From calculations to reasoning: history, trends, and the potential of Computational Ethnography and Computational Social Anthropology

Peponakis, Manolis, Kapidakis, Sarantos, Doerr, Martin and Tountasaki, Eirini From calculations to reasoning: history, trends, and the potential of Computational Ethnography and Computational Social Anthropology. Social Science Computer Review, 2023. [Journal article (Paginated)]

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English abstract

The domains of computational social anthropology and computational ethnography refer to the computational processing or computational modelling of data for anthropological or ethnographic research. In this context, the article surveys the use of computational methods regarding the production and the representation of knowledge. The ultimate goal of the study is to highlight the significance of modelling ethnographic data and anthropological knowledge by harnessing the potential of the semantic web. The first objective was to review the use of computational methods in anthropological research focusing on the last 25 years, while the second objective was to explore the potential of the semantic web focusing on existing technologies for ontological representation. For these purposes, the study explores the use of computers in anthropology regarding data processing and data modelling for more effective data processing. The survey reveals that there is an ongoing transition from the instrumentalisation of computers as tools for calculations, to the implementation of information science methodologies for analysis, deduction, knowledge representation, and reasoning, as part of the research process in social anthropology. Finally, it is highlighted that the ecosystem of the semantic web does not subserve quantification and metrics but introduces a new conceptualisation for addressing and meeting research questions in anthropology.

Item type: Journal article (Paginated)
Keywords: computational anthropology; computational ethnography; ethnographic data; semantic web; knowledge representation; data modelling; ontology
Subjects: A. Theoretical and general aspects of libraries and information. > AC. Relationship of LIS with other fields .
L. Information technology and library technology
Depositing user: Manolis Peponakis
Date deposited: 27 Apr 2023 11:21
Last modified: 27 Apr 2023 11:21
URI: http://hdl.handle.net/10760/44288

References

Abramson, C. M., & Dohan, D. (2015). Beyond Text: Using Arrays to Represent and Analyze Ethnographic Data. Sociological Methodology, 45(1), 272–319. https://doi.org/10.1177/0081175015578740

Abramson, C. M., Joslyn, J., Rendle, K. A., Garrett, S. B., & Dohan, D. (2018). The promises of computational ethnography: Improving transparency, replicability, and validity for realist approaches to ethnographic analysis. Ethnography, 19(2), 254–284. https://doi.org/10.1177/1466138117725340

Albris, K., Otto, E. I., Astrupgaard, S. L., Gregersen, E. M., Jørgensen, L. S., Jørgensen, O., Sandbye, C. R., & Schønning, S. (2021). A view from anthropology: Should anthropologists fear the data machines? Big Data & Society, 8(2), 20539517211043656. https://doi.org/10.1177/20539517211043655

Alvard, M., & Carlson, D. (2020). Identifying Patch Types Using Movement Data from Artisanal Fishers from the Commonwealth of Dominica. Current Anthropology, 61(3), 380–387. https://doi.org/10.1086/708720

Anticoli, L., & Toppano, E. (2011a). How culture may influence ontology co-design: A qualitative study. International Journal of Information Technology and Web Engineering, 6(2), 1–17. https://doi.org/10.4018/jitwe.2011040101

Anticoli, L., & Toppano, E. (2011b). The role of culture in collaborative ontology design. Proceedings of the 2011 International Conference on Intelligent Semantic Web-Services and Applications, 4:1-4:9. https://doi.org/10.1145/1980822.1980826

Arnold, T., & Fuller, H. J. A. (2018). In Search of the User’s Language: Natural Language Processing, Computational Ethnography, and Error-Tolerant Interface Design. Advances in Usability, User Experience and Assistive Technology, 36–43. https://doi.org/10.1007/978-3-319-94947-5_4

Artmann, S. (2010). Computers and Anthropology. In J. H. Birx (Ed.), 21st century anthropology: A reference handbook (pp. 915–924). Sage.

