bluuebunny/crossref_metadata_2025 huggingface dataset:
Early Success Prediction of Indian Movies Using Subtitles: A Document Vector Approach
Authors: Rahul Vaddadi Sai , Tejas M. , Prasanth N. Narayanan , Raja S. P.
Published: April 2022

Scientific studies of the elements that influence the box office performance of Indian films have generally concentrated on post-production elements, such as those discovered after a film has been completed or released, and notably for Bollywood films. Only fewer studies have looked at regional film industries and pre-production factors, which are elements that are known before a decision to greenlight a film is made. This study looked at Indian films using natural language processing and machine learning approaches to see if they would be profitable in the pre-production stage. We extract movie data and English subtitles (as an approximation to the screenplay) for the top five Indian regional film industries: Bollywood, Kollywood, Tollywood, Mollywood, and Sandalwood, as they make up a major portion of the Indian film industry’s revenue. Subtitle Vector (Sub2Vec), a Paragraph Vector model trained on English subtitles, was used to embed subtitle text into 50 and 100 dimensions. The proposed approach followed a two-stage pipeline. In the first stage, Return on Investment (ROI) was calculated using aggregated subtitle embeddings and associated movie data. Classification models used the ROI calculated in the first step to predicting a film’s verdict in the second step. The optimal regressor–classifier pair was determined by evaluating classification models using [Formula: see text]-score and Cohen’s Kappa scores on various hyperparameters. When compared to benchmark methods, our proposed methodology forecasts box office success more accurately.
Research on Urban Distribution Optimization Under Point-based Billing on Simulated Annealing with Variable Neighborhood
Authors: Liu Jiabin , Hu Xuanyu , Chen Junyu , Chen Xin , Wen Xupeng
Published: April 2022

In this paper, we study the optimization of urban distribution networks with leased third-party logistics fleets under point-based billing and construct a mathematical model of multi-model vehicle path optimization (MVRPTW) with time windows aiming at the lowest logistics expenditure under point-based billing mode. For the problem that the simulated annealing solution to the VRPTW problem tends to fall into slow convergence, a variable neighborhood structure and adaptive temperature control are introduced to improve the simulated annealing. Finally, using the order data of a Beijing’s third-party logistics company Z, the model is solved by first clustering the customer nodes through the K-Means algorithm to generate the initial solution and then optimizing it with the improved simulated annealing algorithm. Compared with the simulated manual empirical scheduling, algorithmic scheduling can reduce the logistics cost by 88.1%.
Automated retinal layer segmentation in optical coherence tomography images with intraretinal fluid
Authors: Wang Luquan , Li Xiaowen , Chen Yong , Han Dingan , Wang Mingyi , Zeng Yaguang , Zhong Junping , Wang Xuehua , Ji Yanhong , Xiong Honglian , Wei Xunbin
Published: April 2022

We propose a novel retinal layer segmentation method to accurately segment 10 retinal layers in optical coherence tomography (OCT) images with intraretinal fluid. The method used a fan filter to enhance the linear information pertaining to retinal boundaries in an OCT image by reducing the effect of vessel shadows and fluid regions. A random forest classifier was employed to predict the location of the boundaries. Two novel methods of boundary redirection (SR) and similarity correction (SC) were combined to carry out boundary tracking and thereby accurately locate retinal layer boundaries. Experiments were performed on healthy controls and subjects with diabetic macular edema (DME). The proposed method required an average of 415[Formula: see text]s for healthy controls and of 482[Formula: see text]s for subjects with DME and achieved high accuracy for both groups of subjects. The proposed method requires a shorter running time than previous methods and also provides high accuracy. Thus, the proposed method may be a better choice for small training datasets.
Automatic Extraction of Nested Entities in Clinical Referrals in Spanish
Authors: Báez Pablo , Bravo-Marquez Felipe , Dunstan Jocelyn , Rojas Matías , Villena Fabián
Published: April 2022

Here we describe a new clinical corpus rich in nested entities and a series of neural models to identify them. The corpus comprises de-identified referrals from the waiting list in Chilean public hospitals. A subset of 5,000 referrals (58.6% medical and 41.4% dental) was manually annotated with 10 types of entities, six attributes, and pairs of relations with clinical relevance. In total, there are 110,771 annotated tokens. A trained medical doctor or dentist annotated these referrals, and then, together with three other researchers, consolidated each of the annotations. The annotated corpus has 48.17% of entities embedded in other entities or containing another one. We use this corpus to build models for Named Entity Recognition (NER). The best results were achieved using a Multiple Single-entity architecture with clinical word embeddings stacked with character and Flair contextual embeddings. The entity with the best performance is
abbreviation
, and the hardest to recognize is
finding
. NER models applied to this corpus can leverage statistics of diseases and pending procedures. This work constitutes the first annotated corpus using clinical narratives from Chile and one of the few in Spanish. The annotated corpus, clinical word embeddings, annotation guidelines, and neural models are freely released to the community.
iNAP: A Hybrid Approach for NonInvasive Anemia-Polycythemia Detection in the IoMT
Authors: Ghosal Sagnik , Das Debanjan , Udutalapally Venkanna , Wasnik Preetam Narayan
Published: April 2022

