Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/2957276.2957280acmconferencesArticle/Chapter ViewAbstractPublication PagesgroupConference Proceedingsconference-collections
research-article

Machine Learning and Grounded Theory Method: Convergence, Divergence, and Combination

Published: 13 November 2016 Publication History

Abstract

Grounded Theory Method (GTM) and Machine Learning (ML) are often considered to be quite different. In this note, we explore unexpected convergences between these methods. We propose new research directions that can further clarify the relationships between these methods, and that can use those relationships to strengthen our ability to describe our phenomena and develop stronger hybrid theories.

References

[1]
Chee Siang Ang, Ania Bobrowicz, Diane J. Schiano, and Bonnie Nardi (2013). Data in the wild: Some reflections. Interactions 20(2), 39--43.
[2]
Ofer Arazy, Henry Brausen, David Turner, Adam Balila, Eleni Stroulia, and Joel Lanir (2015). coDNA: Visualizing peer production processes. CSCW 2015 Companion, 5--8.
[3]
Ohad Barzilay, Orit Hazzan, and Amiram Yehudai (2009). Characterizing example embedding as a software activity. Proc. SUITE 2009, 5--8.
[4]
Eric P.S. Baumer, Elisha Elovic, Ying "Crystal" Qin, Francesca Polletta, and Geri K. Gay (2015). Testing and comparing computational approaches for identifying the language of framing in political news. Proc NAACL 2015, 1472--1482.
[5]
Eric P. S. Baumer, Shion Guha, Emily Quan, David Mimno, and Geri K. Gay (2015). How social media non-use influences the likelihood of reversion: Perceived addiction, boundary negotiation, subjective mood, and social connections. Social Media + Society 1(2),.
[6]
Eric P.S. Baumer, David Mimno, Shion Guha, Emily Quan, and Geri Gay (2016). Comparing grounded theory and topicmodeling: Extreme divergence or unlikely convergence? JASIST, in revision.
[7]
Christopher M Bishop (2006). Pattern Recognition and Machine Learning, Springer, NY, NY, USA.
[8]
Cameron Blevins (2010). Topic modeling Martha Ballard's diary. Cameron Blevins (blog), 1 April 2010, http://www.cameronblevins.org/posts/topic-modelingmartha-ballards-diary/ .
[9]
Jeanette Blomberg and Helena Karasti (2013). Reflections on 25 years of ethnography in CSCW. CSCW 22(4--6), 373--423.
[10]
danah boyd and Kate Crawford (2012). Critical questions for big data. Info. Comm. & Soc. 15(5), 662--679.
[11]
Anthony Bryant and Kathy Charmaz (2007). The Sage handbook of grounded theory. London, UK, Sage.
[12]
Dallas Card,Amber E. Boydstun, Justin H. Gross, Philip Resnik, and Noah A. Smith (2015). The media frames corpus: Annotations of frames across issues. Proc ACL 2015, 438--444.
[13]
Munmun De Choudhury and Scott Counts (2013). Understanding affect in the workplace via social media. Proc. CSCW 2013, 303--316.
[14]
Munmum De Choudhury, Scott Counts, Eric J. Horvitz, and Aaron Hoff (2014). Characterizing and predicting postpartum depression from shared facebook data. Proc. CSCW 2014, 626--638
[15]
Kathy Charmaz (2006). Constructing grounded theory: A practical guide through qualitative analysis. London, UK, Sage.
[16]
Ólavur Christiansen (2008). The rationale for the use of classic GT. Grounded Theory Review 7(2), http://groundedtheoryreview.com/2008/06/30/1046/
[17]
Juliet Corbin and Anselm L. Strauss (2007). Basics of quailtative research: Techniques and procedures for developing grounded theory. 3rd edition. Newbury Park, CA, USA: Sage.
[18]
