huashen218/convxai-cia-dataset huggingface dataset:
Title: Designing for the Bittersweet: Improving Sensitive Experiences with Recommender Systems
Authors: family: Lustig
given: Caitlin; family: Konrad
given: Artie; family: Brubaker
given: Jed R.
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: breakup, death, social media, technology-mediated reflection

Abstract: It is difficult to design systems that honor the complex and often contradictory emotions that can be surfaced by sensitive encounters with recommender systems. To explore the design and ethical considerations in this space, we interviewed 20 people who had recently seen sensitive content through Facebook’s Memories feature. Interviewees typically described how (1) expectedness, (2) context of viewing, and (3) what we describe as “affective sense-making” were important factors for how they perceived “bittersweet” content, a sensitizing concept from our interviews that we expand upon. To address these user needs, we pose provocations to support critical work in this area and we suggest that researchers and designers: (1) draw inspiration from no/low-technology artifacts, (2) use empirical research to identify contextual features that have negative impacts on users, and (3) conduct user studies on affective sense-making. CAUTION: This paper discusses difficult subject matter related to death and relationships.
Title: Immersive Speculative Enactments: Bringing Future Scenarios and Technology to Life Using Virtual Reality
Authors: family: Simeone
given: Adalberto L.; family: Cools
given: Robbe; family: Depuydt
given: Stan; family: Gomes
given: João Maria; family: Goris
given: Piet; family: Grocott
given: Joseph; family: Esteves
given: Augusto; family: Gerling
given: Kathrin
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Cross-Reality, Childcare, Speculative Enactments, Space Exploration., Quantified Pets, Design Fiction, Virtual Reality

Abstract: In this paper we present Immersive Speculative Enactments (ISEs), a novel concept that extends conventional Speculative Enactments to Virtual Reality. Through ISEs, participants are immersed in a speculative world depicted by the designers and can engage with it in its truest envisioned form. We explore this concept via four scenarios with increasing technological uncertainty: a glimpse in the daily life of the parent of a newborn baby; a Mixed Reality experience supporting hybrid classrooms; two wearable devices that present a pet’s emotional state and needs; and an enactment on the effect of communication delay across interplanetary distances. We discuss the concept of ISEs and contrast them to other forms of speculation, provide guidelines on how to design them, as well as reflecting on the challenges, limitations, and potential associated with the role of ISEs in the HCI discourse.
Title: “I Don’t Even Remember What I Read”: How Design Influences Dissociation on Social Media
Authors: family: Baughan
given: Amanda; family: Zhang
given: Mingrui Ray; family: Rao
given: Raveena; family: Lukoff
given: Kai; family: Schaadhardt
given: Anastasia; family: Butler
given: Lisa D.; family: Hiniker
given: Alexis
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: design, social media addiction, social media, normative dissociation, dissociation

Abstract: Many people have experienced mindlessly scrolling on social media. We investigated these experiences through the lens of normative dissociation: total cognitive absorption, characterized by diminished self-awareness and reduced sense of agency. To explore user experiences of normative dissociation and how design affects the likelihood of normative dissociation, we deployed Chirp, a custom Twitter client, to 43 U.S. participants. Experience sampling and interviews revealed that sometimes, becoming absorbed in normative dissociation on social media felt like a beneficial break. However, people also reported passively slipping into normative dissociation, such that they failed to absorb any content and were left feeling like they had wasted their time. We found that designed interventions–including custom lists, reading history labels, time limit dialogs, and usage statistics–reduced normative dissociation. Our findings demonstrate that interaction designs intended to capture attention likely do so by harnessing people’s natural inclination to seek normative dissociation experiences. This suggests that normative dissociation may be a more productive framing than addiction for discussing social media overuse.
Title: “I Will Not Drink With You Today”: A Topic-Guided Thematic Analysis of Addiction Recovery on Reddit
Authors: family: Gauthier
given: Robert P; family: Costello
given: Mary Jean; family: Wallace
given: James R
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Reddit, thematic analysis, online communities, addiction, machine learning