Asuncion, C. H., & van Sinderen, M. J. (2010). Pragmatic Interoperability: A Systematic Review of Published Definitions. In P. Bernus, G. Doumeingts, & M. Fox (Eds.), Enterprise Architecture, Integration and Interoperability (pp. 164–175). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-15509-3_15

Ayimdji, A., Koussoubé, S., Fotso, L. P., & Konfé, B. O. (2011). Towards a “Deep” Ontology for African Traditional Medicine. Intelligent Information Management, 03, 244–251. https://doi.org/10.4236/iim.2011.36030

Bakharia, A., & Corrin, L. (2019). Using Recent Advances in Contextual Word Embeddings to Improve the Quantitative Ethnography Workflow. In B. Eagan, M. Misfeldt, & A. Siebert-Evenstone (Eds.), Advances in Quantitative Ethnography (pp. 299–306). Springer International Publishing.

Bartlett, A., Lewis, J., Reyes-Galindo, L., & Stephens, N. (2018). The locus of legitimate interpretation in Big Data sciences: Lessons for computational social science from -omic biology and high-energy physics. Big Data & Society, 5(1), 2053951718768831. https://doi.org/10.1177/2053951718768831

Beaulieu, A. (2017). Vectors for Fieldwork: Computational Thinking and New Modes of Ethnography. In L. Hjorth, H. A. Horst, A. Galloway, & G. Bell (Eds.), The Routledge companion to digital ethnography (pp. 29–39). Routledge, Taylor & Francis Group. https://www.routledge.com/The-Routledge-Companion-to-Digital-Ethnography-1st-Edition/Hjorth-Horst-Galloway-Bell/p/book/9781138940918

Behrens, C. A. (1990). Qualitative and quantitative approaches to the analysis of anthropological data: A new synthesis. Journal of Quantitative Anthropology, 2(1), 305–328.

Behrens, C. A., & Read, D. W. (1993). Anthropology: Moving from task-driven to science-driven computing. Social Science Computer Review, 11(4), 429–451.

Beuving, J. J. (2020). Ethnography’s future in the big data era. Information, Communication & Society, 23(11), 1625–1639. https://doi.org/10.1080/1369118X.2019.1602664

Bharwani, S. (2006). Understanding Complex Behavior and Decision Making Using Ethnographic Knowledge Elicitation Tools (KnETs). Social Science Computer Review, 24(1), 78–105. https://doi.org/10.1177/0894439305282346

Brooker, P. (2022). Computational ethnography: A view from sociology. Big Data & Society, 9(1), 1–6. https://doi.org/10.1177/20539517211069892

Burton, M. L. (1973). Recent computer applications in cultural anthropology. Computers and the Humanities, 7(6), 337–341. https://doi.org/10.1007/BF02395108

Carlsen, H. B., & Ralund, S. (2022). Computational grounded theory revisited: From computer-led to computer-assisted text analysis. Big Data & Society, 9(1), 1–16. https://doi.org/10.1177/20539517221080146

Carlson, S., & Anderson, B. (2007). What Are Data? The Many Kinds of Data and Their Implications for Data Re-Use. Journal of Computer-Mediated Communication, 12(2), 635–651. https://doi.org/10.1111/j.1083-6101.2007.00342.x

Chantas, G., Karavarsamis, S., Nikolopoulos, S., & Kompatsiaris, I. (2018). A Probabilistic, Ontological Framework for Safeguarding the Intangible Cultural Heritage. Journal on Computing and Cultural Heritage, 11(3), 12:1-12:29. https://doi.org/10.1145/3131610

Chi, Y.-L., Sung, H.-Y., & Lien, Y.-Y. (2020). Towards the Ethnic Understanding of Taiwanese Indigenous Peoples: A Mashup Based on Semantic Web and Open Data. In P.-L. P. Rau (Ed.), Cross-Cultural Design. User Experience of Products, Services, and Intelligent Environments (pp. 287–297). Springer International Publishing. https://doi.org/10.1007/978-3-030-49788-0_21

Chui, C., Grüninger, M., & Wong, J. (2020). An Ontology for Formal Models of Kinship. Formal Ontology in Information Systems, 92–106. https://doi.org/10.3233/FAIA200663

Cioffi-Revilla, C. (2016). Bigger Computational Social Science: Data, Theories, Models, and Simulations -- Not Just Big Data (SSRN Scholarly Paper ID 2784278; pp. 1–5). Social Science Research Network. http://ssrn.com/abstract=2784278

Combi, M. (1992). The imaginary, the computer, artificial intelligence: A cultural anthropological approach. AI & Society, 6(1), 41–49. https://doi.org/10.1007/BF02472768

Doerr, M., & Iorizzo, D. (2008). The dream of a global knowledge network: A new approach. Journal on Computing and Cultural Heritage, 1(1), 5:1-5:23. https://doi.org/10.1145/1367080.1367085

Fauconnier, G., & Turner, M. (2003). The Way We Think: Conceptual Blending and the Mind’s Hidden Complexities. Basic Books.

