Disclosure-Based Cyberbullying Support App: Victims' Psychological Well-Being, Awareness, and Support Through CounselingCyberbullying has significant psychological implications, including increased levels of depression, anxiety, and stress. Despite these implications, there is a lack of real-time, accessible mechanisms that support victims, particularly young adults. This study developed and evaluated the effectiveness of a progressive web application that connects victims with certified professionals, e.g., guidance counselors, to seek psychological support. The app offers informational resources, such as videos and testimonials on prevention, intervention, and support, to educate and increase awareness among victims. The study was carried out over a 3-month longitudinal period from April 14 to July 14, 2024, with 286 young adults, most of whom were victims aged 18–34 from two higher education institutions (HEIs) in Nigeria, using a mixed-methods approach. The participants were randomly assigned to two groups: (a) experimental group (the group that used the app), (b) control group (the group that did not use the app). Our findings demonstrate the app’s effectiveness in reducing depression, anxiety, and stress, increasing awareness, and a slight improvement in perceived support among participants in the experimental group. We discuss policy implications for integrating this application into educational frameworks and curricula development.2025SASadiq Aliyu et al.Supporting YouthCSCW
Comparing the Willingness to Share for Human-generated vs. AI-generated Fake NewsGenerative artificial intelligence (AI) presents large risks for society when it is used to create fake news. A crucial factor for fake news to go viral on social media is that users share such content. Here, we aim to shed light on the sharing behavior of users across human-generated vs. AI-generated fake news. Specifically, we study: (1)~What is the perceived veracity of human-generated fake news vs. AI-generated fake news? (2)~What is the user's willingness to share human-generated fake news vs. AI-generated fake news on social media? (3)~What socio-economic characteristics let users fall for AI-generated fake news? To this end, we conducted a pre-registered, online experiment with $N=$ 988 subjects and 20 fake news from the COVID-19 pandemic generated by GPT-4 vs. humans. Our findings show that AI-generated fake news is perceived as less accurate than human-generated fake news, but both tend to be shared equally. Further, several socio-economic factors explain who falls for AI-generated fake news.2024ABAmirsiavosh Bashardoust et al.Session 3e: Trusting the Machine: Fake News. AI Decision-Making, and AuditingCSCW
Digital, Analog, or Hybrid: Comparing Strategies to Support Self-ReflectionRecently, various digital solutions have emerged to enhance the process of self-reflection, which can be crucial for personal growth and resilience. However, whether technology can meaningfully match or augment a traditional approach like pen or paper remains to be ascertained. Our objective was to build an better understanding of design paradigms' role in introspection. Through formative iterations, informed by Self-Determination Theory (SDT), we designed and developed different tool formulations (\textit{Analogue}, \textit{Digital}, and \textit{Hybrid}) for comparison. Participants (\textit{N} = 48) received one variant, completing a pre- and post-six-week assessment with the Self Reflection and Insight Scale (SRIS) and intermediary self-reports for qualitative feedback. We found scores for \textit{Hybrid} and \textit{Digital} conditions change significantly, suggesting format decisions have meaningful impacts on the efficacy of designs to alter intrinsic motivation toward introspective behaviour. We also identify determinants and design considerations to help others conceive solutions to support or stimulate a component of broader well-being.2024JAJames Arnéra et al.Mental Health Apps & Online Support CommunitiesPrototyping & User TestingDIS
Participatory Design to Address Disclosure-Based CyberbullyingDisclosure-based cyberbullying is defined as sharing personal in- formation without consent, often with malicious intentions. This affects young adults and leads to long-term personal damage (i.e., career, reputation, and family). Current strategies on social media platforms fall short of addressing this problem. We engaged 20 young adults (18-34) through a participatory design approach in co-designing solutions that reflect their concerns, needs, and desires. Participants were divided into three groups based on their cyberbullying experience as victim, discloser, or attacker. Our partic- ipants designed 15 solutions for prevention, intervention, support, and awareness. These solutions reveal the need for privacy control, anonymity, and autonomy for victims, a restorative justice system, social media accountability, and the involvement of influencers and content creators in awareness and education. We provide im- plications for progressive and adaptive social media awareness campaigns, a compassionate justice system, and soft control of personal information ownership.2024SASadiq Aliyu et al.Privacy by Design & User ControlPrivacy Perception & Decision-MakingOnline Harassment & Counter-ToolsDIS
