Engage and Mobilize! Understanding Evolving Patterns of Social Media Usage in Emergency ManagementThe work of Emergency Management (EM) agencies requires timely collection of relevant data to inform decision-making for operations and public communication before, during, and after a disaster. However, the limited human resources available to deploy for field data collection is a persistent problem of EM agencies. Thus, over the last decade, many of these agencies have started leveraging social media as a supplemental data source and a new venue to engage with the public. Such uses present both opportunities and challenges. While prior research has analyzed potential benefits and attitudes of practitioners and the public when leveraging social media during disasters, a gap exists in critical analysis of the actual practices and uses of social media among EM agencies, across both geographical regions and phases of the EM lifecycle - typically mitigation, preparedness, response, and recovery. In this paper, we conduct a mixed-method analysis to update and fill this gap on how EM practitioners in the U.S. and Europe use social media, building on a survey study of about 150 professionals and a follow-up interview study with 11 participants. Results indicate that using social media is no longer a non-traditional practice in operational and informational processes for decision-making of EM agencies working at both the local level (e.g., county or town) and non-local level (e.g., state/province, federal/national) for emergency management. Especially, the practitioners affiliated with agencies working at the local level have very high perceived value of social media for situational awareness (e.g., analyzing disaster extent and impact) and public communication (e.g., disseminating timely information and correcting errors in crisis coverage). Further, practitioners now engage with the public during the preparedness phase in order to mobilize them during the response phase. We present a model to understand the current practices of communication between the agencies and the public as well as among practitioners, while leveraging social media. We also discuss novel challenges including public fragmentation caused by the increasing use of multiple social media platforms, information integrity, and social listening expectations. We conclude with the policy, technological, and socio-technical needs to design future social media analytics systems to support the work of EM agencies in such communication.2025HPHemant Purohit et al.Humans vs. AI for Decision MakingCSCW
Persuasiveness of Conversational Agents for Targeted Advertising: Autism and Gen-AI ChatbotsMarketing firms are starting to leverage Generative-AI-based chatbots as a more persuasive form of advertising. This is potentially more harmful for autistic young adults with substantial support needs who tend to have lower financial literacy, be more trusting of bad actors when communicating through social technologies, and more likely to anthropomorphize objects. Thus, we investigated whether a chatbot assuming the likeness of a favorite celebrity would more greatly influence consumer preferences of this population when compared against either autistic individuals without substantial support needs or the general population. We conducted an experimental survey where participants 1) ranked their preferences of various consumer items, 2) performed intervening tasks answering several questions, 3) viewed a chatbot interaction where the chatbot exhibited opposite consumer preferences, and then 4) re-ranked their preferences for those items. We found that autistic young adults with substantial support needs were more likely to change their preferences than the sample from the general population. Moreover, they were also more likely to change their preferences than autistic young adults without substantial support needs. These findings suggest that autistic individuals with substantial support needs are more susceptible to celebrity chatbot persuasion. We discuss the risks and guardrails that need to be associated with deploying generative-AI-based chatbots for targeted advertising.2025KCKirsten Chapman et al.Identifying and Mitigating AI RisksCSCW
Choose From a List: A User Study of Random Password MemorabilityEven for users of password managers, primary passwords are a common root of trust; these must be secure against offline attacks. Randomly generated passwords provide strength guarantees but are less memorable. Cognitive psychology studies have found that providing a choice aids recall, however no studies have investigated the impact of choice on password recall in isolation. To address this, we conducted a longitudinal user study (N=861 at initial follow-up) where users selected and memorized a password from a list of 1, 8, 32, or 128 random passwords. The users entered their password multiple times after selection to improve memory, and we followed up 7 and 28 days later. We found no evidence that selecting from a list improved memorability, which suggests designers and researchers should explore other avenues. Finally, we identify potential directions for new interfaces that help users generate random passwords that will be easier to use.2025MCMichael Clark et al.Brigham Young University, Internet Security Research LabPasswords & AuthenticationPrivacy Perception & Decision-MakingCHI
