HCII at Virtual CSCW 2021

November 5, 2021
2021 CSCW logo


The ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW) was held virtually October 23 to 27, 2021.


The CSCW conference brings together researchers who study the design and use of technologies that affect groups, organizations, communities, and networks.

Carnegie Mellon University authors contributed to more than a dozen accepted papers this year, including two Best Papers and three Honorable Mention awards.


A list of CSCW papers with HCII contributing authors is available below. Click on the paper title to expand the accordion and view the authors, abstract, link to the paper and presentation video (if available).


Congratulations to all of the authors.


Reflect, not Regret: Understanding Regretful Smartphone Use with App Feature-Level Analysis -- Best Paper + Methods Recognition

Hyunsung Cho, DaEun Choi, Donghwi Kim, Wan Ju Kang, Eun Kyoung Choe, Sung-Ju Lee

Digital intervention tools against problematic smartphone usage help users control their consumption on smartphones, for example, by setting a time limit on an app. However, today's social media apps offer a mix of quasiessential and addictive features in an app (e.g., Instagram has following feeds, recommended feeds, stories, and direct messaging features), which makes it hard to apply a uniform logic for all uses of an app without a nuanced understanding of feature-level usage behaviors. We study when and why people regret using different features of social media apps on smartphones. We examine regretful feature uses in four smartphone social media apps (Facebook, Instagram, YouTube, and KakaoTalk) by utilizing feature usage logs, ESM surveys on regretful use collected for a week, and retrospective interviews from 29 Android users. In determining whether a feature use is regretful, users considered different types of rewards they obtained from using a certain feature (i.e., social, informational, personal interests, and entertainment) as well as alternative rewards they could have gained had they not used the smartphone (e.g., productivity). Depending on the types of rewards and the way rewards are presented to users, probabilities to regret vary across features of the same app. We highlight three patterns of features with different characteristics that lead to regretful use. First, "following"-based features (e.g., Facebook's News Feed and Instagram's Following Posts and Stories) induce habitual checking and quickly deplete rewards from app use. Second, recommendation-based features situated close to actively used features (e.g., Instagram's Suggested Posts adjacent to Search) cause habitual feature tour and sidetracking from the original intention of app use. Third, recommendation-based features with bite-sized contents (e.g., Facebook's Watch Videos) induce using "just a bit more," making people fall into prolonged use. We discuss implications of our findings for how social media apps and intervention tools can be designed to reduce regretful use and how feature-level usage information can strengthen self-reflection and behavior changes.

Paper: https://dl.acm.org/doi/10.1145/3479600

To Reuse or Not To Reuse? A Framework and System for Evaluating Summarized Knowledge -- Best Paper


Michael Xieyang Liu, Aniket Kittur, Brad A. Myers


As the amount of information online continues to grow, a correspondingly important opportunity is for individuals to reuse knowledge which has been summarized by others rather than starting from scratch. However, appropriate reuse requires judging the relevance, trustworthiness, and thoroughness of others' knowledge in relation to an individual's goals and context. In this work, we explore augmenting judgements of the appropriateness of reusing knowledge in the domain of programming, specifically of reusing artifacts that result from other developers' searching and decision making. Through an analysis of prior research on sensemaking and trust, along with new interviews with developers, we synthesized a framework for reuse judgements. The interviews also validated that developers express a desire for help with judging whether to reuse an existing decision. From this framework, we developed a set of techniques for capturing the initial decision maker's behavior and visualizing signals calculated based on the behavior, to facilitate subsequent consumers' reuse decisions, instantiated in a prototype system called Strata. Results of a user study suggest that the system significantly improves the accuracy, depth, and speed of reusing decisions. These results have implications for systems involving user-generated content in which other users need to evaluate the relevance and trustworthiness of that content.

Paper: https://dl.acm.org/doi/10.1145/3449240

Watch presentation video


Algorithmic Folk Theories and Identity: How TikTok Users Co-Produce Knowledge of Identity and Engage in Algorithmic Resistance -- Honorable Mention


