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How We Write with Crowds

Published:05 January 2021Publication History
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Abstract

Writing is a common task for crowdsourcing researchers exploring complex and creative work. To better understand how we write with crowds, we conducted both a literature review of crowd-writing systems and structured interviews with designers of such systems. We argue that the cognitive process theory of writing described by Flower and Hayes (1981), originally proposed as a theory of how solo writers write, offers a useful analytic lens for examining the design of crowd-writing systems. This lens enabled us to identify system design challenges that are inherent to the process of writing as well as design challenges that are introduced by crowdsourcing. The findings present both similarities and differences between how solo writers write versus how we write with crowds. To conclude, we discuss how the research community might apply and transcend the cognitive process model to identify opportunities for future research in crowd-writing systems.

References

  1. Elena Agapie, Jaime Teevan, and Andrés Monroy-Hernández. 2015. Crowdsourcing in the field: A case study using local crowds for event reporting. In Third AAAI Conference on Human Computation and Crowdsourcing.Google ScholarGoogle ScholarCross RefCross Ref
  2. Ali Alkhatib, Michael S Bernstein, and Margaret Levi. 2017. Examining crowd work and gig work through the historical lens of piecework. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. 4599--4616.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Nancy Allen, Dianne Atkinson, Meg Morgan, Teresa Moore, and Craig Snow. 1987. What experienced collaborators say about collaborative writing. Iowa State Journal of Business and Technical Communication, Vol. 1, 2 (1987), 70--90.Google ScholarGoogle ScholarCross RefCross Ref
  4. Amazon. 2020. Amazon Mechanical Turk. https://web.archive.org/web/20200612183816/https://www.mturk.com/.Google ScholarGoogle Scholar
  5. Daniel W Barowy, Charlie Curtsinger, Emery D Berger, and Andrew McGregor. 2012. Automan: A platform for integrating human-based and digital computation. In Proceedings of the ACM international conference on Object oriented programming systems languages and applications. 639--654.Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Anne Becker. 2006. A review of writing model research based on cognitive processes. Revision: History, theory, and practice (2006), 25--49.Google ScholarGoogle Scholar
  7. Michael S Bernstein, Greg Little, Robert C Miller, Björn Hartmann, Mark S Ackerman, David R Karger, David Crowell, and Katrina Panovich. 2010. Soylent: a word processor with a crowd inside. In Proceedings of the 23nd annual ACM symposium on User interface software and technology. ACM, 313--322.Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Patrick Biernacki and Dan Waldorf. 1981. Snowball sampling: Problems and techniques of chain referral sampling. Sociological methods & research, Vol. 10, 2 (1981), 141--163.Google ScholarGoogle Scholar
  9. Jeffrey P Bigham, Chandrika Jayant, Hanjie Ji, Greg Little, Andrew Miller, Robert C Miller, Robin Miller, Aubrey Tatarowicz, Brandyn White, Samual White, et almbox. 2010. VizWiz: nearly real-time answers to visual questions. In Proceedings of the 23nd annual ACM symposium on User interface software and technology. 333--342.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Minsuk Chang, Léonore V Guillain, Hyeungshik Jung, Vivian M Hare, Juho Kim, and Maneesh Agrawala. 2018. Recipescape: An interactive tool for analyzing cooking instructions at scale. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 1--12.Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Neil Cohn. 2013. Visual narrative structure. Cognitive science, Vol. 37, 3 (2013), 413--452.Google ScholarGoogle Scholar
