Browsing by Author "Luther, Kurt"

Now showing 1 - 20 of 60
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    Ajna: A Wearable Shared Perception System for Extreme Sensemaking
    Wilchek, Matthew; Luther, Kurt; Batarseh, Feras A. (ACM, 2024)
    This paper introduces the design and prototype of Ajna, a wearable shared perception system for supporting extreme sensemaking in emergency scenarios. Ajna addresses technical challenges in Augmented Reality (AR) devices, specifically the limitations of depth sensors and cameras. These limitations confine object detection to close proximity and hinder perception beyond immediate surroundings, through obstructions, or across different structural levels, impacting collaborative use. It harnesses the Inertial Measurement Unit (IMU) in AR devices to measure users? relative distances from a set physical point, enabling object detection sharing among multiple users across obstacles like walls and over distances. We tested Ajna's effectiveness in a controlled study with 15 participants simulating emergency situations in a multi-story building. We found that Ajna improved object detection, location awareness, and situational awareness, and reduced search times by 15%. Ajna's performance in simulated environments highlights the potential of artificial intelligence (AI) to enhance sensemaking in critical situations, offering insights for law enforcement, search and rescue, and infrastructure management.
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    The American Soldier Collaborative Digital Archive
    Gitre, Edward J. K.; Luther, Kurt (National Endowment for the Humanities, 2018-07-31)
    With funding provided by an NEH Foundation HCRR grant, PW-253776, our team finished the first phase of The American Soldier Collaborative Digital Archive. The goal of this project is to create a free, public website to house and disseminate an incomparable collection of historical sources that capture the experience of Americans who served in the U.S. Army during the Second World War.
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    Analyzing Networks with Hypergraphs: Detection, Classification, and Prediction
    Alkulaib, Lulwah Ahmad KH M. (Virginia Tech, 2024-04-02)
    Recent advances in large graph-based models have shown great performance in a variety of tasks, including node classification, link prediction, and influence modeling. However, these graph-based models struggle to capture high-order relations and interactions among entities effectively, leading them to underperform in many real-world scenarios. This thesis focuses on analyzing networks using hypergraphs for detection, classification, and prediction methods in social media-related problems. In particular, we study four specific applications with four proposed novel methods: detecting topic-specific influential users and tweets via hypergraphs; detecting spatiotemporal, topic-specific, influential users and tweets using hypergraphs; augmenting data in hypergraphs to mitigate class imbalance issues; and introducing a novel hypergraph convolutional network model designed for the multiclass classification of mental health advice in Arabic tweets. For the first method, existing solutions for influential user detection did not consider topics that could produce incorrect results and inadequate performance in that task. The proposed contributions of our work include: 1) Developing a hypergraph framework that detects influential users and tweets. 2) Proposing an effective topic modeling method for short texts. 3) Performing extensive experiments to demonstrate the efficacy of our proposed framework. For the second method, we extend the first method by incorporating spatiotemporal information into our solution. Existing influencer detection methods do not consider spatiotemporal influencers in social media, although influence can be greatly affected by geolocation and time. The contributions of our work for this task include: 1) Proposing a hypergraph framework that spatiotemporally detects influential users and tweets. 2) Developing an effective topic modeling method for short texts that geographically provides the topic distribution. 3) Designing a spatiotemporal topic-specific influencer user ranking algorithm. 4) Performing extensive experiments to demonstrate the efficacy of our proposed framework. For the third method, we address the challenge of bot detection on social media platform X, where there's an inherent imbalance between genuine users and bots, a key factor leading to biased classifiers. Our approach leverages the rich structure of hypergraphs to represent X users and their interactions, providing a novel foundation for effective bot detection. The contributions of our work include: 1) Introducing a hypergraph representation of the X platform, where user accounts are nodes and their interactions form hyperedges, capturing the intricate relationships between users. 2) Developing HyperSMOTE to generate synthetic bot accounts within the hypergraph, ensuring a balanced training dataset while preserving the hypergraph's structure and semantics. 3) Designing a hypergraph neural network specifically for bot detection, utilizing node and hyperedge information for accurate classification. 4) Conducting comprehensive experiments to validate the effectiveness of our methods, particularly in scenarios with pronounced class imbalances. For the fourth method, we introduce a Hypergraph Convolutional Network model for classifying mental health advice in Arabic tweets. Our model distinguishes between valid and misleading advice, leveraging high-order word relations in short texts through hypergraph structures. Our extensive experiments demonstrate its effectiveness over existing methods. The key contributions of our work include: 1) Developing a hypergraph-based model for short text multiclass classification, capturing complex word relationships through hypergraph convolution. 