Cristian Danescu-Niculescu-Mizil, Lilian Lee, Bo Pang, and Jon Kleinberg, J. (2012). Echoes of power: Language effects and power differences in social interaction. Proc WWW 2012, 699--708.
[19]
Janez Demšar. (2006). "Statistical comparisons of classifiers over multiple data sets." The Journal of Machine Learning Research 7,1--30.
[20]
James Dougherty, Ron Kohavi, and Mehran Sahami (1995). Supervised and unsupervised discretization of continuous features. Proc. 12th Int. Conf. Machine Learning, 194--202.
[21]
Paul Dourish (2014). Reading and interpretation ethnography. In Judith S. Olson and Wendy A. Kellogg (eds.), Ways of knowing in HCI. New York, NY, USA, Springer.
[22]
Jennifer G. Dy and Carla E. Brodley. (2004). Feature selection for unsupervised learning. J. Mach. Learn. Res. 5 (December 2004), 845--889.
[23]
Danyel Fisher, Rob DeLine, Mary Czerwinski, and Steven Drucker (2012). Interactions with big data analytics. Interactions 19(3), 50--59.
[24]
Barney G. Glaser (1992). Emergences vs. forcing: Basics of grounded theory analysis. Mill Valley, CA, USA: Sociology Press.
[25]
Barney G. Glaser (2005). The grounded theory perspective III: Theoretical coding. Mill Valley, CA, USA: Sociology Press.
[26]
Barney G. Glaser (1978). Theoretical sensitivity: Advances in the methodology of grounded theory. Mill Valley, CA, USA: Sociology Press.
[27]
Barney G. Glaser and Anselm L. Strauss (1965a). Awareness of dying. Chicago, Aldine.
[28]
Barney G. Glaser and Anselm L. Strauss (1967). The discovery of grounded theory: Strategies for qualitative research. Chicago, Aldine.
[29]
Barney G. Glaser and Anselm L. Strauss (1965b). Discovery of substantive theory: A basic strategy for qualitative analysis. Am. Beh. Sci. 8, 5--12.
[30]
Barney G. Glaser and Anselm L. Strauss (1968). Time for dying. Chicago, Aldine.
[31]
Stephan Greene and Philip Resnik (2009). More than words: Syntactic packaging and implicit sentiment. Proc HLT 2009, 503--511.
[32]
Isabelle Guyon and André Elisseeff. (2003). An introduction to variable and feature selection. J. Mach. Learn. Res. 3 (March 2003), 1157--1182
[33]
Rashina Hoda (2011). Self-organizing agile teams: A grounded theory. PhD thesis, Victoria University of Wellington, 2011.
[34]
Matthew Jockers. 2013. Macroanalysis. University of Illinois Press: Champaign, IL.
[35]
Udo Kelle (2005). "Emergence" vs. "forcing" of empirical data? A crucial problem of "grounded theory" reconsidered. FQS 6(2), art. 27.
[36]
Ron Kohavi. (1995) "A study of cross-validation and bootstrap for accuracy estimation and model selection." IJCAI 14(2).
[37]
Ron Kohavi, (1998). Glossary of terms, "Special issue on applications of machine learning and the knowledge discovery process." http://robotics.stanford.edu/~ronnyk/glossary.htm
[38]
Scott Krig (2014). Computer vision metrics: Survey, taxonomy, and analysis. Apress, Chapter 7.
[39]
Karen S. Kurasaki (2000). Intercoder reliability for validating conclusions drawn from open-ended interview data. Field methods, 12(3), 179--194.
[40]
Amanda Menking and Ingrid Erickson (2015). The heart work of Wikipedia: Gendered, emotional labor in the world's largest online encyclopedia. Proc. CHI 2015, 207--210.
[41]
Tom Mitchell, 2006, "The discipline of machine learning". URL: http://wwwcgi.cs.cmu.edu/~tom/pubs/MachineLearningTR.pdf
[42]
Tanushree Mitra and Eric Gilbert (2014). The language that gets people to give: Phrases that predict success on Kickstarter. Proc. CSCW 2014, 49--61.
[43]
Janice M. Morse, Phyllis Noerager Stern, Juliet Corbin, Barbara Bowers, Adele E. Clarke, and Kathy Charmaz (2009). Developing grounded theory: The second generation. Walnut Creek, CA, USA, Left Coast Press.
