homelessness.

The reference to assumptions in how practitioners use or interact with machine learning (ML) systems is ubiquitous in HCI and responsible ML discourse.However, what remains unclear from prior works is the conceptualization of assumptions and how practitioners identify and handle assumptions throughout their workflows.This leads to confusion about what assumptions are and what needs to be done with them.We use the concept of an argument from Informal Logic, a branch of Philosophy, to offer a new perspective to understand and explicate the confusions surrounding assumptions.Through semi-structured interviews with 22 ML practitioners, we find what contributes most to these confusions is how independently assumptions are constructed, how reactively and reflectively they are handled, and how nebulously they are recorded.Our study brings the peripheral discussion of assumptions in ML to the center and presents recommendations for practitioners to better think about and work with assumptions.

Agentic AI is being heralded as the next step in the development of AI systems. Agentics, complex ensembles of different machine learning, data processing, and generative AI models, can provide new autonomous and proactive decision-making capabilities to organizations, participate in complex workflows, and, when needed, seek guidance from and provide insights to human users in natural languages. Collectively, we wish to explore how and why to design agentic systems to be situated within specific social and organizational contexts, the value of social theory and perspectives to this work, and the potential of this move to address critical issues with AI. Given the focus on social and organization context as essential to agentic design, we see this work as directly related to the ACM COMPASS 2025 theme “computing in place”. We seek to bring together scholars from the diversity of disciplines within ACM to develop research agendas, projects, and joint teaching initiatives that support the development of social agentic design and analysis.

While colonization has sociohistorically impacted people’s identities across various dimensions, those colonial values and biases continue to be perpetuated by sociotechnical systems. One category of sociotechnical systems–sentiment analysis tools–can also perpetuate colonial values and bias, yet less attention has been paid to how such tools may be complicit in perpetuating coloniality, although they are often used to guide various practices (e.g., content moderation). In this paper, we explore potential bias in sentiment analysis tools in the context of Bengali communities who have experienced and continue to experience the impacts of colonialism. Drawing on identity categories most impacted by colonialism amongst local Bengali communities, we focused our analytic attention on gender, religion, and nationality. We conducted an algorithmic audit of all sentiment analysis tools for Bengali, available on the Python package index (PyPI) and GitHub. Despite similar semantic content and structure, our analyses showed that in addition to inconsistencies in output from different tools, Bengali sentiment analysis tools exhibit bias between different identity categories and respond differently to different ways of identity expression. Connecting our findings with colonially shaped sociocultural structures of Bengali communities, we discuss the implications of downstream bias of sentiment analysis tools.

Research into recidivism risk prediction in the criminal justice system has garnered significant attention from HCI, critical algorithm studies, and the emerging field of human-AI decision-making. This study focuses on algorithmic crime mapping, a prevalent yet underexplored form of algorithmic decision support (ADS) in this context. We conducted experiments and follow-up interviews with 60 participants, including community members, technical experts, and law enforcement agents (LEAs), to explore how lived experiences, technical knowledge, and domain expertise shape interactions with the ADS, impacting human-AI decision-making. Surprisingly, we found that domain experts (LEAs) often exhibited anchoring bias, readily accepting and engaging with the first crime map presented to them. Conversely, community members and technical experts were more inclined to engage with the tool, adjust controls, and generate different maps. Our findings highlight that all three stakeholders were able to provide critical feedback regarding AI design and use - community members questioned the core motivation of the tool, technical experts drew attention to the elastic nature of data science practice, and LEAs suggested redesign pathways such that the tool could complement their domain expertise.

Algorithmic decision-making is increasingly being adopted across public higher education. The expansion of data-driven practices by post-secondary institutions has occurred in parallel with the adoption of New Public Management approaches by neoliberal administrations. In this study, we conduct a qualitative analysis of an in-depth ethnographic case study of data and algorithms in use at a public college in Ontario, Canada. We identify the data, algorithms, and outcomes in use at the college. We assess how the college’s processes and relationships support those outcomes and the different stakeholders’ perceptions of the college’s data-driven systems. In addition, we find that the growing reliance on algorithmic decisions leads to increased student surveillance, exacerbation of existing inequities, and the automation of the faculty-student relationship. Finally, we identify a cycle of increased institutional power perpetuated by algorithmic decision-making, and driven by a push towards financial sustainability.

