CSCW TRACK GUIDLEINES FOR PAPER AUTHORS AND REVIEWERS (CSCW 2027 & BEYOND)

This track welcomes papers whose primary contribution is conceptual, theoretical, or design-oriented, rather than focusing on a new technical system or an empirical study. Submissions should advance how the CSCW community understands, conceptualizes, or approaches the design of sociotechnical systems that support collaboration, coordination, and social interaction. Possible theoretical contributions include proposing new models, synthesizing fragmented areas into coherent typologies, extending or challenging existing CSCW frameworks - such as practice theory, infrastructure, articulation work, or critical approaches - and offering normative theories that address power, justice, and ethics. Design-theoretic contributions should connect design to theory by developing frameworks, strategies, or speculative repertoires that open new research directions, or by articulating reusable principles, heuristics, and patterns. Papers may offer conceptual and methodological contributions by introducing new analytical frameworks or reflecting on how methods like ethnography, action research, or research-through-design generate theory.

Design and Theory papers may or may not include new empirical data. When new data is available, the empirical material should be used to develop, illustrate, or test the conceptual argument, not as the main contribution. When new data is not available, authors should build on existing empirical literature, using well-documented cases to demonstrate the value of their concepts or framework. In both cases, the grounding of the contribution should be rigorous and clearly connected to the conceptual claims.

Example papers:

Henrik Korsgaard, Peter Lyle, Joanna Saad-Sulonen, Clemens Nylandsted Klokmose, Midas Nouwens, and Susanne Bødker. 2022. Collectives and Their Artifact Ecologies. Proc. ACM Hum.-Comput. Interact. 6, CSCW2, Article 432 (November 2022), 26 pages. https://doi.org/10.1145/3555533

Stine S. Johansen, Claire Brophy, Markus Rittenbruch, and Jared W. Donovan. 2024. Characterising CSCW Research on Human-Robot Collaboration. Proc. ACM Hum.-Comput. Interact. 8, CSCW1, Article 160 (April 2024), 31 pages. https://doi.org/10.1145/3640999

Leah Hope Ajmani, Jasmine C. Foriest, Jordan Taylor, Kyle Pittman, Sarah Gilbert, and Michael Ann DeVito. 2024. Whose Knowledge is Valued? Epistemic Injustice in CSCW Applications. Proc. ACM Hum.-Comput. Interact. 8, CSCW2, Article 523 (November 2024), 28 pages. https://doi.org/10.1145/3687062

This track welcomes papers that integrate multiple methods to study CSCW phenomena. Contributions may combine qualitative, quantitative, design, or systems approaches in ways that complement and strengthen one another. Some submissions may balance methods equally, while others may rely primarily on one method with a supporting secondary method. Contributions that combine methods from multiple tracks (e.g. system plus survey) should typically submit to the Mixed Methods track. Contributions that combine methods from a single track (e.g. qualitative interviews plus focus groups) should typically submit to that single track.

Submissions may be balanced, drawing equally from two or more complementary methods, or submissions may be primarily qualitative with a small quantitative component, or vice versa. Submissions should follow the standards and expectations of the primary method guiding the paper’s contribution. Supporting methods may play a more limited role, such as triangulating findings, contextualizing results, generating hypotheses, validating interpretations, or extending claims.

Example papers:

Sun, Lily, Lillian Mok, Shilad Sen, and Behzad Sarrafzadeh. "Rhythm of Work: Mixed-Methods Characterization of Information Workers Scheduling Preferences and Practices." Proceedings of the ACM on Human-Computer Interaction 8, no. CSCW2 (2024): 1–38.

Leavitt, Alex, and John J. Robinson. "Upvote My News: The Practices of Peer Information Aggregation for Breaking News on Reddit.com." Proceedings of the ACM on Human-Computer Interaction 1, no. CSCW (2017): 1–18.

Chen, Zhicong, Xiaofei Lan, Jiaxin Piao, Yongqi Zhang, and Yuhan Li. "A Mixed-Methods Analysis of the Algorithm-Mediated Labor of Online Food Deliverers in China." Proceedings of the ACM on Human-Computer Interaction 6, no. CSCW2 (2022): 1–24.

This track welcomes papers that rely primarily on qualitative methods such as interviews, observations, focus groups, ethnographic methods, discourse analysis, etc., where the contribution furthers our empirical, theoretical, and/or practical understanding of collaborative human behavior with and through technologies. Qualitative submissions need not have design implications but should speak to broader phenomena of interest, as conceptual abstraction and transferability of ideas are hallmarks of qualitative research rather than generalizability. The method of qualitative inquiry should be well justified and explained with respect to the research questions and claims. Auto-ethnographic work will be evaluated based on the standards of rigorous ethnographic research and should not be used to represent reflective accounts that are more appropriate for experience reports. Similarly, first-person research methods (e.g., autobiographical design) may be more appropriate for the Design and Theory track.