[44]
Michael Muller (2014). Curiosity, creativity, and surprise as analytic tools: Grounded theory method. In Judith S. Olson and Wendy A. Kellogg (eds.), Ways of knowing in HCI. New York, NY, USA, Springer.
[45]
Michael Muller, Shion Guha, Matthew Davis, Werner Geyer, and Sadat Shami (2015). Developing data-driven theories via grounded theory method and machine learning. Presentation at Human Computer Interaction Consortium, Watsonville, CA, USA, June 2015. Slides available at http://www.slideshare.net/traincroft/hcic-muller-guha-davisgeyer-shami-2015-0629
[46]
Wanda J. Orlikowski and Jack J. Baroudi. 1991. Studying Information Technology in Organizations: Research Approaches and Assumptions. Information Systems Research 2(1), 1--28. http://doi.org/10.1287/isre.2.1.1
[47]
Judith S. Olson and Wendy A. Kellogg (2014) (eds.), Ways of knowing in HCI. New York, NY, USA, Springer.
[48]
Jo Reichertz (2007). Abduction: The logic of discovery of grounded theory. In Anthony Bryant and Kathy Charmaz (eds.), The Sage handbook of grounded theory. Thousand Oaks, CA, USA: Sage.
[49]
Lisa Rhody. 2012. Topic modeling and figurative language. Journal of Digital Humanities 2, 1.
[50]
Shilad Sen, Margaret E. Giesel, Rebecca Gold, Benjamin Hillmann, Matt Lesicko, Samuel Naden, Jesse Russell, Zixiao "Ken" Wang, and Brent Hecht. (2015). Turkers, Scholars, "Arafat" and "Peace": Cultural Communities and Algorithmic Gold Standards. Proc. CSCW, 826--838.
[51]
Rion Snow, Brendan O'Connor, Daniel Jurafsky, and Andrew Y. Ng (2008). Cheap and fast--but is it good?: evaluating non-expert annotations for natural language tasks. Proc. Conf. Empir. Meth. Nat. Lang. Proc., 254--263.
[52]
Susan Leigh Star (2007). Living grounded theory. In Anthony Bryant and Kathy Charmaz (eds.), The Sage handbook of grounded theory. Thousand Oaks, CA, USA: Sage.
[53]
Stephen V Stehman. (1997). "Selecting and interpreting measures of thematic classification accuracy." Remote sensing of Environment 62.1,77--89.
[54]
Anselm L. Strauss (1987). Qualitative analysis for social scientists. Cambridge, UK: Cambridge University Press.
[55]
Anselm L. Strauss and Juliet Corbin (1990). Basics of qualitative research: Grounded theory procedures and techniques. Newbury Park, CA, USA: Sage.
[56]
Jennifer Thom-Santelli, Michael Muller, and David Millen (2008). Social tagging roles: Publishers, evangelists, leaders. Proc CHI 2008, 1041--1044.
[57]
Stefan Timmermans and Iddo Tavory (2012). Theory construction in qualitative research: From grounded theory to abductive analysis. Socio. Theor. 30(3), 167--186.
[58]
Christopher Vendome, Mario Linares-Vásquez, Gabriele Bavota, Massimiliano Di Penta, Daniel German, and Denys Poshyvanyk (2015). License usage and changes: A largescale study of Java projects on GitHub. Proc. IEEE Int. Conf. Prog. Comp. 2015, 218--228.
[59]
Kiri L. Wagstaff, K. (2012). Machine learning that matters. arXiv preprint arXiv:1206.4656, or http://www.wkiri.com/ research/ papers/wagstaff-MLmatters-12.pdf
[60]
Philip A. Warrick, Emily F. Hamilton, Robert E. Kearney, and Doina Precup (2012). A machine learning approach to the detection of fetal hypoxia during labor and delivery. AI Magazine, 33(2), 79--90.
[61]
Janyce M. Wiebe, Rebecca F. Bruce, and Thomas P. O'Hara. 1999. Development and use of a gold-standard data set for subjectivity classifications. In Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics (ACL '99). Association for Computational Linguistics, Stroudsburg, PA, USA, 246--253.

