huashen218/convxai-cia-dataset huggingface dataset:
Title: Not Just a Preference: Reducing Biased Decision-making on Dating Websites
Authors: family: Ma
given: Zilin; family: Gajos
given: Krzysztof Z.
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: implicit bias, dating websites

Abstract: As dating websites are becoming an essential part of how people meet intimate and romantic partners, it is vital to design these systems to be resistant to, or at least do not amplify, bias and discrimination. Instead, the results of our online experiment with a simulated dating website, demonstrate that popular dating website design choices, such as the user of the swipe interface (swiping in one direction to indicate a like and in the other direction to express a dislike) and match scores, resulted in people racially biases choices even when they explicitly claimed not to have considered race in their decision-making. This bias was significantly reduced when the order of information presentation was reversed such that people first saw substantive profile information related to their explicitly-stated preferences before seeing the profile name and photo. These results indicate that currently-popular design choices amplify people’s implicit biases in their choices of potential romantic partners, but the effects of the implicit biases can be reduced by carefully redesigning the dating website interfaces.
Title: OtherTube: Facilitating Content Discovery and Reflection by Exchanging YouTube Recommendations with Strangers
Authors: family: Bhuiyan
given: Md Momen; family: Bautista Isaza
given: Carlos Augusto; family: Mitra
given: Tanushree; family: Lee
given: Sang Won
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Recommender System, Content Discovery, Persona, YouTube, Filter Bubble, Social Comparison, Self-Reflection

Abstract: To promote engagement, recommendation algorithms on platforms like YouTube increasingly personalize users’ feeds, limiting users’ exposure to diverse content and depriving them of opportunities to reflect on their interests compared to others’. In this work, we investigate how exchanging recommendations with strangers can help users discover new content and reflect. We tested this idea by developing OtherTube—a browser extension for YouTube that displays strangers’ personalized YouTube recommendations. OtherTube allows users to (i) create an anonymized profile for social comparison, (ii) share their recommended videos with others, and (iii) browse strangers’ YouTube recommendations. We conducted a 10-day-long user study (n = 41) followed by a post-study interview (n = 11). Our results reveal that users discovered and developed new interests from seeing OtherTube recommendations. We identified user and content characteristics that affect interaction and engagement with exchanged recommendations; for example, younger users interacted more with OtherTube, while the perceived irrelevance of some content discouraged users from watching certain videos. Users reflected on their interests as well as others’, recognizing similarities and differences. Our work shows promise for designs leveraging the exchange of personalized recommendations with strangers.
Title: Designing Word Filter Tools for Creator-led Comment Moderation
Authors: family: Jhaver
given: Shagun; family: Chen
given: Quan Ze; family: Knauss
given: Detlef; family: Zhang
given: Amy X.
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: human-computer integration, FilterBuddy, content moderation, platform governance, content creators, online harassment, YouTube

Abstract: Online social platforms centered around content creators often allow comments on content, where creators can then moderate the comments they receive. As creators can face overwhelming numbers of comments, with some of them harassing or hateful, platforms typically provide tools such as word filters for creators to automate aspects of moderation. From needfinding interviews with 19 creators about how they use existing tools, we found that they struggled with writing good filters as well as organizing and revising their filters, due to the difficulty of determining what the filters actually catch. To address these issues, we present FilterBuddy, a system that supports creators in authoring new filters or building from pre-made ones, as well as organizing their filters and visualizing what comments are captured by them over time. We conducted an early-stage evaluation of FilterBuddy with YouTube creators, finding that participants see FilterBuddy not just as a moderation tool, but also a means to organize their comments to better understand their audiences.
Title: Who, What, Why and How? Towards the Monetary Incentive in Crowd Collaboration: A Case Study of Github’s Sponsor Mechanism
Authors: family: Zhang
given: Xunhui; family: Wang
given: Tao; family: Yu
given: Yue; family: Zeng
given: Qiubing; family: Li
given: Zhixing; family: Wang
given: Huaimin
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: donation, open source, GitHub, financial support, sponsor