The paper presents a novel, self-sufficient, Internet of Medical Things-based model called iNAP to address the shortcomings of anemia and polycythemia detection. The proposed model captures eye and fingernail images using a smartphone camera and automatically extracts the conjunctiva and fingernails as the regions of interest. A novel algorithm extracts the dominant color by analyzing color spectroscopy of the extracted portions and accurately predicts blood hemoglobin level. A less than 11.5 gdL

\( ^{-1} \)

value is categorized as anemia while a greater than 16.5 gdL

\( ^{-1} \)

value as polycythemia. The model incorporates machine learning and image processing techniques allowing easy smartphone implementation. The model predicts blood hemoglobin to an accuracy of

\( \pm \)

0.33 gdL

\( ^{-1} \)

, a bias of 0.2 gdL

\( ^{-1} \)

, and a sensitivity of 90

\( \% \)

compared to clinically tested results on 99 participants. Furthermore, a novel brightness adjustment algorithm is developed, allowing robustness to a wide illumination range and the type of device used. The proposed IoMT framework allows virtual consultations between physicians and patients, as well as provides overall public health information. The model thereby establishes itself as an authentic and acceptable replacement for invasive and clinically-based hemoglobin tests by leveraging the feature of self-anemia and polycythemia diagnosis.
Obesity Prediction with EHR Data: A Deep Learning Approach with Interpretable Elements
Authors: Gupta Mehak , Phan Thao-Ly T. , Bunnell H. Timothy , Beheshti Rahmatollah
Published: April 2022

Childhood obesity is a major public health challenge. Early prediction and identification of the children at an elevated risk of developing childhood obesity may help in engaging earlier and more effective interventions to prevent and manage obesity. Most existing predictive tools for childhood obesity primarily rely on traditional regression-type methods using only a few hand-picked features and without exploiting longitudinal patterns of children’s data. Deep learning methods allow the use of high-dimensional longitudinal datasets. In this article, we present a deep learning model designed for predicting future obesity patterns from generally available items on children’s medical history. To do this, we use a large unaugmented electronic health records dataset from a large pediatric health system in the United States. We adopt a general LSTM network architecture and train our proposed model using both static and dynamic EHR data. To add interpretability, we have additionally included an attention layer to calculate the attention scores for the timestamps and rank features of each timestamp. Our model is used to predict obesity for ages between 3 and 20 years using the data from 1 to 3 years in advance. We compare the performance of our LSTM model with a series of existing studies in the literature and show it outperforms their performance in most age ranges.
Detecting Smartwatch-Based Behavior Change in Response to a Multi-Domain Brain Health Intervention
Authors: Cook Diane J. , Strickland Miranda , Schmitter-Edgecombe Maureen
Published: April 2022

In this study, we introduce and validate a computational method to detect lifestyle change that occurs in response to a multi-domain healthy brain aging intervention. To detect behavior change, digital behavior markers are extracted from smartwatch sensor data and a permutation-based change detection algorithm quantifies the change in marker-based behavior from a pre-intervention, 1-week baseline. To validate the method, we verify that changes are successfully detected from synthetic data with known pattern differences. Next, we employ this method to detect overall behavior change forn= 28 brain health intervention subjects andn= 17 age-matched control subjects. For these individuals, we observe a monotonic increase in behavior change from the baseline week with a slope of 0.7460 for the intervention group and a slope of 0.0230 for the control group. Finally, we utilize a random forest algorithm to perform leave-one-subject-out prediction of intervention versus control subjects based on digital marker delta values. The random forest predicts whether the subject is in the intervention or control group with an accuracy of 0.87. This work has implications for capturing objective, continuous data to inform our understanding of intervention adoption and impact.
"Self-Quaranteens" Process COVID-19: Understanding Information Visualization Language in Memes
Authors: Perovich Laura J. , Alper Meryl , Cleveland Corey
Published: April 2022