Designing a Data-Driven Survey System: Leveraging Participants' Online Data to Personalize SurveysUser surveys are essential to user-centered research in many fields, including human-computer interaction (HCI). Survey personalization—specifically, adapting questionnaires to the respondents' profiles and experiences—can improve reliability and quality of responses. However, popular survey platforms lack usable mechanisms for seamlessly importing participants’ data from other systems. This paper explores the design of a data-driven survey system to fill this gap. First, we conducted formative research, including a literature review and a survey of researchers (𝑁 = 52), to understand researchers’ practices, experiences, needs, and interests in a data-driven survey system. Then, we designed and implemented a minimum viable product called Data-Driven Surveys (DDS), which enables including respondents’ data from online service accounts (Fitbit, Instagram, and GitHub) in survey questions, answers, and flow/logic on existing survey platforms (Qualtrics and SurveyMonkey). Our system is open source and can be extended to work with more online service accounts and survey platforms. It can enhance the survey research experience for both researchers and respondents. A demonstration video is available here: https://doi.org/10.17605/osf.io/vedbj2024LVLev Velykoivanenko et al.University of LausanneUser Research Methods (Interviews, Surveys, Observation)Prototyping & User TestingCHI
Supporting Co-Regulation and Motivation In Learning Programming In Online ClassroomsSelf-regulation of learning in programming has been extensively investigated, emphasising an individual's metacognitive and motivational regulation components. However, learning often happens in socially situated contexts, and little emphasis has been paid to studying social modes of regulation in programming. We designed Thyone, a collaborative Jupyter Notebook extension to support learners' programming regulation in an online classroom context with the overall aim to foster their intrinsic motivation toward programming. Thyone's salient features - Flowchart, Discuss and Share Cell - incorporate affordances for learners to co-regulate their learning and drive their motivation. In an exploratory quasi-experimental study, we investigated learners' engagement with Thyone's features and assessed its influence on their learning motivation in an introductory programming course. We found that Thyone facilitated the co-regulation of programming learning and that the users' engagement with Thyone appeared to positively influence components of their motivation: interest, autonomy, and relatedness. Our results inform the design of technological interventions to support co-regulation in programming learning.2023LGLahari Goswami et al.Remote LearningCSCW
On the Potential of Mediation Chatbots for Mitigating Online Multiparty Privacy Conflicts - A Wizard-of-Oz StudySharing multimedia content, without obtaining consent from the people involved causes multiparty privacy conflicts (MPCs). However, social-media platforms do not proactively protect users from the occurrence of MPCs. Hence, users resort to out-of-band, informal communication channels, attempting to mitigate such conflicts. So far, previous works have focused on hard interventions that do not adequately consider the contextual factors (e.g., social norms, cognitive priming) or are employed too late (i.e., the content has already been seen). In this work, we investigate the potential of conversational agents as a medium for negotiating and mitigating MPCs. We designed MediationBot, a mediator chatbot that encourages consent collection, enables users to explain their points of view, and proposes solutions to finding a middle ground. We evaluated our design using a Wizard-of-Oz experiment with 𝑁 = 32 participants, where we found that MediationBot can effectively help participants to reach an agreement and to prevent MPCs. It produced a structured conversation where participants had well-clarified speaking turns. Overall, our participants found MediationBot to be supportive as it proposes useful middle-ground solutions. Our work informs the future design of mediator agents to support social-media users against MPCs.2023KNKavous Salehzadeh Niksirat et al.PrivacyCSCW
Supporting Collaboration in Introductory Programming Classes Taught in Hybrid Mode: A Participatory Design StudyHybrid learning modalities, where learners can attend a course in-person or remotely, have gained particular significance in post-pandemic educational settings. In introductory programming courses, novices' learning behaviour in the collaborative context of classrooms differs in hybrid mode from that of a traditional setting. Reflections from conducting an introductory programming course in hybrid mode led us to recognise the need for re-designing programming tools to support students' collaborative learning practices. We conducted a participatory design study with nine students, directly engaging them in design to understand their interaction needs in hybrid pedagogical setups to enable effective collaboration during learning. Our findings first highlighted the difficulties that learners face in hybrid modes. The results then revealed learners' preferences for design functionalities to enable collective notions, communication, autonomy, and regulation. Based on our findings, we discuss design principles and implications to inform the future design of collaborative programming environments for hybrid modes.2023LGLahari Goswami et al.Programming Education & Computational ThinkingCollaborative Learning & Peer TeachingDIS