Homelessness is a humanitarian challenge affecting an estimated 1.6 billion people worldwide. In the face of rising homeless populations in developed nations and a strain on social services, government agencies are increasingly adopting data-driven models to determine one’s risk of experiencing homelessness and assigning scarce resources to those in need. We conducted a systematic literature review of 57 papers to understand the evolution of these decision-making algorithms. We investigated trends in computational methods, predictor variables, and target outcomes used to develop the models using a human-centered lens and found that only 9 papers (15.7%) investigated model fairness and bias. We uncovered tensions between explainability and ecological validity wherein predictive risk models (53.4%) unduly focused on reductive explainability while resource allocation models (25.9%) were dependent on unrealistic assumptions and simulated data that are not useful in practice. Further, we discuss research challenges and opportunities for developing human-centered algorithms in this area.

Though ML practitioners increasingly employ various Responsible ML (RML) strategies, their methodological approach in practice is still unclear. In particular, the constraints, assumptions, and choices of practitioners with technical duties–such as developers, engineers, and data scientists—are often implicit, subtle, and under-scrutinized in HCI and related fields. We interviewed 22 technically oriented ML practitioners across seven domains to understand the characteristics of their methodological approaches to RML through the lens of ideal and non-ideal theorizing of fairness. We find that practitioners’ methodological approaches fall along a spectrum of idealization. While they structured their approaches through ideal theorizing, such as by abstracting RML workflow from the inquiry of applicability of ML, they did not systematically document nor pay deliberate attention to their non-ideal approaches, such as diagnosing imperfect conditions. We end our paper with a discussion of a new methodological approach, inspired by elements of non-ideal theory, to structure technical practitioners’ RML process and facilitate collaboration with other stakeholders.

COVID Long Haul (CLH) is an emerging chronic illness with varied patient experiences. Our understanding of CLH is often limited to data from electronic health records (EHRs), such as diagnoses or problem lists, which do not capture the volatility and severity of symptoms or their impact. To better understand the unique presentation of CLH, we conducted a 3-month long cohort study with 14 CLH patients, collecting objective (EHR, daily Fitbit logs) and subjective (weekly surveys, interviews) data. Our findings reveal a complex presentation of symptoms, associated uncertainty, and the ensuing impact CLH has on patients’ personal and professional lives. We identify patient needs, practices, and challenges around adhering to clinical recommendations, engaging with health data, and establishing "new normals" post COVID. We reflect on the potential found at the intersection of these various data streams and the persuasive heuristics possible when designing for this new population and their specific needs.

Caseworkers in the child welfare (CW) sector use predictive decision-making algorithms built on risk assessment (RA) data to guide and support CW decisions. Researchers have highlighted that RAs can contain biased signals which flatten CW case complexities and that the algorithms may benefit from incorporating contextually rich case narratives, i.e. - the casenotes written by caseworkers. To investigate this hypothesized improvement, we quantitatively deconstructed two commonly used RAs from a United States CW agency. We trained classifier models to compare the predictive validity of RAs with and without casenote narratives and applied computational text analysis on casenotes to highlight topics uncovered in the casenotes. Our study finds that common risk metrics used to assess families and build CWS predictive risk models (PRMs) are unable to predict discharge outcomes for children who are not reunified with their birth parent(s). We also find that although casenotes cannot predict discharge outcomes, they contain contextual case signals. Given the lack of predictive validity of RA scores and casenotes, we propose moving beyond quantitative risk assessments for public sector algorithms and towards using contextual sources of information such as narratives to study public sociotechnical systems.

Smartphone enhances healthcare support for everyone, from local to remote patients. Recent advancements in smartphone sensors redefine their usage and the prospect of remote point-of-care tools (e.g., blood diagnostic devices), especially for low-resource settings. This paper studies the sufferings of rural people due to the limited healthcare facilities and figures out the implications. The proliferation of smartphone users suggests converting many smartphones into point-of-care diagnosis devices would be a life-saving decision. Previous studies showed smartphone’s built-in camera captures physiological features (e.g., hemoglobin) from fingertip videos captured under different lights. So, we created a mobile application and attachments (light sources) to record fingertip videos for hemoglobin level calculation. Then we collected feedback on how the rural users interacted with the application. Finally, we applied qualitative and quantitative analysis to investigate their answers. Their invaluable feedback reflected the implications of various aspects of a smartphone-based point-of-care tool. The findings unveil how rural-area people can receive a smartphone's blood diagnostic services. Our results will facilitate mobile health application designers and developers to build a smartphone-based point-of-care tool for any rural area people.