Abstract: While many forms of financial support are currently available, there are still many complaints about inadequate financing from software maintainers. In May 2019, GitHub, the world’s most active social coding platform, launched the Sponsor mechanism as a step toward more deeply integrating open source development and financial support. This paper collects data on 8,028 maintainers, 13,555 sponsors, and 22,515 sponsorships and conducts a comprehensive analysis. We explore the relationship between the Sponsor mechanism and developers along four dimensions using a combination of qualitative and quantitative analysis, examining why developers participate, how the mechanism affects developer activity, who obtains more sponsorships, and what mechanism flaws developers have encountered in the process of using it. We find a long-tail effect in the act of sponsorship, with most maintainers’ expectations remaining unmet, and sponsorship has only a short-term, slightly positive impact on development activity but is not sustainable. While sponsors participate in this mechanism mainly as a means of thanking the developers of OSS that they use, in practice, the social status of developers is the primary influence on the number of sponsorships. We find that both the Sponsor mechanism and open source donations have certain shortcomings and need further improvements to attract more participants.
Title: Apéritif: Scaffolding Preregistrations to Automatically Generate Analysis Code and Methods Descriptions
Authors: family: Pang
given: Yuren; family: Reinecke
given: Katharina; family: Just
given: René
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Data analysis, Experiment design, Reproducibility, Preregistration

Abstract: The HCI community has been advocating preregistration as a practice to improve the credibility of scientific research. However, it remains unclear how HCI researchers preregister studies and what preregistration users perceive as benefits and challenges. By systematically reviewing the past four CHI proceedings and surveying 11 researchers, we found that only 1.11% of papers presented preregistered studies, though both authors and reviewers of preregistered studies perceive it as beneficial. Our formative studies revealed key challenges ranging from a lack of detail about the study design, hindering comprehensibility, to inconsistencies between preregistrations and published papers. To explore ways for addressing these issues, we developed Apéritif, a research prototype that scaffolds the preregistration process and automatically generates analysis code and a methods description. In an evaluation with 17 HCI researchers, we found that Apéritif reduces the effort of preregistering a study, facilitates researchers’ workflows, and promotes consistency between research artifacts.
Title: Automatically Generating and Improving Voice Command Interface from Operation Sequences on Smartphones
Authors: family: Pan
given: Lihang; family: Yu
given: Chun; family: Li
given: JiaHui; family: Huang
given: Tian; family: Bi
given: Xiaojun; family: Shi
given: Yuanchun
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: operation sequence, interaction-centered nature language understanding, voice command interface, generation system

Abstract: Using voice commands to automate smartphone tasks (e.g., making a video call) can effectively augment the interactivity of numerous mobile apps. However, creating voice command interfaces requires a tremendous amount of effort in labeling and compiling the graphical user interface (GUI) and the utterance data. In this paper, we propose AutoVCI, a novel approach to automatically generate voice command interface (VCI) from smartphone operation sequences. The generated voice command interface has two distinct features. First, it automatically maps a voice command to GUI operations and fills in parameters accordingly, leveraging the GUI data instead of corpus or hand-written rules. Second, it launches a complementary Q&A dialogue to confirm the intention in case of ambiguity. In addition, the generated voice command interface can learn and evolve from user interactions. It accumulates the history command understanding results to annotate the user’s input and improve its semantic understanding ability. We implemented this approach on Android devices and conducted a two-phase user study with 16 and 67 participants in each phase. Experimental results of the study demonstrated the practical feasibility of AutoVCI.
Title: TaleBrush: Sketching Stories with Generative Pretrained Language Models
Authors: family: Chung
given: John Joon Young; family: Kim
given: Wooseok; family: Yoo
given: Kang Min; family: Lee
given: Hwaran; family: Adar
given: Eytan; family: Chang
given: Minsuk
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: sketching, creativity support tool, controlled generation, story writing, story generation

Abstract: While advanced text generation algorithms (e.g., GPT-3) have enabled writers to co-create stories with an AI, guiding the narrative remains a challenge. Existing systems often leverage simple turn-taking between the writer and the AI in story development. However, writers remain unsupported in intuitively understanding the AI’s actions or steering the iterative generation. We introduce TaleBrush, a generative story ideation tool that uses line sketching interactions with a GPT-based language model for control and sensemaking of a protagonist’s fortune in co-created stories. Our empirical evaluation found our pipeline reliably controls story generation while maintaining the novelty of generated sentences. In a user study with 14 participants with diverse writing experiences, we found participants successfully leveraged sketching to iteratively explore and write stories according to their intentions about the character’s fortune while taking inspiration from generated stories. We conclude with a reflection on how sketching interactions can facilitate the iterative human-AI co-creation process.
Title: Symphony: Composing Interactive Interfaces for Machine Learning
Authors: family: Bäuerle
given: Alex; family: Cabrera
given: Ángel Alexander; family: Hohman
given: Fred; family: Maher
given: Megan; family: Koski
given: David; family: Suau
given: Xavier; family: Barik
given: Titus; family: Moritz
given: Dominik
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: visualization, Machine learning, documentation, computational notebooks, interactive programming, AI