The COVID-19 pandemic has led to a surge of information visualizations that aim to increase our scientific understanding and communicate about the ongoing health crisis with the general public. In this time, there has also been significant use of data visualization language in artefacts from online communities that provide commentary on the pandemic and create meaning through participatory digital culture. Using a qualitative approach, this paper examines over 300 memes collected from a public social media group targeted to young adults in the United States that uses the language of data visualization to discuss topics related to COVID-19. We outline four main ways that data visualization language is used in these memes-as a coarse indicator, as a visual analogy, as an opportunity for augmentation with emotion or interpretation, and as a visual pun-as well as two ways that memes leverage traditional and emerging approaches in the information visualization community. We describe the context in which these memes are socially created and interpreted in light of the political nature of online spaces and connect this work to ongoing research on participation, emotion, and embodiment in information visualization. These results aim to start a conversation about the use of data visualization language in digital culture and more casual networked environments beyond official channels.
A "Distance Matters" Paradox: Facilitating Intra-Team Collaboration Can Harm Inter-Team Collaboration
Authors: Hu Xinlan Emily , Hinds Rebecca , Valentine Melissa , Bernstein Michael S.
Published: April 2022

By identifying the socio-technical conditions required for teams to work effectively remotely, the Distance Matters framework has been influential in CSCW since its introduction in 2000. Advances in collaboration technology and practices have since brought teams increasingly closer to achieving these conditions. This paper presents a ten-month ethnography in a remote organization, where we observed that despite exhibiting excellent remote collaboration, teams paradoxically struggled to collaborate across team boundaries. We extend the Distance Matters framework to account for inter-team collaboration, arguing that challenges analogous to those in the original intra-team framework --- common ground, collaboration readiness, collaboration technology readiness, and coupling of work --- persist but are actualized differently at the inter-team scale. Finally, we identify a fundamental tension between the intra- and inter-team layers: the collaboration technology and practices that help individual teams thrive (e.g., adopting customized collaboration software) can also prompt collaboration challenges in the inter-team layer, and conversely the technology and practices that facilitate inter-team collaboration (e.g., strong centralized IT organizations) can harm practices at the intra-team layer. The addition of the inter-team layer to the Distance Matters framework opens new opportunities for CSCW, where balancing the tension between team and organizational collaboration needs will be a critical technological, operational, and organizational challenge for remote work in the coming decades.
An Uncommon Task: Participatory Design in Legal AI
Authors: Delgado Fernando , Barocas Solon , Levy Karen
Published: April 2022

Despite growing calls for participation in AI design, there are to date few empirical studies of what these processes look like and how they can be structured for meaningful engagement with domain experts. In this paper, we examine a notable yet understudied AI design process in the legal domain that took place over a decade ago, the impact of which still informs legal automation efforts today. Specifically, we examine the design and evaluation activities that took place from 2006 to 2011 within the Text REtrieval Conference's (TREC) Legal Track, a computational research venue hosted by the National Institute of Standards and Technologies. The Legal Track of TREC is notable in the history of AI research and practice because it relied on a range of participatory approaches to facilitate the design and evaluation of new computational techniques-in this case, for automating attorney document review for civil litigation matters. Drawing on archival research and interviews with coordinators of the Legal Track of TREC, our analysis reveals how an interactive simulation methodology allowed computer scientists and lawyers to become co-designers and helped bridge the chasm between computational research and real-world, high-stakes litigation practice. In analyzing this case from the recent past, our aim is to empirically ground contemporary critiques of AI development and evaluation and the calls for greater participation as a means to address them.
Empathosphere: Promoting Constructive Communication in Ad-hoc Virtual Teams through Perspective-taking Spaces
Authors: Khadpe Pranav , Kulkarni Chinmay , Kaufman Geoff
Published: April 2022

When members of ad-hoc virtual teams need to collectively ideate or deliberate, they often fail to engage with each others' perspectives in a constructive manner. At best, this leads to sub-optimal outcomes, and, at worst, it can cause conflicts that lead to teams not wanting to continue working together. Prior work has attempted to facilitate constructive communication by highlighting problematic communication patterns and nudging teams to alter their interaction norms. However, these approaches achieve limited success because they fail to acknowledge two social barriers: (1) it is hard to reset team norms mid-interaction, and (2) corrective nudges have limited utility unless team members believe it is safe to voice their opinion and that their opinion will be heard. This paper introduces Empathosphere, a chat-embedded intervention to mitigate these barriers and foster constructive communication in teams. To mitigate the first barrier, Empathosphere leverages the known benefits of "experimental spaces" in dampening existing norms and creating a climate conducive to change. Empathosphere instantiates this "space'' as a separate communication channel in a team's workspace. To mitigate the second barrier, Empathosphere harnesses the benefits of perspective-taking to cultivate a group climate that promotes a norm of members speaking up and engaging with each other. Empathosphere achieves this by orchestrating authentic socio-emotional exchanges designed to induce perspective-taking. A controlled study ($N=110$) compared Empathosphere to an alternate intervention strategy of prompting teams to reflect on their team experience. We found that Empathosphere led to higher work satisfaction, encouraged more open communication and feedback within teams, and boosted teams' desire to continue working together. This work demonstrates that "experimental spaces," particularly those that integrate methods of encouraging perspective-taking, can be a powerful means of improving communication in virtual teams.
How Domain Experts Work with Data: Situating Data Science in the Practices and Settings of Craftwork
Authors: Jung Ju Yeon , Steinberger Tom , King John L. , Ackerman Mark S.
Published: April 2022