  12. Justin Cranshaw, Emad Elwany, Todd Newman, Rafal Kocielnik, Bowen Yu, Sandeep Soni, Jaime Teevan, and Andrés Monroy-Hernández. 2017. Calendar. help: Designing a workflow-based scheduling agent with humans in the loop. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. 2382--2393.Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Djellel Eddine Difallah, Michele Catasta, Gianluca Demartini, Panagiotis G Ipeirotis, and Philippe Cudré-Mauroux. 2015. The dynamics of micro-task crowdsourcing: The case of amazon mturk. In Proceedings of the 24th international conference on world wide web. 238--247.Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Steven Dow, Anand Kulkarni, Scott Klemmer, and Björn Hartmann. 2012. Shepherding the crowd yields better work. In Proceedings of the ACM 2012 conference on computer supported cooperative work. ACM, 1013--1022.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Ryan Drapeau, Lydia B Chilton, Jonathan Bragg, and Daniel S Weld. 2016. Microtalk: Using argumentation to improve crowdsourcing accuracy. In Fourth AAAI Conference on Human Computation and Crowdsourcing.Google ScholarGoogle ScholarCross RefCross Ref
  16. Linda Flower and John R Hayes. 1981. A cognitive process theory of writing. College composition and communication, Vol. 32, 4 (1981), 365--387.Google ScholarGoogle ScholarCross RefCross Ref
  17. Snehal Gaikwad, Durim Morina, Rohit Nistala, Megha Agarwal, Alison Cossette, Radhika Bhanu, Saiph Savage, Vishwajeet Narwal, Karan Rajpal, Jeff Regino, et almbox. 2015. Daemo: A self-governed crowdsourcing marketplace. In Adjunct proceedings of the 28th annual ACM symposium on user interface software & technology. 101--102.Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Elena L Glassman, Aaron Lin, Carrie J Cai, and Robert C Miller. 2016. Learnersourcing personalized hints. In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing. ACM, 1626--1636.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Google. 2020. Google Docs. Online. https://web.archive.org/web/20200416081437/https://www.google.com/docs/about/Google ScholarGoogle Scholar
  20. Nick Greer, Jaime Teevan, and Shamsi T Iqbal. 2016. An introduction to technological support for writing. Technical Report. Technical Report. Microsoft Research Tech Report MSR-TR-2016-001.Google ScholarGoogle Scholar
  21. Nathan Hahn, Joseph Chang, Ji Eun Kim, and Aniket Kittur. 2016. The Knowledge Accelerator: Big picture thinking in small pieces. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 2258--2270.Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. David Hicks, Peter E Doolittle, and E Thomas Ewing. 2004. The SCIM-C strategy: Expert historians, historical inquiry, and multimedia. Social Education, Vol. 68, 3 (2004), 221--226.Google ScholarGoogle Scholar
  23. Hwajung Hong, Eric Gilbert, Gregory D Abowd, and Rosa I Arriaga. 2015. In-group questions and out-group answers: crowdsourcing daily living advice for individuals with autism. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. 777--786.Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Jeff Howe. 2019. Crowdsourcing: A Definition. https://web.archive.org/web/20191027010513/https://crowdsourcing.typepad.com/.Google ScholarGoogle Scholar
  25. Chieh-Yang Huang, Shih-Hong Huang, and Ting-Hao Kenneth Huang. 2020. Heteroglossia: In-Situ Story Ideation with the Crowd. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Ting-Hao Huang, Joseph Chee Chang, and Jeffrey P Bigham. 2018. Evorus: A crowd-powered conversational assistant built to automate itself over time. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 1--13.Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Lilly C Irani and M Six Silberman. 2013. Turkopticon: Interrupting worker invisibility in amazon mechanical turk. In Proceedings of the SIGCHI conference on human factors in computing systems. 611--620.Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Youxuan Jiang, Catherine Finegan-Dollak, Jonathan K Kummerfeld, and Walter Lasecki. 2018. Effective crowdsourcing for a new type of summarization task. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). 628--633.Google ScholarGoogle ScholarCross RefCross Ref