2) Defining four types of hyperedges to encapsulate local and global contexts and semantic similarities in our dataset. 3) Conducting comprehensive experiments in which the proposed model outperforms several baseline models in classifying Arabic tweets, demonstrating its superiority. For the fifth method, we extended our previous Hypergraph Convolutional Network (HCN) model to be tailored for sarcasm detection across multiple low-resource languages. Our model excels in interpreting the subtle and context-dependent nature of sarcasm in short texts by exploiting the power of hypergraph structures to capture complex, high-order relationships among words. Through the construction of three hyperedge types, our model navigates the intricate semantic and sentiment differences that characterize sarcastic expressions. The key contributions of our research are as follows: 1) A hypergraph-based model was adapted for the task of sarcasm detection in five short low-resource language texts, allowing the model to capture semantic relationships and contextual cues through advanced hypergraph convolution techniques. 2) Introducing a comprehensive framework for constructing hyperedges, incorporating short text, semantic similarity, and sentiment discrepancy hyperedges, which together enrich the model's ability to understand and detect sarcasm across diverse linguistic contexts. 3) The extensive evaluations reveal that the proposed hypergraph model significantly outperforms a range of established baseline methods in the domain of multilingual sarcasm detection, establishing new benchmarks for accuracy and generalizability in detecting sarcasm within low-resource languages.
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    Backdrop Explorer:  A Human-AI Collaborative Approach for Exploring Studio Backdrops in Civil War Portraits
    Lim, Ken Yoong (Virginia Tech, 2023-06-14)
    In historical photo research, the presence of painted backdrops have the potential to help identify subjects, photographers, locations, and jl{events surrounding} certain photographs. Yet, research processes around these backdrops are poorly documented, with no known tools to aid in the task. We propose a four-step human-AI collaboration workflow to support the jl{discovery} and clustering of these backdrops. Focusing on the painted backdrops of the American Civil War (1861 -- 1865), we present Backdrop Explorer, a content-based image retrieval (CBIR) system incorporating computer vision and novel user interactions. We evaluated Backdrop Explorer on nine users of diverse experience levels and found that all were able to effectively utilize Backdrop Explorer to find photos with similar backdrops. We also document current practices and pain points in Civil War backdrop research through user interviews. Finally, we discuss how our findings and workflow can be applied to other topics and domains.
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    Behind the Scenes: Evaluating Computer Vision Embedding Techniques for Discovering Similar Photo Backgrounds
    Dodson, Terryl Dwayne (Virginia Tech, 2023-07-11)
    Historical photographs can generate significant cultural and economic value, but often their subjects go unidentified. However, if analyzed correctly, visual clues in these photographs can open up new directions in identifying unknown subjects. For example, many 19th century photographs contain painted backdrops that can be mapped to a specific photographer or location, but this research process is often manual, time-consuming, and unsuccessful. AI-based computer vision algorithms could be used to automatically identify painted backdrops or photographers or cluster photos with similar backdrops in order to aid researchers. However, it is unknown which computer vision algorithms are feasible for painted backdrop identification or which techniques work better than others. We present three studies evaluating four different types of image embeddings – Inception, CLIP, MAE, and pHash – across a variety of metrics and techniques. We find that a workflow using CLIP embeddings combined with a background classifier and simulated user feedback performs best. We also discuss implications for human-AI collaboration in visual analysis and new possibilities for digital humanities scholarship.
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    Civil War Twin: Exploring Ethical Challenges in Designing an Educational Face Recognition Application
    Kusuma, Manisha (Virginia Tech, 2022-01-06)
    Facial recognition systems pose numerous ethical challenges around privacy, racial and gender bias, and accuracy, yet little guidance is available for designers and developers. We explore solutions to these challenges in a four-phase design process to create Civil War Twin (CWT), an educational web-based application where users can discover their lookalikes from the American Civil War era (1861-65) while learning more about facial recognition and history. Through this design process, we synthesize industry guidelines, consult with scholars of history, gender, and race, evaluate CWT in feedback sessions with diverse prospective users, and conduct a usability study with crowd workers. We iteratively formulate design goals to incorporate transparency, inclusivity, speculative design, and empathy into our application. We found that users' perceived learning about the strengths and limitations of facial recognition and Civil War history improved after using CWT, and that our design successfully met users' ethical standards. We also discuss how our ethical design process can be applied to future facial recognition applications.