We are extremely happy to be able to present the Computer-Supported Cooperative Work and Social Computing (CSCW) community with this issue of the Proceedings of the ACM on Human-Computer Interaction, containing very interesting and relevant scholarship from its members. This issue includes 209 papers, of which 175 were accepted from the Jan 2023 cycle, and 34 were accepted from the Jul 2023 cycle. It reflects great efforts and contributions from external reviewers, Associate Chairs and Editors, who together have conducted a rigorous review process to select contributions of the highest quality advancing the CSCW field. As Track Chairs, we are grateful for the community's collective efforts to continue shaping and sharing CSCW's tradition of high-quality scholarship across the years.

The use of algorithms for decision-making in higher education is steadily growing, promising cost-savings to institutions and personalized service for students but also raising ethical challenges around surveillance, fairness, and interpretation of data. To address the lack of systematic understanding of how these algorithms are currently designed, we reviewed an extensive corpus of papers proposing algorithms for decision-making in higher education. We categorized them based on input data, computational method, and target outcome, and then investigated the interrelations of these factors with the application of human-centered lenses: theoretical, participatory, or speculative design. We found that the models are trending towards deep learning, and increased use of student personal data and protected attributes, with the target scope expanding towards automated decisions. However, despite the associated decrease in interpretability and explainability, current development predominantly fails to incorporate human-centered lenses. We discuss the challenges with these trends and advocate for a human-centered approach.

Risk assessment algorithms are being adopted by public sector agencies to make high-stakes decisions about human lives. Algorithms model “risk” based on individual client characteristics to identify clients most in need. However, this understanding of risk is primarily based on easily quantifiable risk factors that present an incomplete and biased perspective of clients. We conducted a computational narrative analysis of child-welfare casenotes and draw attention to deeper systemic risk factors that are hard to quantify but directly impact families and street-level decision-making. We found that beyond individual risk factors, the system itself poses a significant amount of risk where parents are over-surveilled by caseworkers and lack agency in decision-making processes. We also problematize the notion of risk as a static construct by highlighting the temporality and mediating effects of different risk, protective, systemic, and procedural factors. Finally, we draw caution against using casenotes in NLP-based systems by unpacking their limitations and biases embedded within them.

We are again thrilled to be able to present the Computer-Supported Cooperative Work and Social Computing (CSCW) community with an issue of the Proceedings of the ACM on Human-Computer Interaction, containing very interesting and relevant scholarship from its members. This issue includes 138 papers, of which 82 were accepted from the July 2022 cycle, and 56 were accepted from the January 2023 cycle. It reflects great efforts and contributions from external reviewers, Associate Chairs and Editors, who together have conducted a rigorous review process to select contributions of the highest quality advancing the CSCW field. As Track Chairs, we are grateful for the community's collective efforts to continue shaping and sharing CSCW's tradition of high-quality scholarship across the years.

The COVID-19 pandemic transformed many aspects of health and daily life. A subset of people who were infected with the virus have ongoing chronic health issues that range in type of symptom and severity. In this study, we conducted a qualitative assessment of self-reported post-COVID symptoms from patients' electronic health records (EHR, n=564) and a randomized collection of Reddit and Twitter posts (n=500 for each). We show the inconsistencies in what types of symptoms are shared between platforms in addition to assessing the severity of the symptoms and how social media characterizations of post-COVID do not tell a complete story of this phenomenon. This research contributes to CSCW health literature by connecting digital traces of post-COVID with EHR data, critiquing the use of social media as a health proxy and points to its potential to add context to the analysis of traditional health data extracted from the EHR.