Abstract: Interfaces for machine learning (ML), information and visualizations about models or data, can help practitioners build robust and responsible ML systems. Despite their benefits, recent studies of ML teams and our interviews with practitioners (n=9) showed that ML interfaces have limited adoption in practice. While existing ML interfaces are effective for specific tasks, they are not designed to be reused, explored, and shared by multiple stakeholders in cross-functional teams. To enable analysis and communication between different ML practitioners, we designed and implemented Symphony, a framework for composing interactive ML interfaces with task-specific, data-driven components that can be used across platforms such as computational notebooks and web dashboards. We developed Symphony through participatory design sessions with 10 teams (n=31), and discuss our findings from deploying Symphony to 3 production ML projects at Apple. Symphony helped ML practitioners discover previously unknown issues like data duplicates and blind spots in models while enabling them to share insights with other stakeholders.
Title: Bivariate Effective Width Method to Improve the Normalization Capability for Subjective Speed-accuracy Biases in Rectangular-target Pointing
Authors: family: Yamanaka
given: Shota; family: Usuba
given: Hiroki; family: Miyashita
given: Homei
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: crowdsourcing, Fitts’ law, pointing, human motor performance, graphical user interface

Abstract: The effective width method of Fitts’ law can normalize speed-accuracy biases in 1D target pointing tasks. However, in graphical user interfaces, more meaningful target shapes are rectangular. To empirically determine the best way to normalize the subjective biases, we ran remote and crowdsourced user experiments with three speed-accuracy instructions. We propose to normalize the speed-accuracy biases by applying the effective sizes to existing Fitts’ law formulations including width W and height H. We call this target-size adjustment the bivariate effective width method. We found that, overall, Accot and Zhai’s weighted Euclidean model using the effective width and height independently showed the best fit to the data in which the three instruction conditions were mixed (i.e., the time data measured in all instructions were analyzed with a single regression expression). Our approach enables researchers to fairly compare two or more conditions (e.g., devices, input techniques, user groups) with the normalized throughputs.
Title: Switching Between Standard Pointing Methods with Current and Emerging Computer Form Factors
Authors: family: Foley
given: Margaret Jean; family: Roy
given: Quentin; family: Huang
given: Da-Yuan; family: Li
given: Wei; family: Vogel
given: Daniel
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: input devices, foldable displays, mode switching

Abstract: We investigate performance characteristics when switching between four pointing methods: absolute touch, absolute pen, relative mouse, and relative trackpad. The established “subtraction method” protocol used in mode-switching studies is extended to test pairs of methods and accommodate switch direction, multiple baselines, and controlling relative cursor position. A first experiment examines method switching on and around the horizontal surface of a tablet. Results find switching between pen and touch is fastest, and switching between relative and absolute methods incurs additional time penalty. A second experiment expands the investigation to an emerging foldable all-screen laptop form factor where switching also occurs on an angled surface and along a smoothly curved hinge. Results find switching between trackpad and touch is fastest, with all switching times generally higher. Our work contributes missing empirical evidence for switching performance using modern input methods, and our results can inform interaction design for current and emerging device form factors.
Title: Evaluating Singing for Computer Input Using Pitch, Interval, and Melody
Authors: family: Zinck
given: Graeme; family: Vogel
given: Daniel
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: music, non-verbal vocal interactions

Abstract: In voice-based interfaces, non-verbal features represent a simple and underutilized design space for hands-free, language-agnostic interactions. We evaluate the performance of three fundamental types of voice-based musical interactions: pitch, interval, and melody. These interactions involve singing or humming a sequence of one or more notes. A 21-person study evaluates the feasibility and enjoyability of these interactions. The top performing participants were able to perform all interactions reasonably quickly (<5s) with average error rates between 1.3% and 8.6% after training. Others improved with training but still had error rates as high as 46% for pitch and melody interactions. The majority of participants found all tasks enjoyable. Using these results, we propose design considerations for using singing interactions as well as potential use cases for both standard computers and augmented reality glasses.
Title: Shaping Textile Sliders: An Evaluation of Form Factors and Tick Marks for Textile Sliders
Authors: family: Nowak
given: Oliver; family: Schäfer
given: René; family: Brocker
given: Anke; family: Wacker
given: Philipp; family: Borchers
given: Jan
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Design recommendations, Continuous input, Sliders, Non-wearables, Textile interfaces, Eyes-free interaction, Embroidery, Smart textiles