Nadia Karizat, Dan Delmonaco, Motahhare Eslami, Nazanin Andalibi


Algorithms in online platforms interact with users' identities in different ways. However, little is known about how users understand the interplay between identity and algorithmic processes on these platforms, and if and how such understandings shape their behavior on these platforms in return. Through semi-structured interviews with 15 US-based TikTok users, we detail users' algorithmic folk theories of the For You Page algorithm in relation to two inter-connected identity types: person and social identity. Participants identified potential harms that can accompany algorithms' tailoring content to their person identities. Further, they believed the algorithm actively suppresses content related to marginalized social identities based on race and ethnicity, body size and physical appearance, ability status, class status, LGBTQ identity, and political and social justice group affiliation. We propose a new algorithmic folk theory of social feeds—The Identity Strainer Theory—to describe when users believe an algorithm filters out and suppresses certain social identities. In developing this theory, we introduce the concept of algorithmic privilege as held by users positioned to benefit from algorithms on the basis of their identities. We further propose the concept of algorithmic representational harm to refer to the harm users experience when they lack algorithmic privilege and are subjected to algorithmic symbolic annihilation. Additionally, we describe how participants changed their behaviors to shape their algorithmic identities to align with how they understood themselves, as well as to resist the suppression of marginalized social identities and lack of algorithmic privilege via individual actions, collective actions, and altering their performances. We theorize our findings to detail the ways the platform's algorithm and its users co-produce knowledge of identity on the platform. We argue the relationship between users’ algorithmic folk theories and identity are consequential for social media platforms, as it impacts users' experiences, behaviors, sense of belonging, and perceived ability to be seen, heard, and feel valued by others as mediated through algorithmic systems.

Paper: https://dl.acm.org/doi/10.1145/3476046 


Join, Stay or Go? A Closer Look at Members’ Life Cycles in Online Health Communities -- Honorable Mention

Zheng Yao, Diyi Yang, John M. Levine (University of Pittsburgh), Carissa A. Low (University of Pittsburgh), Tenbroeck Smith (American Cancer Society, Inc.), Haiyi Zhu, Robert E. Kraut


Online health communities (OHCs) have become important sources where members can obtain social support. Since most benefits of OHCs are provided by OHC members, it is crucial that OHCs maintain a critical mass of active members. This paper examines temporal changes in members' participation in a cancer-oriented OHC, focusing on the changes in members' motivations and behavior as they transition from newcomers to other roles or ultimately leave the community. Our work used mixed methods, combining behavioral log analysis, automated content analysis, surveys and interviews. We found that shifts in members' motivations seemed to be driven by two sources: the internal dynamics common to becoming a member of most online communities and the external needs associated with their cancer journey. When members' disease-driven needs for support decreased, most members quit the site. The motivations of those who stayed shifted from receiving support to providing it to others in the community. As in many online communities, oldtimers contributed the vast majority of content. However, they encountered challenges that threatened their commitment, including negative emotion related to other members' deaths, which led them to take leaves of absence from the community or to drop out permanently. Implications for the motivation changes of OHC members are discussed.

Paper: https://dl.acm.org/doi/10.1145/3449245


More than a Modern Day Green Book: Exploring the Online Community of Black Twitter -- Honorable Mention + D&I Recognition

Shamika Klassen (The University of Colorado at Boulder), Sara Kingsley, Kalyn McCall (Harvard University), Joy Weinberg (The University of Colorado at Boulder), Casey Fiesler (The University of Colorado)


The Negro Motorist Green Book was a tool used by the Black community to navigate systemic racism throughout the U.S. and around the world. Whether providing its users with safer roads to take or businesses that were welcoming to Black patrons, The Negro Motorist Green Book fostered pride and created a physical network of safe spaces within the Black community. Building a bridge between this artifact which served Black people for thirty years and the current moment, we explore Black Twitter as an online space where the Black community navigates identity, activism, racism, and more. Through interviews with people who engage with Black Twitter, we surface the benefits (such as community building, empowerment, and activism) and challenges (like dealing with racism, appropriation, and outsiders) on the platform, juxtaposing the Green Book as a historical artifact and Black Twitter as its contemporary counterpart. Equipped with these insights, we make suggestions including audience segmentation, privacy controls, and involving historically disenfranchised perspectives into the technological design process. These proposals have implications for the design of technologies that would serve Black communities by amplifying Black voices and bolstering work toward justice.



Can Crowds Customize Instructional Materials with Minimal Expert Guidance? Exploring Teacher-guided Crowdsourcing for Improving Hints in an AI-based Tutor


Kexin Bella Yang, Tomohiro Nagashima, Junhui Yao, Joseph Jay Williams (University of Toronto), Kenneth Holstein, Vincent Aleven


AI-based educational technologies may be most welcome in classrooms when they align with teachers’ goals,preferences, and instructional practices. Teachers, however, have scarce time to make such customizations themselves. How might the crowd be leveraged to help time-strapped teachers? Crowdsourcing pipelines have traditionally focused on content generation. It is, however, an open question how a pipeline might be designed so the crowd can succeed in a revision/customization task. In this paper, we explore an initial version of a teacher-guided crowdsourcing pipeline designed to improve the adaptive math hints of an AI-based tutoring system so they fit teachers’ preferences, while requiring minimal expert guidance. In two experiments involving 144 math teachers and 481 crowdworkers, we found that such an expert-guided revision pipeline could save experts’ time and produce better crowd-revised hints (in terms of teacher satisfaction) than two generation conditions. The revised hints however, did not improve on the existing hints in the AI tutor, which were already highly rated, though with room for improvement and customization. Further analysis revealed that the main challenge for crowdworkers may lie in understanding teachers’ brief written comments and implementing them in the form of effective edits, without introducing new problems. We also found that teachers preferred their own revisions over other sources of hints, and exhibited varying preferences over hints in AI-tutor. Overall, the results confirm that there is a clear need for customizing hints to individual teachers preferences, but also highlight the need for more elaborate scaffolds so the crowd has specific knowledge of the requirements that teachers have for hints. The study represents a first exploration in the literature of how to support crowds with minimal expert guidance in revising and customizing instructional materials.