Caseworkers are trained to write detailed narratives about families in Child-Welfare (CW) which informs collaborative high-stakes decision-making. Unlike other administrative data, these narratives offer a more credible source of information with respect to workers’ interactions with families as well as underscore the role of systemic factors in decision-making. SIGCHI researchers have emphasized the need to understand human discretion at the street-level to be able to design human-centered algorithms for the public sector. In this study, we conducted computational text analysis of casenotes at a child-welfare agency in the midwestern United States and highlight patterns of invisible street-level discretionary work and latent power structures that have direct implications for algorithm design. Casenotes offer a unique lens for policymakers and CW leadership towards understanding the experiences of on-the-ground caseworkers. As a result of this study, we highlight how street-level discretionary work needs to be supported by sociotechnical systems developed through worker-centered design. This study offers the first computational inspection of casenotes and introduces them to the SIGCHI community as a critical data source for studying complex sociotechnical systems.

This note explores how various technology-mediated negative experiences and safety concerns of non-Western LGBTQ+ users, particularly from Bangladesh, hinder their continuing online interactions and self-presentation practices. Based on face-to-face and Skype semi-structured interviews (n=31), our initial results report that along with facing life-threatening harassing experiences online, Bangladeshi LGBTQ+ users also struggle with audience management and perceived privacy affordances that critically restrict their identity exploration and overall online participation, often forcing them to adopt fake/pseudo-identity online. These findings advocate for better design implications on safer social media participation, especially for LGBTQ+ users from non-Western contexts, and call for more attention to inclusive technologies.

A growing body of HCI research has sought to understand how online networks are utilized in the adoption and maintenance of disordered activities and behaviors associated with mental illness, including eating habits. However, individual-level influences over discrete online eating disorder (ED) communities are not yet well understood. This study reports results from a comprehensive network and content analysis (combining computational topic modeling and qualitative thematic analysis) of over 32,000 public tweets collected using popular ED-related hashtags during May 2020. Our findings indicate that this ED network in Twitter consists of multiple smaller ED communities where a majority of the nodes are exposed to unhealthy ED contents through retweeting certain influential central nodes. The emergence of novel linguistic indicators and trends (e.g., "#meanspo") also demonstrates the evolving nature of the ED network. This paper contextualizes ED influence in online communities through node-level participation and engagement, as well as relates emerging ED contents with established online behaviors, such as self-harassment.

Adverse Childhood Events (ACEs) are potentially traumatic events that occur in childhood (e.g., sexual abuse and maternal violence). Clinical research highlights the significant impact ACEs have on youth's mental health similar to other youth-related issues like traditional bullying and cyberbullying. However, research focused on the intersection of these two are limited. We report the results from a qualitative study that used electronic health record (EHR) data and clinical narratives from Parkview Behavioral Health hospital (n=719) to better understand the presentation of ACEs in patients who indicated cyber/bullying contributed to their inpatient hospital admission. Our deductive thematic analyses on the clinical narratives/notes and diagnoses highlight the connection of ACEs with cyber/bullying and other clinical diagnoses like depression, anxiety, PTSD, and ADD/ADHD. Additionally, our results point to potential impacts of the gender spectrum and other non-ACE indicators like adoption and the need for Department of Child Services (DCS). The outcome of this study provides distinct computational and clinical design guidelines for better collaborative decision making in healthcare, including the need for ACEs screening as standard-of-care within acute mental health settings. CAUTION: This paper includes graphic contents about adverse childhood traumas and events.

Social interaction across multiple online platforms is a challenge for gender and sexual minorities (GSM) due to the stigmatization they face, which increases the complexity of their self-presentation decisions. These online interactions and identity disclosures can be more complicated for GSM in non-Western contexts due to consequentially different audiences and perceived affordances by the users, and limited baseline understanding of the conflation of these two with local norms and the opportunities they practically represent. Using focus group discussions and semi-structured interviews, we engaged with 61 Hijra individuals from Bangladesh, a severely stigmatized GSM from south Asia, to understand their overall online participation and disclosure behaviors through the lens of personal social media ecosystems. We find that along with platform audiences, affordances, and norms, participant skill/knowledge, and cultural influences also impact navigation through multiple platforms, resulting in differential benefits from privacy features. This impacts how Hijra perceive online spaces, and shape their self-presentation and disclosure behaviors over time. Content Warning: This paper discusses graphic contents (e.g. rape and sexual harassment) related to Hijra.