Abstract: Recovery from addiction is a journey that requires a lifetime of support from a strong network of peers. Many people seek out this support through online communities, like those on Reddit. However, as these communities developed outside of existing aid groups and medical practice, it is unclear how they enable recovery. Their scale also limits researchers’ ability to engage through traditional qualitative research methods. To study these groups, we performed a topic-guided thematic analysis that used machine-generated topic models to purposively sample from two recovery subreddits: r/stopdrinking and r/OpiatesRecovery. We show that these communities provide access to an experienced and accessible support group whose discussions include consequences, reflections, and celebrations, but that also play a distinct metacommunicative role in supporting formal treatment. We discuss how these communities can act as knowledge sources to improve in-person recovery support and medical practice, and how computational techniques can enable HCI researchers to study communities at scale.
Title: User Perspectives and Ethical Experiences of Apps for Depression: A Qualitative Analysis of User Reviews
Authors: family: Bowie-DaBreo
given: Dionne; family: Sas
given: Corina; family: Iles-Smith
given: Heather; family: Sünram-Lea
given: Sandra
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Ethics, Mobile mental health, User experiences, Depression, User reviews

Abstract: Apps for depression can increase access to mental health care but concerns abound with disparities between academic development of apps and those available through app stores. Reviews highlighted ethical shortcomings of these self-management tools, with a need for greater insight into how ethical issues are experienced by users. We addressed these gaps by exploring user reviews of such apps to better understand user experiences and ethical issues. We conducted a thematic analysis of 2,217 user reviews sampled from 40 depression apps in Google Play and Apple App Store, totaling over 77,500 words. Users reported positive and negative experiences, with ethical implications evident in areas of benefits, adverse effects, access, usability and design, support, commercial models, autonomy, privacy, and transparency. We integrated our elements of ethically designed apps for depression and principles of nonmaleficence, beneficence, justice, autonomy, and virtue, and we conclude with implications for ethical design of apps for depression.
Title: Mood Worlds: A Virtual Environment for Autonomous Emotional Expression
Authors: family: Wagener
given: Nadine; family: Niess
given: Jasmin; family: Rogers
given: Yvonne; family: Schöning
given: Johannes
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Emotions, Well-being, Emotion Regulation, Virtual Reality, Happiness, Positive Technology

Abstract: Immersive interactive technologies such as virtual reality (VR) have the potential to foster well-being. While VR applications have been successfully used to evoke positive emotions through the presetting of light, colour and scenery, the experiential potential of allowing users to independently create a virtual environment (VE) has not yet been sufficiently addressed. To that end, we explore how the autonomous design of a VE can affect emotional engagement and well-being. We present Mood Worlds – a VR application allowing users to visualise their emotions by self-creating a VE. In an exploratory evaluation (N=16), we found that Mood Worlds is an effective tool supporting emotional engagement. Additionally, we found that an autonomous creation process in VR increases positive emotions and well-being. Our work shows that VR can be an effective tool to visualise emotions, thereby increasing positive affect. We discuss opportunities and design requirements for VR as positive technology.
Title: AvatAR: An Immersive Analysis Environment for Human Motion Data Combining Interactive 3D Avatars and Trajectories
Authors: family: Reipschläger
given: Patrick; family: Brudy
given: Frederik; family: Dachselt
given: Raimund; family: Matejka
given: Justin; family: Fitzmaurice
given: George; family: Anderson
given: Fraser
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: analysing space utilization, human motion data, In-situ visualisation, augmented/mixed reality, motion analysis, Immersive Analytics

Abstract: Analysis of human motion data can reveal valuable insights about the utilization of space and interaction of humans with their environment. To support this, we present AvatAR, an immersive analysis environment for the in-situ visualization of human motion data, that combines 3D trajectories with virtual avatars showing people’s detailed movement and posture. Additionally, we describe how visualizations can be embedded directly into the environment, showing what a person looked at or what surfaces they touched, and how the avatar’s body parts can be used to access and manipulate those visualizations. AvatAR combines an AR HMD with a tablet to provide both mid-air and touch interaction for system control, as well as an additional overview device to help users navigate the environment. We implemented a prototype and present several scenarios to show that AvatAR can enhance the analysis of human motion data by making data not only explorable, but experienceable.
Title: ReLive: Bridging In-Situ and Ex-Situ Visual Analytics for Analyzing Mixed Reality User Studies
Authors: family: Hubenschmid
given: Sebastian; family: Wieland
given: Jonathan; family: Fink
given: Daniel Immanuel; family: Batch
given: Andrea; family: Zagermann
given: Johannes; family: Elmqvist
given: Niklas; family: Reiterer
given: Harald
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: data visualization, visual analytics, virtual reality., Immersive analytics