Paper: https://dl.acm.org/doi/10.1145/3449193 


Code of Conduct Conversations in Open Source Software Projects on Github


Renee Li (The University of Texas at Austin), Laura Dabbish, Pavitthra Pandurangan, Hana Frluckaj


The rapid growth of open source software necessitates a deeper understanding of moderation and governance methods used within these projects. The code of conduct, a set of rules articulating standard behavior and responsibilities for participation within a community, is becoming an increasingly common policy document in open source software projects for setting project norms of behavior and discourage negative or harassing comments and conversation. This study describes the conversations around adopting and crafting a code of conduct as well as those surrounding community governance. We conduct a qualitative analysis of a random sample of GitHub issues that involve the code of conduct and identify different categories of surrounding conversation. We find that codes of conduct are used both proactively and reactively to govern community behavior in project issues. Oftentimes, the initial addition of a code of conduct does not involve much community participation and input. However, a controversial moderation act is capable of inciting mass community feedback and backlash. Project maintainers balance the tension between disciplining potentially offensive forms of speech and encouraging broad and inclusive participation. These results have implications for the design of inclusive and effective governance practices for open source software communities.

Paper:  https://dl.acm.org/doi/10.1145/3449093



Discovering and Validating AI Errors With Crowdsourced Failure Reports


Ángel Alexander Cabrera, Abraham J Druck, Jason I. Hong, Adam Perer


AI systems can fail to learn important behaviors, leading to real-world issues like safety concerns and biases. Discovering these systematic failures often requires significant developer attention, from hypothesizing potential edge cases to collecting evidence and validating patterns. To scale and streamline this process, we introduce crowdsourced failure reports, end-user descriptions of how or why a model failed, and show how developers can use them to detect AI errors. We also design and implement Deblinder, a visual analytics system for synthesizing failure reports that developers can use to discover and validate systematic failures. In semi-structured interviews and think-aloud studies with 10 AI practitioners, we explore the affordances of the Deblinder system and the applicability of failure reports in real-world settings. Lastly, we show how collecting additional data from the groups identified by developers can improve model performance.

Paper https://dl.acm.org/doi/10.1145/3479569


Everyday Algorithm Auditing: Understanding the Power of Everyday Users in Surfacing Harmful Algorithmic Behaviors


Hong Shen, Alicia DeVos, Motahhare Eslami, Kenneth Holstein


A growing body of literature has proposed formal approaches to audit algorithmic systems for biased and harmful behaviors. While formal auditing approaches have been greatly impactful, they often suffer major blindspots, with critical issues surfacing only in the context of everyday use once systems are deployed. Recent years have seen many cases in which everyday users of algorithmic systems detect and raise awareness about harmful behaviors that they encounter in the course of their everyday interactions with these systems. However, to date little academic attention has been granted to these bottom-up, user-driven auditing processes. In this paper, we propose and explore the concept of everyday algorithm auditing, a process in which users detect, understand, and interrogate problematic machine behaviors via their day-to-day interactions with algorithmic systems. We argue that everyday users are powerful in surfacing problematic machine behaviors that may elude detection via more centrally-organized forms of auditing, regardless of users’ knowledge about the underlying algorithms. We analyze several real-world cases of everyday algorithm auditing, drawing lessons from these cases for the design of future platforms and tools that facilitate such auditing behaviors. Finally, we discuss work that lies ahead, toward bridging the gaps between formal auditing approaches and the organic auditing behaviors that emerge in everyday use of algorithmic systems.

Paper:  https://dl.acm.org/doi/10.1145/3479577 



“It’s our mutual responsibility to share”: The evolution of account sharing in romantic couples


Junchao Lin, Jason I. Hong, Laura Dabbish




While most online accounts are designed assuming a single user, past work has found that romantic couples often share many accounts. Our study examines couples’ account sharing behaviors as their relationships develop. We conducted 19 semi-structured interviews with people who are currently in romantic relationships to understand couples’ account sharing behaviors over the lifecycle of their relationship. We find that account sharing behaviors progress through a relationship where major changes happen at the start of cohabitation, marriage, and occasional breakup. We also find that sharing behaviors and motivations are influenced by couples’ relationship ecology, which consists of the dynamics between the couples and the social environment they live in. Based on these findings, we discuss implications for further study to support couples’ sharing needs at different relationship stages and identify design opportunities for technology solutions to facilitate couples’ sharing.