Algorithms have permeated throughout civil government and society, where they are being used to make high-stakes decisions about human lives. In this paper, we first develop a cohesive framework of algorithmic decision-making adapted for the public sector (ADMAPS) that reflects the complex socio-technical interactions between human discretion, bureaucratic processes, and algorithmic decision-making by synthesizing disparate bodies of work in the fields of Human-Computer Interaction (HCI), Science and Technology Studies (STS), and Public Administration (PA). We then applied the ADMAPS framework to conduct a qualitative analysis of an in-depth, eight-month ethnographic case study of algorithms in daily use within a child-welfare agency that serves approximately 900 families and 1300 children in the mid-western United States. Overall, we found that there is a need to focus on strength-based algorithmic outcomes centered in social ecological frameworks. In addition, algorithmic systems need to support existing bureaucratic processes and augment human discretion, rather than replace it. Finally, collective buy-in in algorithmic systems requires trust in the target outcomes at both the practitioner and bureaucratic levels. As a result of our study, we propose guidelines for the design of high-stakes algorithmic decision-making tools in the child-welfare system, and more generally, in the public sector. We empirically validate the theoretically derived ADMAPS framework to demonstrate how it can be useful for systematically making pragmatic decisions about the design of algorithms for the public sector.

This paper examines volitionality of Facebook usage, that is, which individuals feel they have a choice about whether or not to use the site. It analyzes data from two large surveys, conducted three years apart. Across the two surveys, a variety of factors impacted whether or not respondents saw their Facebook usage as a matter of their own choice, such as engaging in non-use behaviors, measures of Facebook addiction, a sense of their own agency, and, across both studies, level of education. These results expand on prior literature around technology use and non-use, especially in terms of which populations may feel obligated to use, or be unwillingly prevented from using, social media such as Facebook. Furthermore, they provide potential implications both for future work and for technology policy.

The U.S. Child Welfare System (CWS) is charged with improving outcomes for foster youth; yet, they are overburdened and underfunded. To overcome this limitation, several states have turned towards algorithmic decision-making systems to reduce costs and determine better processes for improving CWS outcomes. Using a human-centered algorithmic design approach, we synthesize 50 peer-reviewed publications on computational systems used in CWS to assess how they were being developed, common characteristics of predictors used, as well as the target outcomes. We found that most of the literature has focused on risk assessment models but does not consider theoretical approaches (e.g., child-foster parent matching) nor the perspectives of caseworkers (e.g., case notes). Therefore, future algorithms should strive to be context-aware and theoretically robust by incorporating salient factors identified by past research. We provide the HCI community with research avenues for developing human-centered algorithms that redirect attention towards more equitable outcomes for CWS.

This paper joins a growing body of work within ICTD and related fields studying the privacy challenges in the Global South. While most of the existing work in this area has focused on uses of technology in personal and home settings, a large part of computing in the Global South centers around public places, such as commercial Digital Service Centers (DSCs). In this paper, we present the findings from a six-month-long ethnography studying 19 Digital Service Centers in Dhaka, Bangladesh. We find that infrastructural limitations, local power politics, lack of knowledge, and insufficient protection mechanisms lead to privacy vulnerabilities for the customers of these centers. We apply the lens of informal markets to analyze these vulnerabilities and connect our findings to the broader concerns of ICTD around development, ethics, and postcolonial computing and discuss potential design and policy implications around these issues.

Prior studies of technology non-use demonstrate the need for approaches that go beyond a simple binary distinction between users and non-users. This paper proposes a set of two different methods by which researchers can identify types of non/use relevant to the particular sociotechnical settings they are studying. These methods are demonstrated by applying them to survey data about Facebook non/use. The results demonstrate that the different methods proposed here identify fairly comparable types of non/use. They also illustrate how the two methods make different trade offs between the granularity of the resulting typology and the total sample size. The paper also demonstrates how the different typologies resulting from these methods can be used in predictive modeling, allowing for the two methods to corroborate or disconfirm results from one another. The discussion considers implications and applications of these methods, both for research on technology non/use and for studying social computing more broadly.