Changes in Research Ethics, Openness, and Transparency in Empirical Studies between CHI 2017 and CHI 2022In recent years, various initiatives from within and outside the HCI field have encouraged researchers to improve research ethics, openness, and transparency in their empirical research. We quantify how the CHI literature might have changed in these three aspects by analyzing samples of 118 CHI 2017 and 127 CHI 2022 papers---randomly drawn and stratified across conference sessions. We operationalized research ethics, openness, and transparency into 45 criteria and manually annotated the sampled papers. The results show that the CHI 2022 sample was better in 18 criteria, but in the rest of the criteria, it has no improvement. The most noticeable improvements were related to research transparency (10 out of 17 criteria). We also explored the possibility of assisting the verification process by developing a proof-of-concept screening system. We tested this tool with eight criteria. Six of them achieved high accuracy and F1 score. We discuss the implications for future research practices and education. This paper and all supplementary materials are freely available at https://doi.org/10.17605/osf.io/n25d6.2023KNKavous Salehzadeh Niksirat et al.University of LausanneResearch Ethics & Open ScienceCHI
When Forcing Collaboration is the Most Sensible Choice: Desirability of Precautionary and Dissuasive Mechanisms to Manage Multiparty Privacy ConflictsIndividuals share increasing amounts of personal multimedia data, exposing themselves (uploaders) as well as others (data subjects). Non-consensual sharing of multimedia data that depicts others raises so-called multiparty privacy conflicts (MPCs), which can have severe consequences. To limit the incidence of MPCs, a family of Precautionary mechanisms have recently been developed that force uploaders to collaborate with the other data subjects to prevent MPCs. However, there is still very little work on understanding how users perceive the Precautionary mechanisms together with which ones they prefer and why. In addition, Precautionary mechanisms have some limitations, e.g., they require linking content to the co-owners’ identity. Therefore, we also explore alternatives to Precautionary mechanisms and propose a new class of solutions—Dissuasive mechanisms—that aim at deterring the uploaders from sharing without consent. We then present a user-centric comparison of Precautionary and Dissuasive mechanisms, through a large-scale survey (𝑁 = 1792). Our results showed that respondents prefer Precautionary to Dissuasive mechanisms. These enforce collaboration, provide more control to the data subjects, but also they reduce uploaders’ uncertainty around what is considered appropriate for sharing. We learned that threatening legal consequences is the most desirable Dissuasive mechanism, and that respondents prefer the mechanisms that threaten users with immediate consequences (compared with delayed consequences). Dissuasive mechanisms are in fact well received by frequent sharers and older users, while Precautionary mechanisms are preferred by women and younger users. We discuss the implications for design, including considerations about side leakages, consent collection, and censorship.2021MCMauro Cherubini et al.Privacy and SecurityCSCW
Elucidating Skills for Job Seekers: Insights and Critical Concerns from a Field Deployment in SwitzerlandThis article contributes results of a longitudinal field study of SkillsIdentifier, an employment tool originally designed and assessed in the United States (U.S.), to support "underrepresented" job seekers in identifying and articulating their employment skills. To understand whether the tool could support the needs of job seekers outside the U.S., we assessed it among 16 job seekers with limited education and language resources in Switzerland. While many of our results mirrored those of the U.S., we found that the tool was especially beneficial for non-French speaking immigrants who needed support describing their skills outside of their native language. We also found that listing skills like "active listening" without important context was insufficient and risked hiding key skills and meaning behind those skills to employers. Taking these factors into account, we illustrate the design implications of our findings and directions for practitioners who wish to design employment tools in support of job seekers, especially those who have traditionally been excluded from the labor market. We then provide insight into the potential for unintended consequences as a result of focusing solely on skills in a post-COVID labor market and contribute ways to mitigate them.2021MCMauro Cherubini et al.Job Search & Employment SupportParticipatory DesignDIS
"I thought you were okay": Participatory Design with Young Adults to Fight Multiparty Privacy Conflicts in Online Social NetworksAlthough sharing multimedia content on online social networks (OSNs) has many benefits, publishing photos or videos of other people---without obtaining permission---can cause multiparty privacy conflicts (MPCs). Early studies developed technical solutions and dissuasive approaches to address MPCs. However, none of these studies involved, in the design process, the OSN users who have experienced MPCs. Hence, they possibly overlooked the valuable experiences these individuals have accrued. To fill this gap, we recruited participants specifically from this population of users, and we involved them in participatory design sessions to find solutions to reduce the incidence of MPCs. To frame the activities of our participants, we borrowed terminology and concepts from a well-known framework used in the justice systems. Over the course of several design sessions, our participants designed 10 solutions to mitigate MPCs. The designed solutions are based on different mechanisms, including preventing MPCs from occurring, dissuading users from sharing, resolving the conflicts, and educating users about community standards. We discuss the open design and research opportunities suggested by the designed solutions and contribute an ideal workflow that synthesizes the best of each solution. We contribute to the innovation of privacy-enhancing technologies to limit the incidence of MPCs in OSNs.2021KNKavous Salehzadeh Niksirat et al.Privacy by Design & User ControlParticipatory DesignDIS