Domain experts play an essential role in data science by helping data scientists situate their technical work beyond the statistical analysis of large datasets. How domain experts themselves may engage with data science tools as a type of end-user remains largely invisible. Understanding data science as domain expert-driven depends on understanding how domain experts use data. Drawing on an ethnographic study of a craft brewery in Korea, we show how craft brewers worked with data by situating otherwise abstract data within their brewing practices and settings. We contribute theoretical insight into how domain experts use data distinctly from technical data scientists in terms of their view of data (situated vs. abstract), purposes for engaging with data (guiding processes over predicting outcomes), and overall goals of using data (flexible control vs. precision). We propose four ways in which working with data can be supported through the design of data science tools, and discuss how craftwork can be a useful lens for integrating domain expert-driven understandings of data science into CSCW and HCI research.
No Community Can Do Everything: Why People Participate in Similar Online Communities
Authors: TeBlunthuis Nathan , Kiene Charles , Brown Isabella , Levi Laura (Alia) , McGinnis Nicole , Hill Benjamin Mako
Published: April 2022

Large-scale quantitative analyses have shown that individuals frequently talk to each other about similar things in different online spaces. Why do these overlapping communities exist? We provide an answer grounded in the analysis of 20 interviews with active participants in clusters of highly related subreddits. Within a broad topical area, there are a diversity of benefits an online community can confer. These include (a) specific information and discussion, (b) socialization with similar others, and (c) attention from the largest possible audience. A single community cannot meet all three needs. Our findings suggest that topical areas within an online community platform tend to become populated by groups of specialized communities with diverse sizes, topical boundaries, and rules. Compared with any single community, such systems of overlapping communities are able to provide a greater range of benefits.
Project IRL: Playful Co-Located Interactions with Mobile Augmented Reality
Authors: Dagan Ella , Cárdenas Gasca Ana María , Robinson Ava , Noriega Anwar , Tham Yu Jiang , Vaish Rajan , Monroy-Hernández Andrés
Published: April 2022

We present Project IRL (In Real Life), a suite of five mobile apps we created to explore novel ways of supporting in-person social interactions with augmented reality. In recent years, the tone of public discourse surrounding digital technology has become increasingly critical, and technology's influence on the way people relate to each other has been blamed for making people feel "alone together," diverting their attention from truly engaging with one another when they interact in person. Motivated by this challenge, we focus on an under-explored design space: playful co-located interactions. We evaluated the apps through a deployment study that involved interviews and participant observations with 101 people. We synthesized the results into a series of design guidelines that focus on four themes: (1) device arrangement (e.g., are people sharing one phone, or does each person have their own?), (2) enablers (e.g., should the activity focus on an object, body part, or pet?), (3) affordances of modifying reality (i.e., features of the technology that enhance its potential to encourage various aspects of social interaction), and (4) co-located play (i.e., using technology to make in-person play engaging and inviting). We conclude by presenting our design guidelines for future work on embodied social AR.
"Knowledge Comes Through Participation": Understanding Disability through the Lens of DIY Assistive Technology in Western Kenya
Authors: Hamidi Foad , Owuor Patrick Mbullo , Hynie Michaela , Baljko Melanie
Published: April 2022

People with disabilities in Low- and Middle-Income Countries (LMICs) have limited access to digital assistive technologies (ATs). Most ATs in LMICs are manufactured elsewhere and are expensive and difficult to maintain. Do-It-Yourself Assistive Technologies (DIY-ATs) designed, customized, and repaired by non-technical users offer exciting directions in these contexts. We have been exploring the possibilities and challenges of DIY-ATs in Western Kenya, using community-engaged workshops in rural and urban special education schools for the past three years. We present findings from a concluding-stage research activity: a multiple stakeholder focus group where teachers, disability advocates, and representatives from the local government and technology innovation hubs, discussed the possibilities and challenges of addressing disability issues through DIY-ATs in this context. Participants identified opportunities for DIY-ATs for social inclusion, disability assessment, and inclusive education, and shared concerns about their sustainability, safety, and contextual relevance.
An Exploration of Captioning Practices and Challenges of Individual Content Creators on YouTube for People with Hearing Impairments
Authors: Li Franklin Mingzhe , Lu Cheng , Lu Zhicong , Carrington Patrick , Truong Khai N.
Published: April 2022