In this research paper, we analyzed what moments and activities make people happy, based on a collection of happy moments. We are focusing on specific happy moments from a collection of text responses that people have shared through the crowd-sourcing platform: Amazon Mechanical Turk (MTurk). Using crowd-sourcing to collect our data allows us to advance our understanding of the cause of happiness, by focusing on words and real human experiences. Workers of MTurk were asked to reflect on what makes them happy in a given period and share three specific moments in complete sentences. Through text-based analysis, we will look to see what other components have a role in making a specific event happy and further analyze how we can classify such words. Also, we dive deeper into specific subcategories of classifiers in an attempt to form insights about their happiness level based on specific factors. With the goal to extract features from the text in HappyDB, in this study we used the bag of words approach. Through doing so, our results were successful at predicting the happiness category, concerning both accuracy and context. Our models were able to accomplish the goal of understanding a happy moment and fit such a moment into one of the seven ground truth happiness categories we set at the beginning of this study. We finished the article with the ethical perspective of such research works and related social implications.

Prior research on anonymous social media (ASM) has studied the issue of sexual harassment and has revealed its connections to stereotyping, aggression, interpersonal relationships, and mental health among others [16, 24, 60]. However, the characteristics of such harassment in the context of low and middle-income countries (LMICs) in the global south has not received enough attention in the literature. This paper presents our findings on the use of ASM in Bangladesh based on an anonymous online survey of (n= 291) participants and semi-structured interviews with (n= 27) participants. Our study shows a wide prevalence of sexual harassment on anonymous social networks in Bangladesh, the relationship between a closely-knitted communal culture and anonymous harassment, and the lack of infrastructural support for the victims. We also propose a set of design and policy recommendations for such anonymous social media to extend the current ICTD literature on ensuring a safer online environment for women, especially in an LMIC.

Relatively little work has empirically examined use and non-use of social technologies as more than a dichotomous binary, despite increasing calls to do so. This paper compares three different forms of non/use that might otherwise fall under the single umbrella of Facebook "user": (1) those who have a current active account; (2) those who have deactivated their account; and (3) those who have considered deactivating but not actually done so. A subset of respondents (N=256) from a larger, demographically representative sample of internet users completed measures for usage and perceptions of Facebook, Facebook addiction, privacy experiences and behaviors, and demographics. Multinomial logistic regression modeling shows four specific variables as most predictive of a respondent's type: negative effects from "addictive" use, subjective intensity of Facebook usage, number of Facebook friends, and familiarity with or use of Facebook's privacy settings. These findings both fill gaps left by, and help resolve conflicting expectations from, prior work. Furthermore, they demonstrate how valuable insights can be gained by disaggregating "users" based on different forms of engagement with a given technology.

Mobile applications ("apps") developed to promote online safety for children are underutilized and rely heavily on parental control features that monitor and restrict their child's mobile activities. This asymmetry in parental surveillance initiates an interesting research question -- how do children themselves feel about such parental control apps? We conducted a qualitative analysis of 736 reviews of 37 mobile online safety apps from Google Play that were publicly posted and written by children (ages 8-19). Our results indicate that child ratings were significantly lower than that of parents with 76% of the child reviews giving apps a single star. Children felt that the apps were overly restrictive and invasive of their personal privacy, negatively impacting their relationships with their parents. We relate these findings with HCI literature on mobile online safety, including broader literature around privacy and surveillance, and outline design opportunities for online safety apps.

Previous work has explored regretful experiences on social media. In parallel, scholars have examined how people do not use social media. This paper aims to synthesize these two research areas and asks: Do regretful experiences on social media influence people to (consider) not using social media? How might this influence differ for different sorts of regretful experiences? We adopted a mixed methods approach, combining topic modeling, logistic regressions, and contingency analysis to analyze data from a web survey with a demographically representative sample of US internet users (n=515) focusing on their Facebook use. We found that experiences that arise because of users' own actions influence actual deactivation of their Facebook account, while experiences that arise because of others' actions lead to considerations of non-use. We discuss the implications of these findings for two theoretical areas of interest in HCI: individual agency in social media use and the networked dimensions of privacy.