  29. Harmanpreet Kaur, Alex C Williams, Anne Loomis Thompson, Walter S Lasecki, Shamsi T Iqbal, and Jaime Teevan. 2018. Creating Better Action Plans for Writing Tasks via Vocabulary-Based Planning. Proceedings of the ACM on Human-Computer Interaction, Vol. 2, CSCW (2018), 86.Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Joy Kim and Andres Monroy-Hernandez. 2016. Storia: Summarizing social media content based on narrative theory using crowdsourcing. In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing. ACM, 1018--1027.Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Joy Kim, Sarah Sterman, Allegra Argent Beal Cohen, and Michael S Bernstein. 2017. Mechanical novel: Crowdsourcing complex work through reflection and revision. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. ACM, 233--245.Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Aniket Kittur, Jeffrey V Nickerson, Michael Bernstein, Elizabeth Gerber, Aaron Shaw, John Zimmerman, Matt Lease, and John Horton. 2013. The future of crowd work. In Proceedings of the 2013 conference on Computer supported cooperative work. 1301--1318.Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Aniket Kittur, Boris Smus, Susheel Khamkar, and Robert E Kraut. 2011. Crowdforge: Crowdsourcing complex work. In Proceedings of the 24th annual ACM symposium on User interface software and technology. ACM, 43--52.Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Nicolas Kokkalis, Thomas Köhn, Carl Pfeiffer, Dima Chornyi, Michael S Bernstein, and Scott R Klemmer. 2013. EmailValet: Managing email overload through private, accountable crowdsourcing. In Proceedings of the 2013 conference on Computer supported cooperative work. 1291--1300.Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Robert Kraut, Jolene Galegher, Robert Fish, and Barbara Chalfonte. 1992. Task requirements and media choice in collaborative writing. Human-Computer Interaction, Vol. 7, 4 (1992), 375--407.Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Walter Lasecki, Christopher Miller, Adam Sadilek, Andrew Abumoussa, Donato Borrello, Raja Kushalnagar, and Jeffrey Bigham. 2012. Real-time captioning by groups of non-experts. In Proceedings of the 25th annual ACM symposium on User interface software and technology. 23--34.Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Walter S Lasecki, Juho Kim, Nick Rafter, Onkur Sen, Jeffrey P Bigham, and Michael S Bernstein. 2015. Apparition: Crowdsourced user interfaces that come to life as you sketch them. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. 1925--1934.Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Walter S Lasecki, Rachel Wesley, Jeffrey Nichols, Anand Kulkarni, James F Allen, and Jeffrey P Bigham. 2013. Chorus: a crowd-powered conversational assistant. In Proceedings of the 26th annual ACM symposium on User interface software and technology. ACM, 151--162.Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Edith Law and Luis von Ahn. 2011. Human computation. Synthesis lectures on artificial intelligence and machine learning, Vol. 5, 3 (2011), 1--121.Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Mary M Lay and William M Karis. 1991. Collaborative writing in industry: Investigations in theory and practice. Baywood Publishing Company.Google ScholarGoogle Scholar
  41. C Michael Levy and Sarah Ransdell. 2013. The science of writing: Theories, methods, individual differences and applications. Routledge.Google ScholarGoogle Scholar
  42. Tianyi Li, Kurt Luther, and Chris North. 2018. CrowdIA: Solving Mysteries with Crowdsourced Sensemaking. Proceedings of the ACM on Human-Computer Interaction, Vol. 2, CSCW (2018), 105.Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Chi-Chin Lin, Yi-Ching Huang, and Jane Yung-jen Hsu. 2014. Crowdsourced explanations for humorous internet memes based on linguistic theories. In Second AAAI Conference on Human Computation and Crowdsourcing.Google ScholarGoogle ScholarCross RefCross Ref
  44. Greg Little, Lydia B Chilton, Max Goldman, and Robert C Miller. 2010. Turkit: human computation algorithms on mechanical turk. In Proceedings of the 23nd annual ACM symposium on User interface software and technology. 57--66.Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Paul Benjamin Lowry, Aaron Curtis, and Michelle René Lowry. 2004. Building a taxonomy and nomenclature of collaborative writing to improve interdisciplinary research and practice. The Journal of Business Communication (1973), Vol. 41, 1 (2004), 66--99.Google ScholarGoogle ScholarCross RefCross Ref