Deaf and Hard-of-Hearing (DHH) audiences have long complained about caption qualities for many online videos created by individual content creators on video-sharing platforms (e.g., YouTube). However, there lack explorations of practices, challenges, and perceptions of online video captions from the perspectives of both individual content creators and DHH audiences. In this work, we first explore DHH audiences' feedback on and reactions to YouTube video captions through interviews with 13 DHH individuals, and uncover DHH audiences' experiences, challenges, and perceptions on watching videos created by individual content creators (e.g., manually added caption tags could create additional confidence and trust in caption qualities for DHH audiences). We then discover individual content creators' practices, challenges, and perceptions on captioning their videos (e.g., back-captioning problems) by conducting a YouTube video analysis with 189 captioning-related YouTube videos, followed by a survey with 62 individual content creators. Overall, our findings provide an in-depth understanding of captions generated by individual content creators and bridge the knowledge gap mutually between content creators and DHH audiences on captions.
Attitudes and Folk Theories of Data Subjects on Transparency and Accuracy in Emotion Recognition
Authors: Grill Gabriel , Andalibi Nazanin
Published: April 2022

The growth of technologies promising to infer emotions raises political and ethical concerns, including concerns regarding their accuracy and transparency. A marginalized perspective in these conversations is that of data subjects potentially affected by emotion recognition. Taking social media as one emotion recognition deployment context, we conducted interviews with data subjects (i.e., social media users) to investigate their notions about accuracy and transparency in emotion recognition and interrogate stated attitudes towards these notions and related folk theories. We find that data subjects see accurate inferences as uncomfortable and as threatening their agency, pointing to privacy and ambiguity as desired design principles for social media platforms. While some participants argued that contemporary emotion recognition must be accurate, others raised concerns about possibilities for contesting the technology and called for better transparency. Furthermore, some challenged the technology altogether, highlighting that emotions are complex, relational, performative, and situated. In interpreting our findings, we identify new folk theories about accuracy and meaningful transparency in emotion recognition. Overall, our analysis shows an unsatisfactory status quo for data subjects that is shaped by power imbalances and a lack of reflexivity and democratic deliberation within platform governance.
Disturbing the Peace: Experiencing and Mitigating Emerging Harassment in Social Virtual Reality
Authors: Freeman Guo , Zamanifard Samaneh , Maloney Divine , Acena Dane
Published: April 2022

Harassment has long been considered a severe social issue and a culturally contextualized construct. More recently, understanding and mitigating emerging harassment in social Virtual Reality (VR) has become a growing research area in HCI and CSCW. Based on the perspective of harassment in the U.S. culture, in this paper we identify new characteristics of online harassment in social VR using 30 in-depth interviews. We especially attend to how people who are already considered marginalized in the gaming and virtual worlds contexts (e.g., women, LGBTQ, and ethnic minorities) experience such harassment. As social VR is still a novel technology, our proactive approach highlights embodied harassment as an emerging but understudied form of harassment in novel online social spaces. Our critical review of social VR users' experiences of harassment and recommendations to mitigate such harassment also extends the current conceptualization of online harassment in CSCW. We therefore contribute to the active prevention of future harassment in nuanced online environments, platforms, and experiences.
Eliciting Confidence for Improving Crowdsourced Audio Annotations
Authors: Méndez Méndez Ana Elisa , Cartwright Mark , Bello Juan Pablo , Nov Oded
Published: April 2022

In this work we explore confidence elicitation methods for crowdsourcing "soft" labels, e.g., probability estimates, to reduce the annotation costs for domains with ambiguous data. Machine learning research has shown that such "soft" labels are more informative and can reduce the data requirements when training supervised machine learning models. By reducing the number of required labels, we can reduce the costs of slow annotation processes such as audio annotation. In our experiments we evaluated three confidence elicitation methods: 1) "No Confidence" elicitation, 2) "Simple Confidence" elicitation, and 3) "Betting" mechanism for confidence elicitation, at both individual (i.e., per participant) and aggregate (i.e., crowd) levels. In addition, we evaluated the interaction between confidence elicitation methods, annotation types (binary, probability, and z-score derived probability), and "soft" versus "hard" (i.e., binarized) aggregate labels. Our results show that both confidence elicitation mechanisms result in higher annotation quality than the "No Confidence" mechanism for binary annotations at both participant and recording levels. In addition, when aggregating labels at the recording level, results indicate that we can achieve comparable results to those with 10-participant aggregate annotations using fewer annotators if we aggregate "soft" labels instead of "hard" labels. These results suggest that for binary audio annotation using a confidence elicitation mechanism and aggregating continuous labels we can obtain higher annotation quality, more informative labels, with quality differences more pronounced with fewer participants. Finally, we propose a way of integrating these confidence elicitation methods into a two-stage, multi-label annotation pipeline.
Involving Crowdworkers with Lived Experience in Content-Development for Push-Based Digital Mental Health Tools: Lessons Learned from Crowdsourcing Mental Health Messages
Authors: Kornfield Rachel , Mohr David C. , Ranney Rachel , Lattie Emily G. , Meyerhoff Jonah , Williams Joseph J. , Reddy Madhu
Published: April 2022