Assessing the impact of regional or statewide interventions in primary and secondary school (K-12) computer science (CS) education is difficult for a variety of reasons. Qualitative survey data provide only a limited view of impacts, but quantitative data can be notoriously difficult to acquire at scale from large numbers of classrooms, schools, or local educational authorities. In this paper, we use several publicly available data sources to glean insights into public high school CS enrollments across an entire U.S. state. Course enrollments with NCES course codes and local descriptors, school-level demographic data, and school geographic attendance boundaries can be combined to highlight where CS offerings persist and thrive, how CS enrollments change over time, and the ultimate quantitative impact of a statewide intervention. We propose a more appropriate level of data aggregation for these types of quantitative studies than has been undertaken in previous work while demonstrating the importance of a contextual aggregation process. The results of our disparate impact analysis for the first time quantify the impact of a statewide Exploring Computer Science (ECS) program rollout on economic groups across the region. Our blueprint for this analysis can serve as a template to guide and assess large-scale K-12 CS interventions wherever detailed project evaluation methods cannot scale to encompass the entire study area, especially in cases where attribute heterogeneity is a significant issue.

With the rapid growth of ICT adoption in the Global South, crimes over and through digital technologies have also increased. Consequently, governments have begun to undertake a variety of different surveillance programs, which in turn provoke questions regarding citizens' privacy rights. However, both the concepts of privacy and of citizens' corresponding political rights have not been well-developed in HCI for non-Western contexts. This paper presents findings from a three-month long ethnography and online survey (n=606) conducted in Bangladesh, where the government recently imposed mandatory biometric registration for every mobile phone user. Our analysis surfaces important privacy and safety concerns regarding identity, ownership, and trust, and reveals the cultural and political challenges of imposing biometric registration program in Bangladesh. We also discuss how alternative designs of infrastructure, technology, and policy may better meet stakeholders' competing needs in the Global South.

This paper describes the use of the Delphi method as a means of incorporating study participants into the processes of data analysis and interpretation.As a case study, it focuses on perceptions about use and non-use of the social media site Facebook.The work presented here involves three phases.First, a large survey included both a demographically representative sample and a convenience sample.Second, a smaller follow-up survey presented results from that survey back to survey respondents.Third, a series of qualitative member checking interviews with additional survey respondents served to validate the findings of the follow-up survey.This paper demonstrates the utility of Delphi by highlighting the ways that it enables us to synthesize across these three study phases, advancing understanding of perceptions about social media use and non-use.The paper concludes by discussing the broader applicability of the Delphi method across CSCW research.

Researchers in information science and related areas have developed various methods for analyzing textual data, such as survey responses. This article describes the application of analysis methods from two distinct fields, one method from interpretive social science and one method from statistical machine learning, to the same survey data. The results show that the two analyses produce some similar and some complementary insights about the phenomenon of interest, in this case, nonuse of social media. We compare both the processes of conducting these analyses and the results they produce to derive insights about each method's unique advantages and drawbacks, as well as the broader roles that these methods play in the respective fields where they are often used. These insights allow us to make more informed decisions about the tradeoffs in choosing different methods for analyzing textual data. Furthermore, this comparison suggests ways that such methods might be combined in novel and compelling ways.

Grounded Theory Method (GTM) and Machine Learning (ML) are often considered to be quite different. In this note, we explore unexpected convergences between these methods. We propose new research directions that can further clarify the relationships between these methods, and that can use those relationships to strengthen our ability to describe our phenomena and develop stronger hybrid theories.

Employee engagement is a reflection of an employee's experience of work. Previous research has analyzed each employee's experience in terms of individual factors. We provide the first report of the influence on engagement of peers (who report to the same manager) and friends (who share social ties in an internal social network), using linear regression to model employee engagement in a sample of more than 44,000 employees. We show that an employee's engagement is associated with the engagement of her/his peers, friends, and manager. Our results contribute to analyses of social factors at work, and argue for revisions to existing theories of employee engagement.

This paper presents an analysis of the privacy issues associated with the practice of repairing broken digital objects in Bangladesh. Historically, research in Human-Computer Interaction (HCI), Information and Communication Technologies for Development (ICTD), and related disciplines has focused on the design and development of new interventions or technologies. As a result, the repair of old or broken technologies has been an often neglected topic of research. The goal of our work is to improve the practices surrounding the repair of digital artifacts in developing countries. Specifically, in this paper we examine the privacy challenges associated with the process of repairing digital artifacts, which usually requires that the owner of a broken artifact hand over the technology to a repairer. Findings from our ethnographic work conducted at 10 repair markets in Dhaka, Bangladesh, show a variety of ways in which the privacy of an individual's personal data may be compromised during the repair process. We also examine people's perceptions around privacy in repair and its connections with broader social and cultural values. Finally, we discuss the challenges and opportunities for future research to strengthen the repair ecosystem in developing countries. Taken together, our findings contribute to the growing discourse around post-use cycles of technology in ICTD and HCI.