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    CoListen
    Stewart, Michael Clark (Virginia Tech, 2018-09-19)
    Humans need to feel connected to one another. With each new technology we create and re-create ways to connect with others we care about. Thanks to the ubiquity of powerful mobile technology in certain parts of the world, we have nearly immediate access to those remote others. Despite these advances our shared experiences are diminishing, and the ways we most often connect with our remote framily members seem to be superficial and at the expense of more meaningful interaction with collocated family members. People are not likely to give up the convenience and entertainment afforded by their mobile technology, but might those same technologies be capable of supporting interactions that help the users be the selves they wish they were, rather than the consumers their technologies were designed to support? To investigate the space of technological support for people's feelings of togetherness I conducted three studies. The first study was a diary study over 14 days where I asked about the current practices of middle schoolers for communicating with friends out side of school and for listening to music. In the second study, I conducted a design charrette where participants designed a technology to support co-listening, and then tried my first prototype. CoListen is a streaming music player that supports a listener in listening to the same music at the same time as a friend or family member. CoListen is designed with the explicit intent of requiring as little of the listener's attention as possible. In the third study, I deployed Colisten v1.0 in the wild and conducted a 14-day diary study asking participants about their experiences. I found that many of the participants from my target population listen to music and communicate with their friends, and that phatic communication (as opposed to goal-oriented communication) was prominent. I also found participants to be interested in the idea of technology to support co-listening and intrigued by how few little the barrier to co-listening can be, and how little attention is required. In study 3 I found that people enjoyed the experience of remote co-listening and did listen to music as a background activity. Many participatns reported feeling more together with their framily members with whom they co-listened.
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    Combating Problematic Information Online with Dual Process Cognitive Affordances
    Bhuiyan, MD Momen (Virginia Tech, 2023-08-04)
    Dual process theories of mind have been developed over the last decades to posit that humans use heuristics or mental shortcuts (automatic) and analytical (reflective) reasoning while consuming information. Can such theories be used to support users' information consumption in the presence of problematic content in online spaces? To answer, I merge these theories with the idea of affordances from HCI to into the concept of dual process cognitive affordances, consisting of automatic affordance and reflective affordance. Using this concept, I built and tested a set of systems to address two categories of online problematic content: misinformation and filter bubbles. In the first system, NudgeCred, I use cognitive heuristics from the MAIN model to design automatic affordances for better credibility assessment of news tweets from mainstream and misinformative sources. In TransparencyCue, I show the promise of value-centered automatic affordance design inside news articles differentiating content quality. To encourage information consumption outside their ideological filter bubble, in NewsComp, I use comparative annotation to design reflective affordances that enable active engagement with stories from opposing-leaning sources. In OtherTube, I use parasocial interaction, that is, experiencing information feed through the eyes of someone else, to design a reflective affordance that enables recognition of filter bubbles in their YouTube recommendation feeds. Each system shows various degrees of success and outlines considerations in cognitive affordances design. Overall, this thesis showcases the utility of design strategies centered on dual process information cognition model of human mind to combat problematic information space.
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    Compete, Collaborate, Investigate: Exploring the Social Structures of Professional OSINT Investigations
    Belghith, Yasmine; Venkatagiri, Sukrit; Luther, Kurt (ACM, 2022-04-27)
    Online investigations are increasingly conducted by individuals with diverse skill levels and experiences, with mixed results. Novice investigations often result in vigilantism or doxxing, while expert investigations have greater success rates and fewer mishaps. Many of these experts are involved in a community of practice known as Open Source Intelligence (OSINT), with an ethos and set of techniques for conducting investigations using only publicly available data. Through semi-structured interviews with 14 expert OSINT investigators from nine different organizations, we examine the social dynamics of this community, including the collaboration and competition patterns that underlie their investigations. We also describe investigators’ use of and challenges with existing OSINT tools, and implications for the design of social computing systems to better support crowdsourced investigations.