A House Divided: How U.S. Politics Could Shape Contact-Tracing Adoption in Future PandemicsContact tracing has shown to be an effective tool in limiting the spread of transmittable diseases in countries where it is widely adopted. During the COVID-19 pandemic, contact tracing app adoption in the United States was low despite having the highest number of recorded cases worldwide. To better understand why, we conducted a survey (N=302, matched to U.S. census demographics) and found that political orientation overwhelmingly predicted attitudes towards COVID-19 and the adoption of contact tracing apps. These attitudes also overwhelmingly shaped people's willingness to participate in contact tracing for diseases in future pandemics. Our findings suggest that the politically charged environment surrounding COVID-19 in the U.S. may have a long-term impact on American's willingness to utilize contact tracing for diseases in future pandemics. We conclude with recommendations for technology designers and policymakers on how to overcome the sharp divide that has been driven by the political discourse in the U.S.2025GSGarrett Smith et al.Brigham Young UniversityAI Ethics, Fairness & AccountabilityContent Moderation & Platform GovernanceMisinformation & Fact-CheckingCHI
"I Know I'm Being Observed:" Video Interventions to Educate Users about Targeted Advertising on FacebookRecent work explores how to educate and encourage users to protect their online privacy. We tested the efficacy of short videos for educating users about targeted advertising on Facebook. We designed a video that utilized an emotional appeal to explain risks associated with targeted advertising (fear appeal), and which demonstrated how to use the associated ad privacy settings (digital literacy). We also designed a version of this video which additionally showed the viewer their personal Facebook ad profile, facilitating personal reflection on how they are currently being profiled (reflective learning). We conducted an experiment (n = 127) in which participants watched a randomly assigned video and measured the impact over the following 10 weeks. We found that these videos significantly increased user engagement with Facebook advertising preferences, especially for those who viewed the reflective learning content. However, those who only watched the fear appeal content were more likely to disengage with Facebook as a whole.2024GSGarrett Smith et al.Brigham Young UniversityPrivacy by Design & User ControlPrivacy Perception & Decision-MakingDark Patterns RecognitionCHI
Towards Digital Independence: Identifying the Tensions between Autistic Young Adults and Their Support Network When Mediating Social MediaWe conducted an ethnographically-informed study with 28 participants (9 autistic Young Adults or "YAs'" in need of substantial daily support, 6 parents, 13 support staff) to understand how autistic YAs self-regulate and receive mediation on social media. We found that autistic YAs relied on blanket boundary rules and struggled with impulse control; therefore, they coped by asking their support network to help them deal with negative social experiences. Their support networks responded by providing informal advice, in-the-moment instruction, and formal education, but often resorted to monitoring and restrictive mediation when more proactive approaches were ineffective. Overall, we saw boundary tensions arise between Autistic YAs and their support networks as they struggled to find the right balance between providing oversight versus promoting autonomy. This work contributes to the critical disability literature by revealing the benefits and tensions of allyship in the context of helping young autistic adults navigate social media.2024SCSpring Cullen et al.Brigham Young UniversityCognitive Impairment & Neurodiversity (Autism, ADHD, Dyslexia)Social Platform Design & User BehaviorEmpowerment of Marginalized GroupsCHI
Reactive or Proactive? How Robots Should Explain FailuresAs robots tackle increasingly complex tasks, the need for explanations becomes essential for gaining trust and acceptance. Explainable robotic systems should not only elucidate failures when they occur but also predict and preemptively explain potential issues. This paper compares explanations from Reactive Systems, which detect and explain failures after they occur, to Proactive Systems, which predict and explain issues in advance. Our study reveals that the Proactive System fosters higher perceived intelligence and trust and its explanations were rated more understandable and timely. Our findings aim to advance the design of effective robot explanation systems, allowing people to diagnose and provide assistance for problems that may prevent a robot from finishing its task.2024GLGregory LeMasurier et al.Explainable AI (XAI)Human-Robot Collaboration (HRC)HRI
Closing the Knowledge Gap in Designing Data Annotation Interfaces for AI-powered Disaster Management Analytic SystemsData annotation interfaces predominantly leverage ground truth labels to guide annotators toward accurate responses. With the growing adoption of Artificial Intelligence (AI) in domain-specific professional tasks, it has become increasingly important to help beginning annotators identify how their early-stage knowledge can lead to inaccurate answers, which in turn, helps to ensure quality annotations at scale. To investigate this issue, we conducted a formative study involving eight individuals from the field of disaster management, each possessing varying levels of expertise. The goal was to understand the prevalent factors contributing to disagreements among annotators when classifying Twitter messages related to disasters and to analyze their respective