Digital tools can support individuals managing mental health concerns, but delivering sufficiently engaging content is challenging. This paper seeks to clarify how individuals with mental health concerns can contribute content to improve push-based mental health messaging tools. We recruited crowdworkers with mental health symptoms to evaluate and revise expert-composed content for an automated messaging tool, and to generate new topics and messages. A second wave of crowdworkers evaluated expert and crowdsourced content. Crowdworkers generated topics for messages that had not been prioritized by experts, including self-care, positive thinking, inspiration, relaxation, and reassurance. Peer evaluators rated messages written by experts and peers similarly. Our findings also suggest the importance of personalization, particularly when content adaptation occurs over time as users interact with example messages. These findings demonstrate the potential of crowdsourcing for generating diverse and engaging content for push-based tools, and suggest the need to support users in meaningful content customization.
Joint Media Engagement in Families Playing Animal Crossing: New Horizons during the COVID-19 Pandemic
Authors: Martinez Jesse J. , Windleharth Travis W. , Li Qisheng , Bhattacharya Arpita , Pearce Katy E. , Yip Jason , Lee Jin Ha
Published: April 2022

The video game Animal Crossing: New Horizons (AC:NH) launched during the COVID-19 pandemic and families turned to it as a game to play together during isolation. This interview study of 27 families considered how families used AC:NH for Joint Media Engagement (JME), where family members engage with media content together, interacting with each other and bringing additional meaning to the experience. We find that the design of AC:NH well facilitates Takeuchi and Stevens's six conditions for productive JME. Furthermore, we identify and discuss additional conditions that contribute to productive JME: variety and flexibility in play styles that amplify mutual engagement, support for disentrained play that enables new forms of "joint" engagement, and scaffolding for affective interactions. This is followed by an exploration of how the COVID-19 pandemic affected JME. We conclude with design implications for building games to support productive JME for families through design for persistent shared spaces, flexible in-game progress, and social life simulation.
Making a Medical Maker's Playbook: An Ethnographic Study of Safety-Critical Collective Design by Makers in Response to COVID-19
Authors: Hofmann Megan , Lakshmi Udaya , Mack Kelly , Arriaga Rosa I. , Hudson Scott E. , Mankoff Jennifer
Published: April 2022

We present an ethnographic study of a maker community that conducted safety-driven medical making to deliver over 80,000 devices for use at medical facilities in response to the COVID-19 pandemic. To achieve this, the community had to balance their clinical value of safety with the maker value of broadened participation in design and production. We analyse their struggles and achievement through the artifacts they produced and the labors of key facilitators between diverse community members. Based on this analysis we provide insights into how medical maker communities, which are necessarily risk-averse and safety-oriented, can still support makers' grassroots efforts to care for their communities. Based on these findings, we recommend that design tools enable adaptation to a wider set of domains, rather than exclusively presenting information relevant to manufacturing. Further, we call for future work on the portability of designs across different types of printers which could enable broader participation in future maker efforts at this scale.
More than just Software Surprises: Purposes, Processes, and Directions for Software Application Easter Eggs
Authors: Lakier Matthew , Vogel Daniel
Published: April 2022

"Easter eggs" are features hidden inside software, and the practice of developers including them is a long-standing global phenomenon. They have seen some investigation in the context of games, but despite their prevalence in non-game software applications, their nature within this context is less clear. We perform a qualitative, investigative analysis of Easter eggs in non-game software application contexts, using primarily archival research including discussion forums, social media posts, and user-created online databases, along with select developer interviews. Our work uncovers the stories behind, motivations for creating, and intended perceptions of Easter eggs, which we present as categories of purposes with illustrative examples. This analysis also informs a categorization and discussion of processes that Easter eggs undergo concerning the social and emotional circumstances of their developers and users. Finally, we use our results to motivate future directions for applying Easter eggs in user interfaces.
Others Are to Blame: Whom People Consider Responsible for Online Misinformation
Authors: Lima Gabriel , Han Jiyoung , Cha Meeyoung
Published: April 2022

Determining who is responsible for online misinformation is an important problem. This research offers a multifaceted view of the public's perception of who is responsible for online misinformation. Via two studies, we surveyed how people attribute responsibility separately for creating, disseminating, and failing to prevent the dissemination of false information online. Study 1 (N=99) employed a mixed-methods approach to identify a series of actors deemed responsible for each aspect of misinformation. Its open-ended methodology suggested that participants tended to externalize responsibility, which we explored further in the subsequent study. Study 2 (N=496) found that the responsible entities differed for the three distinct aspects of misinformation: online users, news media, and interest groups were associated with creating falsehoods, whereas social media platforms were predominantly seen as accountable for failing to prevent them. Our data shows that blame was directed towards those on the opposite side of the political spectrum, indicating substantial polarization. Most critically, people did not seem to associate themselves with online misinformation and externalized responsibility towards "other users." We discuss implications, including the need to promote personal accountability among users and the social demand for accountable social media platforms and news media.
Patients Waiting for Cues: Information Asymmetries and Challenges in Sharing Patient-Generated Data in the Clinic
Authors: Oh Chi Young , Luo Yuhan , St. Jean Beth , Choe Eun Kyoung
Published: April 2022