  46. Kurt Luther, Jari-Lee Tolentino, Wei Wu, Amy Pavel, Brian P Bailey, Maneesh Agrawala, Björn Hartmann, and Steven P Dow. 2015. Structuring, aggregating, and evaluating crowdsourced design critique. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing. ACM, 473--485.Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. Narges Mahyar, Michael R James, Michelle M Ng, Reginald A Wu, and Steven P Dow. 2018. CommunityCrit: Inviting the Public to Improve and Evaluate Urban Design Ideas through Micro-Activities. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, 195.Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. Thomas W Malone and Kevin Crowston. 1994. The interdisciplinary study of coordination. ACM Computing Surveys (CSUR), Vol. 26, 1 (1994), 87--119.Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. Brian McInnis, Dan Cosley, Chaebong Nam, and Gilly Leshed. 2016. Taking a HIT: Designing around rejection, mistrust, risk, and workers' experiences in Amazon Mechanical Turk. In Proceedings of the 2016 CHI conference on human factors in computing systems. 2271--2282.Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. Brian McInnis, Gilly Leshed, and Dan Cosley. 2018. Crafting Policy Discussion Prompts as a Task for Newcomers. Proceedings of the ACM on Human-Computer Interaction, Vol. 2, CSCW (2018), 121.Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. Microsoft. 2020. Microsoft Office Online. Online. https://web.archive.org/web/20200415074732/https://products.office.com/en-us/free-office-online-for-the-webGoogle ScholarGoogle Scholar
  52. Michael Nebeling, Alexandra To, Anhong Guo, Adrian A de Freitas, Jaime Teevan, Steven P Dow, and Jeffrey P Bigham. 2016. WearWrite: Crowd-assisted writing from smartwatches. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 3834--3846.Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. Jonas Oppenlaender, Kristy Milland, Aku Visuri, Panos Ipeirotis, and Simo Hosio. 2020. Creativity on Paid Crowdsourcing Platforms. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1--14.Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. Peter Pirolli and Stuart Card. 2005. The sensemaking process and leverage points for analyst technology as identified through cognitive task analysis. In Proceedings of international conference on intelligence analysis, Vol. 5. McLean, VA, USA, 2--4.Google ScholarGoogle Scholar
  55. Jérémie Rappaz, Michele Catasta, Robert West, and Karl Aberer. 2018. Latent structure in collaboration: the case of Reddit R/place. In Twelfth International AAAI Conference on Web and Social Media.Google ScholarGoogle ScholarCross RefCross Ref
  56. Daniela Retelny, Sébastien Robaszkiewicz, Alexandra To, Walter S Lasecki, Jay Patel, Negar Rahmati, Tulsee Doshi, Melissa Valentine, and Michael S Bernstein. 2014. Expert crowdsourcing with flash teams. In Proceedings of the 27th annual ACM symposium on User interface software and technology. 75--85.Google ScholarGoogle ScholarDigital LibraryDigital Library
  57. Niloufar Salehi, Jaime Teevan, Shamsi Iqbal, and Ece Kamar. 2017. Communicating context to the crowd for complex writing tasks. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. ACM, 1890--1901.Google ScholarGoogle ScholarDigital LibraryDigital Library
  58. Matthew J Salganik and Duncan J Watts. 2009. Web-based experiments for the study of collective social dynamics in cultural markets. Topics in cognitive science, Vol. 1, 3 (2009), 439--468.Google ScholarGoogle Scholar
  59. Gordon B Schmidt and William M Jettinghoff. 2016. Using Amazon Mechanical Turk and other compensated crowdsourcing sites. Business Horizons, Vol. 59, 4 (2016), 391--400.Google ScholarGoogle ScholarCross RefCross Ref
  60. Ben Shneiderman. 1996. The eyes have it: A task by data type taxonomy for information visualizations. In Proceedings 1996 IEEE symposium on visual languages. IEEE, 336--343.Google ScholarGoogle ScholarCross RefCross Ref
  61. Anselm L Strauss. 1987. Qualitative analysis for social scientists. Cambridge university press.Google ScholarGoogle Scholar