responses. Our analysis identified two primary causes of disagreement between expert and beginner annotators: 1) a lack of contextual knowledge or uncertainty about the situation, and 2) the absence of visual or supplementary cues. Based on these findings, we designed a Context interface, which generates aids that help beginners identify potential mistakes and provide the hidden context of the presented tweet. The summative study compares Context design with two widely used designs in data annotation UI, Highlight and Reasoning based interfaces. We found significant differences between these designs in terms of attitudinal and behavioral data. We conclude with implications for designing future interfaces aiming at closing the knowledge gap among annotators.2024ZAZinat Ara et al.Explainable AI (XAI)Field StudiesComputational Methods in HCIIUI
A Tale of Two Cultures: Comparing Interpersonal Information Disclosure Norms on TwitterWe present an exploration of cultural norms surrounding online disclosure of information about one's interpersonal relationships (such as information about family members, colleagues, friends, or lovers). Our study extends the prior literature on Multi-Party Privacy and information disclosure by probing on cultural differences. In order to identify tweets about one's interpersonal relationships, we performed a two step process. First, we utilized a card-sort study to develop a culturally-sensitive saturated taxonomy of words that represent interpersonal relationships (e.g., ma, mom, mother). Then we developed a high-accuracy interpersonal disclosure detector based on dependency-parsing (F1-score: 86\%) to identify when the words refer to a personal relationship of the poster (e.g., "my mom" as opposed to "a mom"). This allowed us to filter through more than 2 million tweets posted in the U.S. and India over a 3 month period. We thus identified 400K+ tweets that disclosed information about the poster's interpersonal relationships. Prior literature identifies the cultural dimension of individualism versus collectivism as being a major determinant of offline communication differences in terms of emotion, topic, and content disclosed. Thus, we took a mixed methods approach to analyze these differences between tweets from an individualist (U.S.) versus collectivist (India) society (e.g., comparing the amount of joy expressed about one's family). We identify several cultural differences in disclosure behaviors. We also reveal how a combination of qualitative and quantitative methods are needed to uncover these differences; Using just one or the other can be misleading. The paper concludes by providing recommendations on how future systems designers can study and design for culturally-sensitive interpersonal disclosure norms.2023MMMainack Mondal et al.Social BehaviorCSCW
Personalized Quest and Dialogue Generation in Role-Playing Games: A Knowledge Graph- and Language Model-based ApproachProcedural content generation (PCG) in video games offers unprecedented opportunities for customization and user engagement. Working within the specialized context of role-playing games (RPGs), we introduce a novel framework for quest and dialogue generation that places the player at the core of the generative process. Drawing on a hand-crafted knowledge base, our method grounds generated content with in-game context while simultaneously employing a large-scale language model to create fluent, unique, accompanying dialogue. Through human evaluation, we confirm that quests generated using this method can approach the performance of hand-crafted quests in terms of fluency, coherence, novelty, and creativity; demonstrate the enhancement to the player experience provided by greater dynamism; and provide a novel, automated metric for the relevance between quest and dialogue. We view our contribution as a critical step toward dynamic, co-creative narrative frameworks in which humans and AI systems jointly collaborate to create unique and user-specific playable experiences.2023TATrevor Ashby et al.Brigham Young UniversityHuman-LLM CollaborationRole-Playing & Narrative GamesCHI
Outside Where? A Survey of Climates and Built Environments in Studies of HCI outdoorsWe found significant gaps in the climates and built environments used as settings for studies of HCI outdoors. The experience of using a computer outdoors varies widely depending on location-specific factors such as weather and the availability of electricity. We surveyed 699 papers from CHI venues and found 101 studies involving a person and a computer interacting outdoors for which we could determine the study location. We categorized each study location by climate using the Koppen-Geiger scheme and by built environment using the Recreation Opportunity Spectrum. 91 of 101 studies took place in temperate or continental climates and 82 took place in urban settings. Emerging understanding of the ongoing impacts of climate change increases the importance of investigating HCI outdoors in a wider range of weather conditions. While some primitive natural settings have been preserved against development at great cost, we found no studies of HCI outdoors in those settings.2022MJMichael D Jones et al.Brigham Young U.Smart Cities & Urban SensingSustainable HCIClimate Change Communication ToolsCHI