There are a number of common misconceptions about sample size and the pursuit of objectivity in qualitative research. Be careful in particular about:

1. Sample Size: Best practice for sample size is that researchers should collect data until saturation is reached. The number of interviews needed is contextual. Researchers not familiar with qualitative methods may sometimes incorrectly call a sample too small, using intuitions that come from other methods. Watch out for this error.
2. Inter-rater reliability: Calculating inter-rater reliability is often not needed or appropriate for interpretive qualitative coding and analysis. For more details, see this paper.

Example papers:

Samir Passi and Steven J. Jackson. 2018. Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects. Proc. ACM Hum.-Comput. Interact. 2, CSCW, Article 136 (November 2018), 28 pages.

Nervo Verdezoto, Naveen Bagalkot, Syeda Zainab Akbar, Swati Sharma, Nicola Mackintosh, Deirdre Harrington, and Paula Griffiths. 2021. The Invisible Work of Maintenance in Community Health: Challenges and Opportunities for Digital Health to Support Frontline Health Workers in Karnataka, South India. Proc. ACM Hum.-Comput. Interact. 5, CSCW1, Article 91 (April 2021), 31 pages.

This track welcomes papers that study sociotechnical phenomena with quantitative methods: examples may include “big data,” quantitative user studies, experimental methods such as RCTs, statistical methods, etc. Often, quantitative CSCW papers aim to characterize sociotechnical phenomena broadly (e.g., behavior at platform-level scale) and/or within relatively tight operational bounds (e.g., precise timing information, linguistic analysis, etc.). As with qualitative research, quantitative work need not have design implications, but should speak to broad phenomena of interest. Often, but not always, generalizability is of interest (within the bounds afforded by the design of the study).

Example papers:

Mathur, Arunesh, Gunes Acar, Michael J. Friedman, Eli Lucherini, Jonathan Mayer, Marshini Chetty, and Arvind Narayanan. "Dark patterns at scale: Findings from a crawl of 11K shopping websites." Proceedings of the ACM on human-computer interaction 3, no. CSCW (2019): 1-32.

Chandrasekharan, E., Pavalanathan, U., Srinivasan, A., Glynn, A., Eisenstein, J., & Gilbert, E. (2017). “You can't stay here: The efficacy of reddit's 2015 ban examined through hate speech.” Proceedings of the ACM on human-computer interaction, 1(CSCW), 1-22.

This track welcomes papers contributing novel social computing technologies, including software, hardware, architectures, infrastructures, algorithms, toolkits, and interaction designs built for collaborative experiences. This scope encompasses exploring how emerging technologies in machine learning, artificial intelligence, robotics, and augmented or virtual reality influence users, groups, and society. While the primary contribution is technical and typically includes a research prototype, these papers often provide empirical insights into human impact or focus heavily on how the system alters interaction. System contributions can take various forms, ranging from the rare achievement of novel functionality via novel techniques, to novel functionality via known techniques, known functionality via novel techniques, known functionality via known techniques, or visionary research sketches. Technical novelty is frequently balanced by the empirical insights gained through user evaluations. However, if the technical novelty is exceptionally high, user evaluations may be minimal or omitted entirely if substituted by a rigorous technical evaluation supporting the paper's claims. Authors and reviewers are encouraged to consider that contributions can take different forms, as outlined by the UIST community.

Example papers:

Putz, Florentin, Steffen Haesler, and Matthias Hollick. "Sounds Good? Fast and Secure Contact Exchange in Groups." Proceedings of the ACM on Human-Computer Interaction 8, no. CSCW2 (2024): 1-44.

Han, Catherine, Anne Li, Deepak Kumar, and Zakir Durumeric. PressProtect: Helping Journalists Navigate Social Media in the Face of Online Harassment." Proceedings of the ACM on Human-Computer Interaction 8, no. CSCW2 (2024): 1-34.

Lam, Michelle S., Ayush Pandit, Colin H. Kalicki, Rachit Gupta, Poonam Sahoo, and Danaë Metaxa. Sociotechnical audits: Broadening the algorithm auditing lens to investigate targeted advertising." Proceedings of the ACM on Human-Computer Interaction 7, no. CSCW2 (2023): 1-37.

Deng, Wesley Hanwen, Wang Claire, Howard Ziyu Han, Jason I. Hong, Kenneth Holstein, and Motahhare Eslami. "Weaudit: Scaffolding user auditors and ai practitioners in auditing generative ai." Proceedings of the ACM on Human-Computer Interaction 9, no. 7 (2025): 1-35.