Paper: https://dl.acm.org/doi/10.1145/3449234 


Practice-Based Teacher Questioning Strategy Training with ELK: A Role-Playing Simulation for Eliciting Learner Knowledge


Xu Wang (University of Michigan), Meredith Thompson (MIT), Kexin Yang, Dan Roy (MIT), Kenneth R Koedinger, Carolyn Rosé, Justin Reich (MIT)


Practice is essential for learning. However, for many interpersonal skills, there often are not enough opportunities and venues for novices to repeatedly practice. Role-playing simulations offer a promising framework to advance practice-based professional training for complex communication skills, in fields such as teaching. In this work, we introduce ELK (Eliciting Learner Knowledge), a role-playing simulation system that helps K-12 teachers develop effective questioning strategies to elicit learners' prior knowledge. We evaluate ELK with 75 pre-service teachers through a mixed-method study. We find that teachers demonstrate a modest increase in effective questioning strategies and develop sympathy towards students after using ELK for 3 rounds. We implement a supplementary activity in ELK in which users evaluate transcripts generated from past role-play sessions. We demonstrate that evaluating conversation moves is as effective for learning as role-playing, while without requiring the presence of a partner. We contribute design implications for role-play systems for communication strategy training.

Paper: https://dl.acm.org/doi/10.1145/3449125


Scaffolding the Online Peer-support Experience: Novice Supporters' Strategies and Challenges   


Tianying Chen, Kristy Zhang, Robert E Kraut


People with mental distress are increasingly turning to one-to-one synchronous communication websites to receive peer support from other members. Though some research has identified benefits and challenges of online peer-support, there is a limited understanding of how to best prepare and scaffold for untrained peer supporters as they attempt to become skillful in an online setting. We recruited 30 (15 pairs) participants to engage in an online support conversation about procrastination problems, gave one member of each pair minimal training in the principles and strategies of motivational interviewing, and used interviews and conversation transcripts to examine challenges novice helpers faced when providing support and learning new conversational skills. We presented the helpers with two conversation goals to achieve with the conversation: building understanding, and promoting readiness for change. The research identified the common strategies the helpers used to achieve these goals and the challenges they faced. We also discuss theoretical and design implications for platform designers to better scaffold this experience.

Paper: https://dl.acm.org/doi/10.1145/3479510



Together But Alone: Atomization and Peer Support among Gig Workers


Zheng Yao, Sarah Weden (Smith College), Lea Emerlyn, Haiyi Zhu, Robert E Kraut


The individualistic nature of gig work allows workers to have high levels of flexibility, but it also leads to atomization, leaving them isolated from peer workers. In this paper, we employed a qualitative approach to understand how online social media groups provide informational and emotional support to physical gig workers during the COVID-19 pandemic. We found that social media groups alleviate the atomization effect, as workers use these groups to obtain experiential knowledge from their peers, build connections, and organize collective action. However, we noted a reluctance among workers to share strategic information where there was a perceived risk of being competitively disadvantaged. In addition, we found that the diversity among gig workers has also led to limited empathy for one another, which further impedes the provision of emotional support. While social media groups could potentially become places where workers organize collective efforts, several factors, including the uncertainty of other workers' activities and the understanding of the independent contractor status, have diminished the effectiveness of efforts at collective action.

Paper: https://dl.acm.org/doi/10.1145/3479535 



The Unique Challenges for Creative Small Businesses Seeking Feedback on Social Media

Yasmine Kotturi, Allie Blaising (California Polytechnic University), Sarah E Fox, Chinmay Kulkarni


Social media can be an especially effective source of feedback on open-ended work, such as product design. Unlike large businesses with entire teams dedicated to “social”, there is little understanding of how small business owners constrained both in personnel and resources can leverage the benefits of direct, informal communication channels afforded by social media. Through a series of design workshops and interviews with 26 small business owners at a local feminist makerspace, we uncover the unique challenges small business owners experience when seeking feedback on open-ended work via social media. We found participants carefully balanced large-scale access to diverse audiences with attempts to receive reliable feedback, and often targeted audiences narrowly to reinstate participants’ control and trust. While business owners idealized building authentic relationships with their social audience to create collectively, they shared the behind-the-scenes work needed to do so successfully such as navigating blurred personal and business identities, and self-regulation necessary to continuously stay engaged and not internalize discouraging feedback.

Paper: https://dl.acm.org/doi/10.1145/3449089