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    CoSINT: Designing a Collaborative Capture the Flag Competition to Investigate Misinformation
    Venkatagiri, Sukrit; Mukhopadhyay, Anirban; Hicks, David; Brantly, Aaron F.; Luther, Kurt (ACM, 2023-07-10)
    Crowdsourced investigations shore up democratic institutions by debunking misinformation and uncovering human rights abuses. However, current crowdsourcing approaches rely on simplistic collaborative or competitive models and lack technological support, limiting their collective impact. Prior research has shown that blending elements of competition and collaboration can lead to greater performance and creativity, but crowdsourced investigations pose unique analytical and ethical challenges. In this paper, we employed a four-month-long Research through Design process to design and evaluate a novel interaction style called collaborative capture the fag competitions (CoCTFs). We instantiated this interaction style through CoSINT, a platform that enables a trained crowd to work with professional investigators to identify and investigate social media misinformation. Our mixed-methods evaluation showed that CoSINT leverages the complementary strengths of competition and collaboration, allowing a crowd to quickly identify and debunk misinformation. We also highlight tensions between competition versus collaboration and discuss implications for the design of crowdsourced investigations.
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    Critisearch for Scholarly Search
    Joshi, Sarang G. (Virginia Tech, 2018-11-30)
    Online search has empowered users with access to vast amounts of information. However, current online interfaces do not permit users to manipulate the hits on a search engine result page (SERP). This leads to the user adapting his/her own search style to suit the search engine instead of being the other way round. We present Critisearch, an online search interface that allows users to manipulate hits by upvoting, downvoting and sorting them such that they can be arranged in a user-defined order. Critisearch was originally developed for middle school children. However, we found an interesting dearth of studies on how graduate students conduct searches. In order to evaluate how the manipulation of hits can benefit users, we conducted a longitudinal study with 10 graduate students who used Critisearch and/or other search engine/s of their choice for conducting the scholarly search over a three week period. Results indicate that using Critisearch for hit manipulation enabled metacognitive scaffolding (plan, filter, sort information) on the search engine interface especially in exploratory search contexts. Critisearch seems to facilitate a conversation with the interface by enabling marking of hits. In addition, Critisearch also appears to promote reflection with the upvote/downvote capabilities for marking of hits available to the user. The hit manipulation and metacognitive scaffolding on the Critisearch interface encourages users to conduct their search tasks in a more breadth-first fashion as opposed to the commonly used depth-first search strategy. Using qualitative analysis, we discovered three distinct types of search tasks that users perform in a scholarly context namely, specific exploration, needle-in-a-haystack and general exploration. This analysis provides a starting point for a better understanding information needs of users in a scholarly context and a classification of search tasks thus, adding to the existing body of literature on nature of online search tasks.
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    Crowd Compositions for Bias Detection and Mitigation in Predicting Recidivism
    Mhatre, Sakshi Manish (Virginia Tech, 2024-09-30)
    This thesis explores an approach to predicting recidivism by leveraging crowdsourcing, contrasting traditional judicial discretion and algorithmic models. Instead of relying on judges or algorithms, participants predicted the likelihood of re-offending using the COMPAS dataset, which includes demographic and criminal record information. The study analyzed both quantitative and qualitative data to assess biases in human versus algorithmic predictions. Findings reveal that homogeneous crowds reflect the biases of their composition, leading to more pronounced gender and racial biases. In contrast, heterogeneous crowds, with equal and random distributions, present a more balanced view, though underlying biases still emerge. Both gender and racial biases influence how re-offending risk is perceived, significantly impacting risk evaluations. Specifically, crowds rated African American offenders as less likely to re-offend compared to COMPAS, which assigned them higher risk scores, while Caucasian and Hispanic offenders were perceived as more likely to re-offend by crowds. Gender differences also emerged, with males rated as less likely to re-offend and females as more likely. This study highlights crowdsourcing's potential to mitigate biases and provides insights into balancing consistency and fairness in risk assessments.
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    CrowdLayout: Crowdsourced Design and Evaluation of Biological Network Visualizations
    Singh, Divit P.; Lisle, Lee; Murali, T. M.; Luther, Kurt (ACM, 2018-04)
    Biologists often perform experiments whose results generate large quantities of data, such as interactions between molecules in a cell, that are best represented as networks (graphs). To visualize these networks and communicate them in publications, biologists must manually position the nodes and edges of each network to reflect their real-world physical structure. This process does not scale well, and graph layout algorithms lack the biological underpinnings to offer a viable alternative. In this paper, we present CrowdLayout, a crowdsourcing system that leverages human intelligence and creativity to design layouts of biological network visualizations. CrowdLayout provides design guidelines, abstractions, and editing tools to help novice workers perform like experts. We evaluated CrowdLayout in two experiments with paid crowd workers and real biological network data, finding that crowds could both create and evaluate meaningful, high-quality layouts. We also discuss implications for crowdsourced design and network visualizations in other domains.