Permission vs. App Limiters: Profiling Smartphone Users to Understand Differing Strategies for Mobile Privacy ManagementWe conducted a user study with 380 Android users, profiling them according to two key privacy behaviors: the number of apps installed and the Dangerous permissions granted to those apps. We identified four unique privacy profiles: 1) Privacy Balancers (49.74% of participants), 2) Permission Limiters (28.68% ), 3) App Limiters (14.74%), and 4) the Privacy Unconcerned (6.84%). App and Permission Limiters were significantly more concerned about perceived surveillance than Privacy Balancers and the Privacy Unconcerned. App Limiters had the lowest number of apps installed on their devices with the lowest intention of using apps and sharing information with them, compared to Permission Limiters who had the highest number of apps installed and reported higher intention to share information with apps. The four profiles reflect the differing privacy management strategies, perceptions, and intentions of Android users that go beyond the binary decision to share or withhold information via mobile apps.2022AAAshwaq Alsoubai et al.University of Central FloridaPrivacy by Design & User ControlPrivacy Perception & Decision-MakingIoT Device PrivacyCHI
Perceiving Affordances Differently: The Unintended Consequences When Young Autistic Adults Engage with Social MediaSocial media can facilitate numerous benefits, ranging from facilitating access to social, instrumental, financial, and other support, to professional development and civic participation. However, these benefits may not be generalizable to all users. Therefore, we conducted an ethnographic case study with eight Autistic young adults, ten staff members, and four parents to understand how Autistic users of social media engage with others, as well as any unintended consequences of use. We leveraged an affordances perspective to understand how Autistic young adults share and consume user-generated content, make connections, and engage in networked interactions with others via social media. We found that they often used a literal interpretation of digital affordances that sometimes led to negative consequences including physical harm, financial loss, social anxiety, feelings of exclusion, and inadvertently damaging their social relationships. We make recommendations for redesigning social media affordances to be more inclusive of neurodiverse users.2022XPXinru Page et al.Brigham Young UniversityCognitive Impairment & Neurodiversity (Autism, ADHD, Dyslexia)Universal & Inclusive DesignCHI
Comparing Perspectives around Human and Technology Support for Contact TracingVarious contact tracing approaches have been applied to help contain the spread of COVID-19, with technology-based tracing and human tracing among the most widely adopted. However, governments and communities worldwide vary in their adoption of digital contact tracing, with many instead choosing the human approach. We investigate how people perceive the respective benefits and risks of human and digital contact tracing through a mixed-methods survey with 291 respondents from the United States. Participants perceived digital contact tracing as more beneficial for protecting privacy, providing convenience, and ensuring data accuracy, and felt that human contact tracing could help provide security, emotional reassurance, advice, and accessibility. We explore the role of self-tracking technologies in public health crisis situations, highlighting how designs must adapt to promote societal benefit rather than just self-understanding. We discuss how future digital contact tracing can better balance the benefits of human tracers and technology amidst the complex contact tracing process and context.2021XLXi Lu et al.University of California, IrvineMental Health Apps & Online Support CommunitiesPrivacy by Design & User ControlCHI
Trustworthiness Perceptions of Social Media Resources Named after a Crisis EventPeople often create social media accounts and pages named after crisis events. We call such accounts and pages Crisis Named Resources (CNRs). CNRs share information about crisis events and are followed by many. Yet, they also appear suddenly (at crisis onset) and in most cases, the owners are unknown. Thus, it can be challenging for audiences in particular to know whether to trust (or not trust) these CNRs and the information they provide. In this study, we conducted surveys and interviews with members of the public and experts in crisis informatics, emergency response, and communication studies to evaluate the trustworthiness of CNRs named after the 2017 Hurricane Irma. Findings showed that participants evaluated trustworthiness based on their perceptions of a CNR’s content, information source, profile, and owner. Findings also show that if people perceive that a CNR owner has prior experience in crisis response, can help the public to respond to the event, understands the situation, has the best interests of affected individuals in mind, or will correct misinformation, they tend to trust that CNR. Participant demographics and expertise showed no effect on perceptions of trustworthiness.2020ACApoorva Chauhan et al.Misinformation and TrustCSCW