Patient-generated data (PGD) show great promise for informing the delivery of personalized and patient-centered care. However, patients' data tracking does not automatically lead to data sharing and discussion with clinicians, which can make it difficult to utilize and derive optimal benefit from PGD. In this paper, we investigate whether and how patients share their PGD with clinicians and the types of challenges that arise within this context. We describe patients' immediate experiences of PGD sharing with clinicians, based on our short onsite interviews with 57 patients who had just met with a clinician at a university health center. Our analyses identified overarching patterns in patients' PGD sharing practices and the associated challenges that arise from the information asymmetry between patients and clinicians and from patients' reliance on their memory to share their PGD. We discuss the implications of our findings for designing PGD-integrated health IT systems in ways to support patients' tracking of relevant PGD, clinicians' effective engagement with patients around PGD, and the efficient sharing and review of PGD within clinical settings.
Software-Supported Audits of Decision-Making Systems: Testing Google and Facebook's Political Advertising Policies
Authors: Matias J. Nathan , Hounsel Austin , Feamster Nick
Published: April 2022

How can society understand and hold accountable complex human and algorithmic decision-making systems whose systematic errors are opaque to the public? These systems routinely make decisions on individual rights and well-being, and on protecting society and the democratic process. Practical and statistical constraints on external audits--such as dimensional complexity--can lead researchers and regulators to miss important sources of error in these complex decision-making systems. In this paper, we design and implement a software-supported approach to audit studies that auto-generates audit materials and coordinates volunteer activity. We implemented this software in the case of political advertising policies enacted by Facebook and Google during the 2018 U.S. election. Guided by this software, a team of volunteers posted 477 auto-generated ads and analyzed the companies' actions, finding systematic errors in how companies enforced policies. We find that software can overcome some common constraints of audit studies, within limitations related to sample size and volunteer capacity.
To Use or Abuse: Opportunities and Difficulties in the Use of Multi-channel Support to Reduce Technology Abuse by Adolescents
Authors: Hung Min-Wei , Yuan Chien Wen (Tina) , Bi Nanyi , Chen Yi-Chao , Lee Wan-Chen , Huang Ming-Chyi , You Chuang-Wen
Published: April 2022

Technology abuse among adolescents refers to the problematic use of technology devices, and the negative impact it can have on lifestyle and one's physical and mental health. This paper reports on in-depth interviews with 15 dyads of adolescent patients, their parents, and four experts with the objective of unraveling the issue of technology abuse. We conducted qualitative analysis aimed at unpacking the contextual factors affecting technology abuse, and differences between adolescents and their parents pertaining to this issue. Our discussions led us to formulate solutions to technology abuse: (1) motivating adolescents by sending timely reminders and providing interactive micro-incentives; (2) promoting communication between adolescents and their parents by sharing usage data related to device usage; and (3) incorporating social supports to complement parental support, while fulfilling the adolescent's social needs. This paper provides valuable insights into the design of technological solutions aimed at mediating technology abuse.
Ubiquitous News Experienced Alone: Interviews with Americans and their Devices
Authors: Lottridge Danielle , Quehl Katie , Bentley Frank , Silverman Max , Ong Melissa , Dickard Michael , White Brooke , Plaut Ethan
Published: April 2022

Scholars, journalists, and other commentators argue that many parts of the world, including the US, are suffering a social-epistemological crisis, sometimes called "post-truth," and that this crisis is related to the fragmentation of newsmedia. The conventional media effects research explanation for this relationship between news and "post-truth" is framed in terms of messages and information-especially misinformation-as the mechanism by which communication effects change. Analysing the results of a large (n = 164) interview study on mobile and other digital news audiencehood, this article presents an alternative, complementary explanation focused on ritual functions of communication. The primary method of affinity analysis of interview data identified a number of recurring themes: people's preferences, methods, and patterns of news consumption exhibit wild diversity beyond easy summary, but they share the experience of news as nearly ubiquitous, and often as excessive and therefore in need of management. Strategies for managing news broadly fell into categories of news avoidance and active research. Perhaps the most consistent observation across participants' accounts, however, is a conspicuous absence of other people, with news managed and confronted alone. Those findings are interpreted through James Carey's ritual theory of communication, which argues that meaning emerges not only through transmission of messages and information, but also through people's shared experiences of participating in the ritual processes of communication. This work makes a parallel argument that media fragmentation has been implicated in social-epistemological breakdown not only through the mechanism of messages and (mis)information, but also through the transformation and, in certain cases, loss of shared news rituals. The combination of large-scale interview research with media ritual analysis led to these insights about the cultural relevance and collective implications of people's experiences of ubiquitous news and the avoidance thereof.
Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities
Authors: Wang Qiaosi , Camacho Ida , Jing Shan , Goel Ashok K.
Published: April 2022