  62. Saiganesh Swaminathan, Raymond Fok, Fanglin Chen, Ting-Hao Huang, Irene Lin, Rohan Jadvani, Walter S Lasecki, and Jeffrey P Bigham. 2017. Wearmail: On-the-go access to information in your email with a privacy-preserving human computation workflow. In Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology. 807--815.Google ScholarGoogle ScholarDigital LibraryDigital Library
  63. The Etherpad Foundation. 2020. Etherpad. https://web.archive.org/web/20200803185137/https://etherpad.org/.Google ScholarGoogle Scholar
  64. Richard C Thomas. 2012. Long term human-computer interaction: An exploratory perspective. Springer Science & Business Media.Google ScholarGoogle Scholar
  65. Melissa A Valentine, Daniela Retelny, Alexandra To, Negar Rahmati, Tulsee Doshi, and Michael S Bernstein. 2017. Flash organizations: Crowdsourcing complex work by structuring crowds as organizations. In Proceedings of the 2017 CHI conference on human factors in computing systems. 3523--3537.Google ScholarGoogle ScholarDigital LibraryDigital Library
  66. Vasilis Verroios and Michael S Bernstein. 2014. Context trees: Crowdsourcing global understanding from local views. In Second AAAI Conference on Human Computation and Crowdsourcing.Google ScholarGoogle ScholarCross RefCross Ref
  67. Luis Von Ahn and Laura Dabbish. 2004. Labeling images with a computer game. In Proceedings of the SIGCHI conference on Human factors in computing systems. 319--326.Google ScholarGoogle ScholarDigital LibraryDigital Library
  68. Nai-Ching Wang, David Hicks, and Kurt Luther. 2018. Exploring Trade-Offs Between Learning and Productivity in Crowdsourced History. Proceedings of the ACM on Human-Computer Interaction, Vol. 2, CSCW (2018), 178.Google ScholarGoogle ScholarDigital LibraryDigital Library
  69. Mark E Whiting, Dilrukshi Gamage, Snehalkumar (Neil) S Gaikwad, Aaron Gilbee, Shirish Goyal, Alipta Ballav, Dinesh Majeti, Nalin Chhibber, Angela Richmond-Fuller, Freddie Vargus, et almbox. 2017. Crowd guilds: Worker-led reputation and feedback on crowdsourcing platforms. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. 1902--1913.Google ScholarGoogle ScholarDigital LibraryDigital Library
  70. Mark E Whiting, Grant Hugh, and Michael S Bernstein. 2019. Fair Work: Crowd Work Minimum Wage with One Line of Code. In Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, Vol. 7. 197--206.Google ScholarGoogle ScholarCross RefCross Ref
  71. Wikimedia Foundation, Inc. 2020. Wikipedia: The free encyclopedia. Online. https://www.wikipedia.orgGoogle ScholarGoogle Scholar
  72. Wesley Willett, Jeffrey Heer, and Maneesh Agrawala. 2012. Strategies for crowdsourcing social data analysis. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 227--236.Google ScholarGoogle ScholarDigital LibraryDigital Library
  73. Anbang Xu, Huaming Rao, Steven P Dow, and Brian P Bailey. 2015. A classroom study of using crowd feedback in the iterative design process. In Proceedings of the 18th ACM conference on computer supported cooperative work & social computing. ACM, 1637--1648.Google ScholarGoogle ScholarDigital LibraryDigital Library
  74. Amy X Zhang, Lea Verou, and David Karger. 2017. Wikum: Bridging discussion forums and wikis using recursive summarization. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. ACM, 2082--2096.Google ScholarGoogle ScholarDigital LibraryDigital Library
  75. Haiyi Zhu, Steven P Dow, Robert E Kraut, and Aniket Kittur. 2014. Reviewing versus doing: Learning and performance in crowd assessment. In Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing. ACM, 1445--1455.Google ScholarGoogle ScholarDigital LibraryDigital Library

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        cover image Proceedings of the ACM on Human-Computer Interaction
        Proceedings of the ACM on Human-Computer Interaction  Volume 4, Issue CSCW3
        CSCW
        December 2020
        1825 pages
        EISSN:2573-0142
        DOI:10.1145/3446568
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        • Published: 5 January 2021
        Published in pacmhci Volume 4, Issue CSCW3

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