Cited By

View all
  • (2025)"My Very Subjective Human Interpretation": Domain Expert Perspectives on Navigating the Text Analysis Loop for Topic ModelsProceedings of the ACM on Human-Computer Interaction10.1145/37012019:1(1-30)Online publication date: 10-Jan-2025
  • (2025)Community Support for Aging in Place: A Computational Thematic Analysis of Discussions about Informal Care on RedditProceedings of the ACM on Human-Computer Interaction10.1145/37011879:1(1-25)Online publication date: 10-Jan-2025
  • (2024)Beyond Predictive Algorithms in Child WelfareProceedings of the 50th Graphics Interface Conference10.1145/3670947.3670976(1-13)Online publication date: 3-Jun-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
GROUP '16: Proceedings of the 2016 ACM International Conference on Supporting Group Work
November 2016
534 pages
ISBN:9781450342766
DOI:10.1145/2957276
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 November 2016

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. axial coding
  2. coding families
  3. grounded theory
  4. machine learning
  5. supervised learning
  6. unsupervised learning

Qualifiers

  • Research-article

Conference

GROUP '16
Sponsor:
GROUP '16: 2016 ACM Conference on Supporting Groupwork
November 13 - 16, 2016
Florida, Sanibel Island, USA

Acceptance Rates

GROUP '16 Paper Acceptance Rate 36 of 111 submissions, 32%;
Overall Acceptance Rate 125 of 405 submissions, 31%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)127
  • Downloads (Last 6 weeks)11
Reflects downloads up to 13 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2025)"My Very Subjective Human Interpretation": Domain Expert Perspectives on Navigating the Text Analysis Loop for Topic ModelsProceedings of the ACM on Human-Computer Interaction10.1145/37012019:1(1-30)Online publication date: 10-Jan-2025
  • (2025)Community Support for Aging in Place: A Computational Thematic Analysis of Discussions about Informal Care on RedditProceedings of the ACM on Human-Computer Interaction10.1145/37011879:1(1-25)Online publication date: 10-Jan-2025
  • (2024)Beyond Predictive Algorithms in Child WelfareProceedings of the 50th Graphics Interface Conference10.1145/3670947.3670976(1-13)Online publication date: 3-Jun-2024
  • (2024)"Is Long-distance Hiking an Emotional Roller Coaster?" Evaluating Emotions and Weather Effects on the Appalachian TrailExtended Abstracts of the CHI Conference on Human Factors in Computing Systems10.1145/3613905.3651024(1-8)Online publication date: 11-May-2024
  • (2024)Concept Induction: Analyzing Unstructured Text with High-Level Concepts Using LLooMProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642830(1-28)Online publication date: 11-May-2024
  • (2024)CollabCoder: A Lower-barrier, Rigorous Workflow for Inductive Collaborative Qualitative Analysis with Large Language ModelsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642002(1-29)Online publication date: 11-May-2024
  • (2024)Offenheit, Zugänglichkeit und Teilhabe an digitaler Medienforschung: das Beispiel teil-automatisierte Inhaltsanalyse in sozialen MedienHandbuch Digitale Medien und Methoden10.1007/978-3-658-36629-2_7-1(1-20)Online publication date: 20-Jan-2024
  • (2024)Enhancing Semantic Understanding by Bridging Topic Modeling and Thematic Analysis: An Empirical Study on Self-Help Twitter Corpus and In-Depth InterviewsDigital Humanities Looking at the World10.1007/978-3-031-48941-9_5(53-71)Online publication date: 20-Apr-2024
  • (2023)Evaluating the Coverage and Depth of Latent Dirichlet Allocation Topic Model in Comparison with Human Coding of Qualitative Data: The Case of Education ResearchMachine Learning and Knowledge Extraction10.3390/make50200295:2(473-490)Online publication date: 14-May-2023
  • (2023)Technology-Mediated Strategies for Coping with Mental Health Challenges: Insights from People with Bipolar DisorderProceedings of the ACM on Human-Computer Interaction10.1145/36100317:CSCW2(1-31)Online publication date: 4-Oct-2023
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media