Our online interactions are constantly mediated through Artificial Intelligence (AI), especially our social interactions. AI-mediated social interaction is the AI-facilitated process of building and maintaining social connections between individuals through information inferred from people's online posts. With its impending application across a number of contexts, the challenges and opportunities of AI-mediated social interaction remain underexplored. This paper seeks to understand the design space of AI-mediated social interaction in the context of online learning, where students frequently face social isolation. We deployed an AI agent named SAMI in three class discussion forums to help online learners build social connections. Using SAMI as a probe, we conducted semi-structured interviews with 26 students to understand their difficulties in remote social interactions and their experiences with SAMI. Through the lenses of social translucence and social-technical gap, we illustrate online learners' difficulties in remote social interactions and how SAMI resolved some of the difficulties. We also identify potential ethical and social challenges of SAMI such as user agency and privacy. Based on our findings, we outline the design space of AI-mediated social interaction. We discuss the design tension between AI performance and ethical design and pinpoint two design opportunities for AI-mediated social interaction in designing towards human-AI collaborative social matching and artificial serendipity.
Understanding the Role of Context in Creating Enjoyable Co-Located Interactions
Authors: Liu Szu-Yu (Cyn) , Smith Brian A. , Vaish Rajan , Monroy-Hernández Andrés
Published: April 2022

In recent years, public discourse has blamed digital technologies for making people feel "alone together,'' distracting us from engaging with one another, even when we are interacting in-person. We argue that in order to design technologies that foster and augment co-located interactions, we need to first understand the context in which enjoyable co-located socialization takes place. We address this gap by surveying and interviewing over 1,000 U.S.-based participants to understand what, where, with whom, how, and why people enjoy spending time in-person. Our findings suggest that people enjoy engaging in everyday activities with individuals with whom they have strong social ties because it helps enable nonverbal cues, facilitate spontaneity, support authenticity, encourage undivided attention, and leverage the physicality of their bodies and the environment. We conclude by providing a set of recommendations for designers interested in creating co-located technologies that encourage social engagement and relationship building.
User Experience Research in the Work Context: Maps, Gaps and Agenda
Authors: Simsek Caglar Pinar , Roto Virpi , Vainio Teija
Published: April 2022

User experience (UX) research has been criticized for focusing on leisure contexts and overlooking the work contexts. Moreover, researchers have been drawing attention to how UX at work differs from UX in leisure contexts, requiring development of domain specific knowledge. Inspired by these discussions, our motivation is to see the current state of UX at work research. Based on the systematic review of 52 papers, our results revealed that UX at work research is still immature. It is run by dichotomies; the conceptualizations such as the definition and the dimensions of UX at work are ambiguous; the variety of the studied work domains are limited and do not contribute to the understanding of UX at work; and the consideration of contextual factors are arbitrary and coincidental. To advance this important field of research, we indicate the research gaps and suggest a research agenda introducing areas for future research.
Veteran Critical Theory as a Lens to Understand Veterans' Needs and Support on Social Media
Authors: Zhou Jiawei , Saha Koustuv , Lopez Carron Irene Michelle , Yoo Dong Whi , Deeter Catherine R. , De Choudhury Munmun , Arriaga Rosa I.
Published: April 2022

Veterans are a unique marginalized group facing multiple vulnerabilities. Current assessments of veteran needs and support largely come from first-person accounts guided by researchers' prompts. Social media platforms not only enable veterans to connect with each other, but also to self-disclose experiences and seek support. This paper addresses the gap in our understanding of veteran needs and their own support dynamics by examining self-initiated and ecologically-valid self-expressions. In particular, we adopt the Veteran Critical Theory (VCT) to conduct a computational study on the Reddit community of veterans. Using topic modeling, we find veteran-friendly gestures with good intentions might not be appreciated in the subreddit. By employing transfer learning methodologies, we find this community has more informational and emotional support behaviors than general online communities and a higher prevalence of informational support than emotional support. Lastly, an examination of support dynamics reveals some contrasts to previous scholarship in military culture and social media. We discover that positive language and author platform tenure have negative relations with posts receiving replies and replies getting votes, and that replies reflecting personal disclosures tend to get more votes. Through the lens of VCT, we discuss how online communities can help uncover veterans' needs and provide more effective social support.