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    Designing and Evaluating Object-Level Interaction to Support Human-Model Communication in Data Analysis
    Self, Jessica Zeitz (Virginia Tech, 2016-05-09)
    High-dimensional data appear in all domains and it is challenging to explore. As the number of dimensions in datasets increases, the harder it becomes to discover patterns and develop insights. Data analysis and exploration is an important skill given the amount of data collection in every field of work. However, learning this skill without an understanding of high-dimensional data is challenging. Users naturally tend to characterize data in simplistic one-dimensional terms using metrics such as mean, median, mode. Real-world data is more complex. To gain the most insight from data, users need to recognize and create high-dimensional arguments. Data exploration methods can encourage thinking beyond traditional one-dimensional insights. Dimension reduction algorithms, such as multidimensional scaling, support data explorations by reducing datasets to two dimensions for visualization. Because these algorithms rely on underlying parameterizations, they may be manipulated to assess the data from multiple perspectives. Manipulating can be difficult for users without a strong knowledge of the underlying algorithms. Visual analytics tools that afford object-level interaction (OLI) allow for generation of more complex insights, despite inexperience with multivariate data or the underlying algorithm. The goal of this research is to develop and test variations on types of interactions for interactive visual analytic systems that enable users to tweak model parameters directly or indirectly so that they may explore high-dimensional data. To study interactive data analysis, we present an interface, Andromeda, that enables non-experts of statistical models to explore domain-specific, high-dimensional data. This application implements interactive weighted multidimensional scaling (WMDS) and allows for both parametric and observation-level interaction to provide in-depth data exploration. We performed multiple user studies to answer how parametric and object-level interaction aid in data analysis. With each study, we found usability issues and then designed solutions for the next study. With each critique we uncovered design principles of effective, interactive, visual analytic tools. The final part of this research presents these principles supported by the results of our multiple informal and formal usability studies. The established design principles focus on human-centered usability for developing interactive visual analytic systems that enable users to analyze high-dimensional data through object-level interaction.
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    Designing for Schadenfreude (or, how to express well-being and see if youʼre boring people)
    André, Paul; Schraefel, M.C.; Dix, Alan; White, Ryen W.; Bernstein, Michael; Luther, Kurt (ACM, 2010)
    This position paper presents two studies of content not normally expressed in status updates—well-being and status feedback—and considers how they may be processed, valued and used for potential quality-of-life benefits in terms of personal and social reflection and awareness. Do I Tweet Good? (poor grammar intentional) is a site investigating more nuanced forms of status feedback than current microblogging sites allow, towards understanding self-identity, reflection, and online perception. Healthii is a tool for sharing physical and emotional well-being via status updates, investigating concepts of self-reflection and social awareness. Together, these projects consider furthering the value of microblogging on two fronts: 1) refining the online personal/social networking experience, and 2) using the status update for enhancing the personal/social experience in the offline world, and considering how to leverage that online/offline split. We offer results from two different methods of study and target groups—one co-workers in an academic setting, the other followers on Twitter—to consider how microblogging can become more than just a communication medium if it facilitates these types of reflective practice.
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    Designing Human-AI Collaborative Systems for Historical Photo Identification
    Mohanty, Vikram (Virginia Tech, 2023-08-30)
    Identifying individuals in historical photographs is important for preserving material culture, correcting historical records, and adding economic value. Historians, antiques dealers, and collectors often rely on manual, time-consuming approaches. While Artificial Intelligence (AI) offers potential solutions, it's not widely adopted due to a lack of specialized tools and inherent inaccuracies and biases. In my dissertation, I address this gap by combining the complementary strengths of human intelligence and AI. I introduce Photo Sleuth, a novel person identification pipeline that combines crowdsourced expertise with facial recognition, supporting users in identifying unknown portraits from the American Civil War era (1861--65). Despite successfully identifying numerous unknown photos, users often face the `last-mile problem' --- selecting the correct match(es) from a shortlist of high-confidence facial recognition candidates while avoiding false positives. To assist experts, I developed Second Opinion, an online tool that employs a novel crowdsourcing workflow, inspired by cognitive psychology, effectively filtering out up to 75% of facial recognition's false positives. Yet, as AI models continually evolve, changes in the underlying model can potentially impact user experience in such crowd--expert--AI workflows. I conducted an online study to understand user perceptions of changes in facial recognition models, especially in the context of historical person identification. Our findings showed that while human-AI collaborations were effective in identifying photos, they also introduced false positives. To reduce these misidentifications, I built Photo Steward, an information stewardship architecture that employs a deliberative workflow for validating historical photo identifications. Building on this foundation, I introduced DoubleCheck, a quality assessment framework that combines community stewardship and comprehensive provenance information, for helping users accurately assess photo identification quality. Through my dissertation, I explore the design and deployment of human-AI collaborative tools, emphasizing the creation of sustainable online communities and workflows that foster accurate decision-making in the context of historical photo identification.