The DELAY Framework: Designing for Extended LAtencYThis paper introduces the Designing for Extended Latency (DELAY) Framework meant to inspire new systems that support social interaction in high-latency settings such as interplanetary communication, intermittent internet access, and time-zone incompatibilities. The framework includes six dimensions: Goal, Communication Genre, Sequencing, Cardinality, Mutability, and Responsiveness. We describe the iterative design process used to create the Framework, as well as three novel prototypes designed to increase social connectedness and social presence in high-latency situations: 1) the InSync app that allows partners to perform activities simultaneously even though they only see proof of their synchronicity later; 2) the After the Beep system that lets users leave IoT messages that are triggered by the recipients; and 3) the Surrogate platform where players play group battle games against "surrogate" artificial intelligence avatars that mimic unavailable individuals. Data from two design workshops validates the usefulness of the framework for generating new solutions to high-latency scenarios.2020DHDerek L. Hansen et al.Brigham Young UniversityRemote Work Tools & ExperienceParticipatory DesignUser Research Methods (Interviews, Surveys, Observation)CHI
I Don't Even Have to Bother Them!: Using Social Media to Automate the Authentication Ceremony in Secure MessagingThe privacy guaranteed by secure messaging applications relies on users completing an authentication ceremony to verify they are using the proper encryption keys. We examine the feasibility of social authentication, which partially automates the ceremony using social media accounts. We implemented social authentication in Signal and conducted a within-subject user study with 42 participants to compare this with existing methods. To generalize our results, we conducted a Mechanical Turk survey involving 421 respondents. Our results show that users found social authentication to be convenient and fast. They particularly liked verifying keys asynchronously, and viewing social media profiles naturally coincided with how participants thought of verification. However, some participants reacted negatively to integrating social media with Signal, primarily because they distrust social media services. Overall, automating the authentication ceremony and distributing trust with additional service providers is promising, but this infrastructure needs to be more trusted than social media companies.2019EVElham Vaziripour et al.Brigham Young UniversityPrivacy by Design & User ControlPasswords & AuthenticationCHI
PHUI-kit: Interface Layout and Fabrication on Curved 3D Printed ObjectsWe seek to make physical user interface (PHUI) design more like graphical user interface (GUI) design by using a drag-and drop interface to place widgets, allowing widgets to be repositioned and by hiding implementation details. PHUIs are interfaces built from tangible widgets arranged on the surfaces of physical objects. PHUI layout will become more important as we move from rectangular screens to purpose-built interactive devices. Approaches to PHUI layout based on sculpture make it difficult to reposition widgets, and software approaches do not involve placing widgets on the device exterior. We created PHUI-kit, a software approach to PHUI layout on 3D printed enclosures, which has a drag-and-drop interface, supports repositioning of widgets, and hides implementation details. We describe algorithms for placing widgets on curved surfaces, modifying the enclosure geometry, and routing wiring inside the enclosure. The tool is easy to use and supports a wide range of design possibilities.2018MJMichael D Jones et al.Brigham Young U.Desktop 3D Printing & Personal FabricationCircuit Making & Hardware PrototypingCHI
Closing the Loop: User-Centered Design and Evaluation of a Human-in-the-Loop Topic Modeling SystemHuman-in-the-loop topic modeling allows end users to guide the creation of topic models and improve the models' quality without requiring expertise in topic modeling algorithms. Prior work in this area either focuses on refinement implementation without understanding how users actually wish to improve the model or focuses on user wants without exposing them to a the effect user input has on the model. This work implements a set of user-preferred refinements identified from prior work. An interview study with twelve non-expert participants examines how end users are affected by issues that arise when the user is truly brought into the loop of the algorithm process. As these issues mirror those identified in interactive machine learning more broadly, such as unpredictability, latency, and trust, this work provides a mechanism for examining interactive machine learning challenges with non-expert end users through the lens of human-in-the-loop topic modeling. We find that although users experience unpredictability, their reactions vary from positive to negative, and surprisingly, we do not find any cases of distrust, but instead note instances where users perhaps trust the system too much or have too little confidence in themselves.2018ASAndrea Kleinsmith et al.Human-LLM CollaborationComputational Methods in HCIIUI
HCI Outdoors: Understanding Human-Computer Interaction in Outdoor RecreationHCI in outdoor recreation is a growing research area. While papers investigating systems in specific domains, such as biking, climbing, or skiing, are beginning to appear, the broader community is just beginning to form. The community still seems to lack a cohesive agenda for advancing our understanding of this application domain. The goal of this workshop is to bring together individuals interested in HCI outdoors to review past work, build a unifying research agenda, share ongoing work, encourage collaboration, and make plans for future meetings. The workshop will result in a report containing a research agenda, extensive annotated bibliography, an article about this topic and plans for unifying the community at future meetings.2018MJMichael D Jones et al.Brigham Young U.Context-Aware ComputingField StudiesCHI