OBJECTIVE: To understand self-monitoring strategies used independently of clinical treatment by individuals with bipolar disorder (BD), in order to recommend technology design principles to support mental health management. MATERIALS AND METHODS: Participants with BD (N = 552) were recruited through the Depression and Bipolar Support Alliance, the International Bipolar Foundation, and WeSearchTogether.org to complete a survey of closed- and open-ended questions. In this study, we focus on descriptive results and qualitative analyses. RESULTS: Individuals reported primarily self-monitoring items related to their bipolar disorder (mood, sleep, finances, exercise, and social interactions), with an increasing trend towards the use of digital tracking methods observed. Most participants reported having positive experiences with technology-based tracking because it enables self-reflection and agency regarding health management and also enhances lines of communication with treatment teams. Reported challenges stem from poor usability or difficulty interpreting self-tracked data. DISCUSSION: Two major implications for technology-based self-monitoring emerged from our results. First, technologies can be designed to be more condition-oriented, intuitive, and proactive. Second, more automated forms of digital symptom tracking and intervention are desired, and our results suggest the feasibility of detecting and predicting emotional states from patterns of technology usage. However, we also uncovered tension points, namely that technology designed to support mental health can also be a disruptor. CONCLUSION: This study provides increased understanding of self-monitoring practices, attitudes, and needs of individuals with bipolar disorder. This knowledge bears implications for clinical researchers and practitioners seeking insight into how individuals independently self-manage their condition as well as for researchers designing monitoring technologies to support mental health management.

Location sharing applications (LSA) have proliferated in recent years. Current research principally focuses on egocentric privacy issues and design but has historically not explored the impact of surveillance on location sharing behavior. In this paper, we examine homophily in friendship and surveillance networks for 65 foursquare users. Our results indicate that location surveillance networks are strongly homophilous along the lines of race and gender while friendship networks are weakly homophilous on income. Qualitatively, an analysis of comments and interviews provides support for a discourse around location surveillance, which is mainly social, collaborative, positive and participatory. We relate these findings with prior literature on surveillance, self-presentation and homophily and situate this study in existing HCI/CSCW scholarship.

Prior research shows that people often engage in deception when sharing location. Privacy concerns, social surveillance and impression management are the primary drivers of these types of behaviors. One methodological question that arises in this research context is the problem of reliable measurement to study predictors of deceptive location disclosure from usage data. In this note, we propose a simple experience sampling method (ESM) approach that is useful for studying this phenomenon. We describe our ESM deployment and report the results of a long term, quantitative study of 204 foursquare users over 1 year. Results indicate that physical distance, tie strength and order of visibility on the foursquare feed are significant predictors (with moderate to high effect sizes) of deceptive location disclosure. We connect these findings to the rich tradition of location disclosure behavior research in ubiquitous computing.

Location based social networking applications enable people to share their location with friends for social purposes by "checking in" to places they visit. Prior research suggests that both privacy and impression management motivate location disclosure concerns. In this interview study of foursquare users, we explore the ways people think about location sharing and its effects on impression management and formation. Results indicate that location-sharing decisions depend on the perceived visibility of the check-in, blur boundaries between public and private venues, and can initiate tensions within the foursquare friend network. We introduce the concept of "check-in transience" to explain factors contributing to impression management and argue that sharing location is often used as a signaling strategy to achieve social objectives.

Team science and collaboration have become crucial to addressing key research questions confronting society. Institutions that are spread across multiple geographic locations face additional challenges. To better understand the nature of cross‐campus collaboration within a single institution and the effects of institutional efforts to spark collaboration, we conducted a case study of collaboration atCornellUniversity using scientometric and network analyses. Results suggest that cross‐campus collaboration is increasingly common, but is accounted for primarily by a relatively small number of departments and individual researchers. Specific researchers involved in many collaborative projects are identified, and their unique characteristics are described. Institutional efforts, such as seed grants and topical retreats, have some effect for researchers who are central in the collaboration network, but were less clearly effective for others.