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    Designing Human-Centered Collaborative Systems for School Redistricting
    Sistrunk, Virginia Andreea (Virginia Tech, 2024-07-24)
    In a multitude of nations, the provision of education is predominantly facilitated through public schooling systems. These systems are structured in accordance with school districts, which are geographical territories where educational institutions share identical administrative frameworks and frequently coincide with the confines of a city or county. To enhance the operational efficiency of these schooling systems, the demarcations of public schools undergo periodic modifications. This procedure, also known as school redistricting, invariably engenders a myriad of tensions within the associated communities. This dissertation addresses the potential and necessity to integrate geographically-enabled crowd-sourced input into the redistricting process, and concurrently presents and evaluates a feasible solution. The pivotal contributions of this dissertation encompass: i) the delineation of the interdisciplinary sub-field at the nexus of HCI, CSCW, and education policy, ii) the identification of requirements from participants proficient in traditional, face-to-face deliberations, representing a diverse array of stakeholder groups, iii) the conception of a self-serve interactive boundary optimization system, and iv) a comprehensive user study conducted during a live public school rezoning deliberation utilizing the newly proposed hybrid approach. The live study specifically elucidates the efficacy of key design choices and the representation and rationalization of intricate user constraints in civic deliberations and educational policy architecture. My research looks into four primary areas of exploration: (i) the application of computer science usability-design principles to augment and expedite the visual deconstruction of intricate multi-domain data, thereby enhancing comprehension for novice users, (ii) the identification of salient elements of experiential learning within the milieu of visual scaffolding, (iii) the development of a preliminary platform designed to expand the capacity for crowd-sourcing novice users in the act of reconciling geo-spatial constraints, and finally, (iv) the utilization of Human-Computer Interaction (HCI) and data-driven analysis to discern, consolidate, and inaugurate novel communication channels that foster the restoration of trust within communities. To do so, I analyzed the previous work that was done in the domain, proposed a new direction, and created a web-application, called Redistrict. This an on-line platform allows the user to generate and explore "what if" scenarios, express opinions, and participate asynchronously in proximity-based public school boundary deliberations. I first evaluated the perceived value added by Redistrict through a user study with 12 participants experienced in traditional in-person deliberations, representing multiple stakeholder groups. Subsequently, I expanded the testing to an online rezoning. As a result of all interactions and the use of the web application, the participants reported a better understanding of geographically enabled projections, proposals from public officials, and increased consideration of how difficult it is to balance multidisciplinary constraints. Here, I present the design possibilities used and the effective online aid for the issue of public school rezoning deliberations and redistricting. This data-driven approach aids the school board and decision makers by offering automated strategies, a straightforward, visual, and intuitive method to comprehend intricate geographical limitations. The users demonstrated the ability to navigate the interface without iii any previous training or explanation. In this work, I propose the following three new concepts: (i) A new interdisciplinary subfield for Human Computing Interaction -Computer Supported Cooperative Work that combines Computer Science, Geography, and Education Policy. We explain and demonstrate how single domain approach failed in supporting this field and how complex geo-spatial problems require intensive technology to simultaneously balance all education policy constraints. This sits only at the intersection of a multi-domain approach. (ii) A sophisticated deconstruction of intricate data sets is presented through this methodology. It enables users to assimilate, comprehend, and formulate decisions predicated on the information delineated on a geospatial representation, leveraging preexisting knowledge of geographical proximity, and engaging in scenario analysis. Each iterative attempt facilitates incremental understanding, epitomizing the concept of information scaffolding. The efficacy of this process is demonstrated by its ability to foster independent thought and comprehension, obviating the need for explicit instructions. This technique is henceforth referred to as 'visual scaffolding'. (iii) In our most recent investigation, we engage in an introspective analysis of the observed input in civic decision making. We present the proposition of integrating digital civic engagement with user geolocation data. We advocate for the balance of this input, as certain geographical areas may be disproportionately represented in civic deliberations. The introduction of a weighting mechanism could facilitate a deeper understanding of the foundational premises on which civic decisions are based. We coin the term 'digital geo-civics' to characterize this pioneering approach.