Abstract: Textile interfaces enable designers to integrate unobtrusive media and smart home controls into furniture such as sofas. While the technical aspects of such controllers have been the subject of numerous research projects, the physical form factor of these controls has received little attention so far. This work investigates how general design properties, such as overall slider shape, raised vs. recessed sliders, and number and layout of tick marks, affect users’ preferences and performance. Our first user study identified a preference for certain design combinations, such as recessed, closed-shaped sliders. Our second user study included performance measurements on variations of the preferred designs from study 1, and took a closer look at tick marks. Tick marks supported orientation better than slider shape. Sliders with at least three tick marks were preferred, and performed well. Non-uniform, equally distributed tick marks reduced the movements users needed to orient themselves on the slider.
Title: First Steps Towards Designing Electrotactons: Investigating Intensity and Pulse Frequency as Parameters for Electrotactile Cues
Authors: family: Alotaibi
given: Yosuef; family: Williamson
given: John H; family: Brewster
given: Stephen Anthony
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: pulse frequency, interaction design, intensity, Electrotactile feedback

Abstract: Electrotactile stimulation is a novel form of haptic feedback. There is little work investigating its basic design parameters and how they create effective tactile cues. This paper describes two experiments that extend our knowledge of two key parameters. The first investigated the combination of pulse width and amplitude (Intensity) on sensations of urgency, annoyance, valence and arousal. Results showed significant effects: increasing Intensity caused higher ratings of urgency, annoyance and arousal but reduced valence. We established clear levels for differentiating each sensation. A second study then investigated Intensity and Pulse Frequency to find out how many distinguishable levels could be perceived. Results showed that both Intensity and Pulse Frequency significantly affected perception, with four distinguishable levels of Intensity and two of Pulse Frequency. These results add significant new knowledge about the parameter space of electrotactile cue design and help designers select suitable properties to use when creating electrotactile cues.
Title: “It's A Drag”: Exploring How to Improve Parents’ Experiences of Managing Mobile Device Use During Family Time
Authors: family: Chin Derix
given: Eleanor; family: Wah Leong
given: Tuck; family: Prior
given: Julia
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Experiences, Parents, Parental Mediation, Family Technology Use, Mobile Devices

Abstract: Research reveals that managing mobile device use during family time can be a source of stress for parents. In particular, it can create conflict in their relationships. As such, there is a need to understand how these problematic experiences might be addressed by new approaches to technology design. This paper presents a study in which 14 parents were prompted to reflect on how their experiences and relationships could be improved by four design proposals. These proposals resulted from ideation workshops involving 12 professional designers, and were presented as scenario-based storyboards during interviews. Our interviews revealed three design approaches that appealed to parents. We describe seven benefits that parents imagined these approaches would have, and discuss ways in which they should be further explored. Thus, we contribute to a more complete understanding of how technology design might better support parents’ aspirations for how devices are used within the family.
Title: Exploring Technology-Mediated Parental Socialisation of Emotion: Leveraging an Embodied, In-situ Intervention for Child Emotion Regulation
Authors: family: Theofanopoulou
given: Nikki; family: Slovak
given: Petr
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: parent-child interaction, tangible interaction, emotion regulation, emotion socialisation, user-centred design

Abstract: Emotion-related parent-child interactions during early childhood play a crucial role in the development of emotion regulation, a fundamental life skill central to well-being. However, limited work in HCI has explored how technology could support parents in adopting supportive emotion socialisation practices. In this paper, we explore how an embodied, in-situ intervention in the form of a smart toy can impact emotion-related parent-child interactions in the home. We draw on (1) interviews with 29 parents of young children who had the smart toy for at least 1 month; (2) co-design workshops with 12 parents and 8 parenting course facilitators. We discuss how the smart toy impacted parent-child interactions around emotions for a subset of families, and draw on workshop data to explore how this could be designed for directly. Finally, we propose a set of design directions for technology-enabled systems aiming to elicit and scaffold specific parent-child interactions over time.