Abstract: The nascent field of mixed reality is seeing an ever-increasing need for user studies and field evaluation, which are particularly challenging given device heterogeneity, diversity of use, and mobile deployment. Immersive analytics tools have recently emerged to support such analysis in situ, yet the complexity of the data also warrants an ex-situ analysis using more traditional non-immersive visual analytics setups. To bridge the gap between both approaches, we introduce ReLive: a mixed-immersion visual analytics framework for exploring and analyzing mixed reality user studies. ReLive combines an in-situ virtual reality view with a complementary ex-situ desktop view. While the virtual reality view allows users to relive interactive spatial recordings replicating the original study, the synchronized desktop view provides a familiar interface for analyzing aggregated data. We validated our concepts in a two-step evaluation consisting of a design walkthrough and an empirical expert user study.
Title: A Design Space For Data Visualisation Transformations Between 2D And 3D In Mixed-Reality Environments
Authors: family: Lee
given: Benjamin; family: Cordeil
given: Maxime; family: Prouzeau
given: Arnaud; family: Jenny
given: Bernhard; family: Dwyer
given: Tim
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: animated transitions, direct manipulation, mixed reality, Immersive Analytics, visualisation

Abstract: As mixed-reality (MR) technologies become more mainstream, the delineation between data visualisations displayed on screens or other surfaces and those floating in space becomes increasingly blurred. Rather than the choice of using either a 2D surface or the 3D space for visualising data being a dichotomy, we argue that users should have the freedom to transform visualisations seamlessly between the two as needed. However, the design space for such transformations is large, and practically uncharted. To explore this, we first establish an overview of the different states that a data visualisation can take in MR, followed by how transformations between these states can facilitate common visualisation tasks. We then describe a design space of how these transformations function, in terms of the different stages throughout the transformation, and the user interactions and input parameters that affect it. This design space is then demonstrated with multiple exemplary techniques based in MR.
Title: HAExplorer: Understanding Interdependent Biomechanical Motions with Interactive Helical Axes
Authors: family: Eulzer
given: Pepe; family: Rockenfeller
given: Robert; family: Lawonn
given: Kai
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: coordinated views, focus and context, biomechanics, helical axis, contextualization, screw axis

Abstract: The helical axis is a common tool used in biomechanical modeling to parameterize the motion of rigid objects. It encodes an object’s rotation around and translation along a unique axis. Visualizations of helical axes have helped to make kinematic data tangible. However, the analysis process often remains tedious, especially if complex motions are examined. We identify multiple key challenges: the absence of interactive tools for the computation and handling of helical axes, visual clutter in axis representations, and a lack of contextualization. We solve these issues by providing the first generalized framework for kinematic analysis with helical axes. Axis sets can be computed on-demand, interactively filtered, and explored in multiple coordinated views. We iteratively developed and evaluated the HAExplorer with active biomechanics researchers. Our results show that the techniques we introduce open up the possibility to analyze non-planar, compound, and interdependent motion data.
Title: Preferences and Effectiveness of Sleep Data Visualizations for Smartwatches and Fitness Bands
Authors: family: Islam
given: Alaul; family: Aravind
given: Ranjini; family: Blascheck
given: Tanja; family: Bezerianos
given: Anastasia; family: Isenberg
given: Petra
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: smartphones, smartwatch, fitness trackers, glanceable visualization

Abstract: We present the findings of four studies related to the visualization of sleep data on wearables with two form factors: smartwatches and fitness bands. Our goal was to understand the interests, preferences, and effectiveness of different sleep visualizations by form factor. In a survey, we showed that wearers were mostly interested in weekly sleep duration, and nightly sleep phase data. Visualizations of this data were generally preferred over purely text-based representations, and the preferred chart type for fitness bands, and smartwatches was often the same. In one in-person pilot study, and two crowdsourced studies, we then tested the effectiveness of the most preferred representations for different tasks, and found that participants performed simple tasks effectively on both form factors but more complex tasks benefited from the larger smartwatch size. Lastly, we reflect on our crowdsourced study methodology for testing the effectiveness of visualizations for wearables. Supplementary material is available at https://osf.io/yz8ar/.
Title: Negative Emotions, Positive Outcomes? Exploring the Communication of Negativity in Serious Data Stories
Authors: family: Lan
given: Xingyu; family: Wu
given: Yanqiu; family: Shi
given: Yang; family: Chen
given: Qing; family: Cao
given: Nan
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: data storytelling, affective design, user experience