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    Designing Telehealth Rehabilitation Systems for Diverse Stakeholder Needs
    Clark, Juliet Ariana (Virginia Tech, 2021-05-26)
    The strengthening of community care and the development of co-managed telehealth systems are vital components in addressing growing critical healthcare issues encountered worldwide. The global COVID pandemic highlights the challenges in providing appropriate co-managed home-based care in a systemic and financially viable way at scale. To develop practical and sustainable solutions it is important to understand the individual, institutional, and socio-technical opportunities and barriers potentially encountered when attempting to design and implement telehealth systems as part of a broader social healthcare network. In this thesis, I describe my work assessing the feasibility of deploying telehealth systems within the context of home based physical rehabilitation. I conducted an online survey and in-depth interviews with occupational and physical therapists to determine the issues impacting their current practices and the likelihood that a telehealth rehabilitation system might support or hinder their practice. Findings from this qualitative work highlighted the importance of maintaining the patient/therapist relationship, the need to empower the caregiver, and the potential for telehealth systems to provide quantitative and qualitative proof of care and patient progress. Building on these insights, I designed an interactive tablet application to assist therapists with the efficient and seamless installation and calibration of a telehealth system for stroke rehabilitation in the home. The application was evaluated in two studies with non-expert and expert users. The results from these studies indicate the efficiency of the application resulting from this design approach and the rich potential for integration of the system into clinical practice.
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    Diverse Perspectives Can Mitigate Political Bias in Crowdsourced Content Moderation
    Thebault-Spieker, Jacob; Venkatagiri, Sukrit; Mine, Naomi; Luther, Kurt (ACM, 2023-06-12)
    In recent years, social media companies have grappled with defining and enforcing content moderation policies surrounding political content on their platforms, due in part to concerns about political bias, disinformation, and polarization. These policies have taken many forms, including disallowing political advertising, limiting the reach of political topics, fact-checking political claims, and enabling users to hide political content altogether. However, implementing these policies requires human judgement to label political content, and it is unclear how well human labelers perform at this task, or whether biases affect this process. Therefore, in this study we experimentally evaluate the feasibility and practicality of using crowd workers to identify political content, and we uncover biases that make it difficult to identify this content. Our results problematize crowds composed of seemingly interchangeable workers, and provide preliminary evidence that aggregating judgements from heterogeneous workers may help mitigate political biases. In light of these findings, we identify strategies to achieving fairer labeling outcomes, while also better supporting crowd workers at this task and potentially mitigating biases.
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    DoubleCheck: Designing Community-based Assessability for Historical Person Identification
    Mohanty, Vikram; Luther, Kurt (ACM, 2023)
    Historical photos are valuable for their cultural and economic significance, but can be difficult to identify accurately due to various challenges such as low-quality images, lack of corroborating evidence, and limited research resources. Misidentified photos can have significant negative consequences, including lost economic value, incorrect historical records, and the spread of misinformation that can lead to perpetuating conspiracy theories. To accurately assess the credibility of a photo identification (ID), it may be necessary to conduct investigative research, use domain knowledge, and consult experts. In this paper, we introduce DoubleCheck, a quality assessment framework for verifying historical photo IDs on Civil War Photo Sleuth (CWPS), a popular online platform for identifying American Civil War-era photos using facial recognition and crowdsourcing. DoubleCheck focuses on improving CWPS's user experience and system architecture to display information useful for assessing the quality of historical photo IDs on CWPS. In a mixed-methods evaluation of DoubleCheck, we found that users contributed a wide diversity of sources for photo IDs, which helped facilitate the community's assessment of these IDs through DoubleCheck's provenance visualizations. Further, DoubleCheck's quality assessment badges and visualizations supported users in making accurate assessments of photo IDs, even in cases involving ID conflicts.