Abstract: Recent work has highlighted that emotion is key to the user experience with data stories. However, limited attention has been paid to negative emotions specifically. This work investigates the outcomes of negative emotions in the context of serious data stories and examines how they can be augmented by design methods from the perspectives of both storytellers and viewers. First, we conducted a workshop with 9 data story experts to understand the possible benefits of eliciting negative emotions in serious data stories and 19 potential design methods that contribute to negative emotions. Based on the findings from the workshop, we then conducted a lab study with 35 participants to explore the outcomes of eliciting negative emotions as well as the effectiveness of the design methods. The results indicated that negative emotions mainly facilitated contemplative experiences and long-term memory. Besides, the design methods showed varied effectiveness in augmenting negative emotions and being recalled.
Title: How do you Converse with an Analytical Chatbot? Revisiting Gricean Maxims for Designing Analytical Conversational Behavior
Authors: family: Setlur
given: Vidya; family: Tory
given: Melanie
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: chatbots, ambiguity, visual analysis, refinement., repair, intent

Abstract: Chatbots have garnered interest as conversational interfaces for a variety of tasks. While general design guidelines exist for chatbot interfaces, little work explores analytical chatbots that support conversing with data. We explore Gricean Maxims to help inform the basic design of effective conversational interaction. We also draw inspiration from natural language interfaces for data exploration to support ambiguity and intent handling. We ran Wizard of Oz studies with 30 participants to evaluate user expectations for text and voice chatbot design variants. Results identified preferences for intent interpretation and revealed variations in user expectations based on the interface affordances. We subsequently conducted an exploratory analysis of three analytical chatbot systems (text + chart, voice + chart, voice-only) that implement these preferred design variants. Empirical evidence from a second 30-participant study informs implications specific to data-driven conversation such as interpreting intent, data orientation, and establishing trust through appropriate system responses.
Title: Do You See What You Mean? Using Predictive Visualizations to Reduce Optimism in Duration Estimates
Authors: family: Koval
given: Morgane; family: Jansen
given: Yvonne
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: predictive visualization, Planning fallacy

Abstract: Making time estimates, such as how long a given task might take, frequently leads to inaccurate predictions because of an optimistic bias. Previous attempts to alleviate this bias, including decomposing the task into smaller components and listing potential surprises, have not shown any major improvement. This article builds on the premise that these procedures may have failed because they involve compound probabilities and mixture distributions which are difficult to compute in one’s head. We hypothesize that predictive visualizations of such distributions would facilitate the estimation of task durations. We conducted a crowdsourced study in which 145 participants provided different estimates of overall and sub-task durations and we used these to generate predictive visualizations of the resulting mixture distributions. We compared participants’ initial estimates with their updated ones and found compelling evidence that predictive visualizations encourage less optimistic estimates.
Title: Understanding Privacy Switching Behaviour on Twitter
Authors: family: Kekulluoglu
given: Dilara; family: Vaniea
given: Kami; family: Magdy
given: Walid
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Online Social Networks, Privacy, Privacy Settings, Security, Twitter

Abstract: Changing a Twitter account’s privacy setting between public and protected changes the visibility of past tweets. By inspecting the privacy setting of more than 100K Twitter users over 3 months, we noticed that over 40% of those users changed their privacy setting at least once with around 16% changing it over 5 times. This observation motivated us to explore the reasons why people switch their privacy settings. We studied these switching phenomena quantitatively by comparing the tweeting behaviour of users when public vs protected, and qualitatively using two follow-up surveys (n=100, n=324) to understand potential reasoning behind the observed behaviours. Our quantitative analysis shows that users who switch privacy settings mention others and share hashtags more when their setting is public. Our surveys highlighted that users turn protected to share personal content and regulate boundaries while they turn public to interact with others in ways the protected setting prevents.
Title: “A Reasonable Thing to Ask For”: Towards a Unified Voice in Privacy Collective Action
Authors: family: Wu
given: Yuxi; family: Edwards
given: W. Keith; family: Das
given: Sauvik
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: user privacy, collective action

Abstract: People feel concerned, angry, and powerless when subjected to surveillance, data breaches and other privacy-violating experiences with institutions (PVEIs). Collective action may empower groups of people affected by a PVEI to jointly demand redress, but a necessary first step is for the collective to agree on demands. We designed a sensitizing prototype to explore how to shepherd a collective to generate a unified set of demands for redress in response to a triggering PVEI. We found that collectives can converge on high-priority concerns and demands for redress, and that many of their demands indicated preferences for broad reform. We then gathered a panel of security and privacy experts to react to the collective’s demands. Experts were dismissive, preferring incremental measures that cleanly mapped onto existing legal structures. We argue this misalignment may help uphold the power chasm between data-harvesting institutions and the individuals whose personal data they monetize.