Fischer, M. (2004). Integrating Anthropological Approaches to the Study of Culture: The “hard” and the “soft.” Cybernetics and Systems, 35(2–3), 147–162. https://doi.org/10.1080/01969720490426830

Fischer, M. D. (1994). Applications in Computing for Social Anthropologists (edition published in the Taylor&Francis e-Library, 2005). Routledge.

Fischer, M. D. (2006). Introduction: Configuring Anthropology. Social Science Computer Review, 24(1), 3–14. https://doi.org/10.1177/0894439305282575

Fischer, M. D., & Ember, C. (2018). Big Data and Research Opportunities Using HRAF Databases. In C. Shu-Heng (Ed.), Big Data in Computational Social Science and Humanities. Springer. http://dx.doi.org/10.1007/978-3-319-95465-3_17

Fischer, M. D., & Finkelstein, A. (1991). Social knowledge representation: A case study. In N. Fielding & R. Lee (Eds.), Using computers in qualitative research (pp. 119–135). SAGE.

Fischer, M. D., Lyon, S. M., Sosna, D., & Henig, D. (2013). Harmonizing Diversity Tuning Anthropological Research to Complexity. Social Science Computer Review, 31(1), 3–15. https://doi.org/10.1177/0894439312455311

Fortun, M., Fortun, K., & Marcus, G. E. (2017). Computers in/and anthropology: The poetics and politics of digitization. In L. Hjorth, H. A. Horst, A. Galloway, & G. Bell (Eds.), The Routledge companion to digital ethnography (pp. 11–20). Routledge, Taylor & Francis Group. https://www.routledge.com/The-Routledge-Companion-to-Digital-Ethnography-1st-Edition/Hjorth-Horst-Galloway-Bell/p/book/9781138940918

Fuentes, A., & Wiessner, P. (2016). Reintegrating Anthropology: From Inside Out: An Introduction to Supplement 13. Current Anthropology, 57(S13), S3–S12. https://doi.org/10.1086/685694

Geertz, C. (1973). The Interpretation of Cultures: Selected Essays. Basic Books.

Gruber, T. R. (1995). Toward principles for the design of ontologies used for knowledge sharing? International Journal of Human-Computer Studies, 43(5–6), 907–928. https://doi.org/10.1006/ijhc.1995.1081

Gruber, T. R. (2007). Ontology of folksonomy: A mash-up of apples and oranges. International Journal on Semantic Web and Information Systems, 3(1), 1–11.

Hakken, D. (1993). Computing and Social Change: New Technology and Workplace Transformation, 1980-1990. Annual Review of Anthropology, 22, 107–132.

Haron, H., & Hamiz, M. (2014a). An Ontological Framework to Preserve Malay Indigenous Health Knowledge. Advanced Science Letters, 20(1), 226–230. https://doi.org/10.1166/asl.2014.5261

Haron, H., & Hamiz, M. (2014b). An Ontological Model for Indigenous Knowledge of Malay Confinement Dietary. Journal of Software, 9(5), 1302–1312. https://doi.org/10.4304/jsw.9.5.1302-1312

Hjorth, L., Horst, H. A., Galloway, A., & Bell, G. (Eds.). (2017). The Routledge companion to digital ethnography. Routledge, Taylor & Francis Group.

Knox, H., & Nafus, D. (Eds.). (2018). Ethnography for a data-saturated world. Manchester University Press.

Kohne, J. (2014). Ontology, Its Origins and Its Meaning in Information Science. In R. Hagengruber & U. Riss (Eds.), Philosophy, computing and information science (pp. 85–89). Pickering & Chatto.

Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., Jebara, T., King, G., Macy, M., Roy, D., & Alstyne, M. V. (2009). Computational Social Science. Science, 323(5915), 721–723. https://doi.org/10.1126/science.1167742

Leetaru, K. H., Perkins, T., & Rewerts, C. (2014). Cultural Computing at Literature Scale: Encoding the Cultural Knowledge of Tens of Billions of Words of Academic Literature. D-Lib Magazine, 20(9/10). https://doi.org/10.1045/september2014-leetaru

L’Homme, M.-C., & Bernier-Colborne, G. (2012). Terms as labels for concepts, terms as lexical units: A comparative analysis in ontologies and specialized dictionaries. Applied Ontology, 7(4), 387–400. https://doi.org/10.3233/AO-2012-0116

Lyon, S. M. (2013). Networks and Kinship Formal Models of Alliance, Descent, and Inheritance in a Pakistani Punjabi Village. Social Science Computer Review, 31(1), 45–55. https://doi.org/10.1177/0894439312453275

Lyon, S. M., & Magliveras, S. S. (2006). Kinship, Computing, and Anthropology. Social Science Computer Review, 24(1), 30–42. https://doi.org/10.1177/0894439305281494

Marcus, G. E. (1998). The once and future ethnographic archive. History of the Human Sciences, 11(4), 49–63. https://doi.org/10.1177/095269519801100404

Michel, J.-B., Shen, Y. K., Aiden, A. P., Veres, A., Gray, M. K., Pickett, J. P., Hoiberg, D., Clancy, D., Norvig, P., Orwant, J., Pinker, S., Nowak, M. A., & Aiden, E. L. (2011). Quantitative Analysis of Culture Using Millions of Digitized Books. Science, 331(6014), 176–182. https://doi.org/10.1126/science.1199644

Moretti, F. (2013). Distant Reading. Verso Books.

Mueller, A. (2016). Beyond ethnographic scriptocentrism: Modelling multi-scalar processes, networks, and relationships. Anthropological Theory, 16(1), 98–130. https://doi.org/10.1177/1463499615626621

Munk, A. K., & Winthereik, B. R. (2022). Computational Ethnography: A Case of COVID-19’s Methodological Consequences. In M. H. Bruun, A. Wahlberg, R. Douglas-Jones, C. Hasse, K. Hoeyer, D. B. Kristensen, & B. R. Winthereik (Eds.), The Palgrave Handbook of the Anthropology of Technology (pp. 201–214). Springer. https://doi.org/10.1007/978-981-16-7084-8_10

Nelson, L. K. (2017). Computational Grounded Theory: A Methodological Framework. Sociological Methods & Research. https://doi.org/10.1177/0049124117729703

Paff, S. (2022). Anthropology by Data Science. Annals of Anthropological Practice, 46(1), 7–18. https://doi.org/10.1111/napa.12169

Papakyriakopoulos, O., & Mboya, A. M. (2022). Beyond Algorithmic Bias: A Socio-Computational Interrogation of the Google Search by Image Algorithm. Social Science Computer Review, 08944393211073169. https://doi.org/10.1177/08944393211073169

Pels, P., Boog, I., Florusbosch, J. H., Kripe, Z., Minter, T., Postma, M., Sleeboom‐Faulkner, M., Simpson, B., Dilger, H., Schönhuth, M., Poser, A. von, Castillo, R. C. A., Lederman, R., & Richards‐Rissetto, H. (2018). Data management in anthropology: The next phase in ethics governance? Social Anthropology, 26(3), 391–413. https://doi.org/10.1111/1469-8676.12526

Phefo, O. S. D., Kefitiley, N., & Hlomani, H. (2015). Towards the Cultural Knowledge Ontology. 2015 IEEE International Conference on Information Reuse and Integration (IRI), 526–533. https://doi.org/10.1109/IRI.2015.85

Pokraev, S., Reichert, M., Steen, M. W. A., & Wieringa, R. J. (2005). Semantic and pragmatic interoperability: A model for understanding. 160. Scopus. http://ceur-ws.org/Vol-160/paper21.pdf

Pool, R. (2017). The verification of ethnographic data. Ethnography, 18(3), 281–286. https://doi.org/10.1177/1466138117723936

Purzycki, B. G., & Jamieson-Lane, A. (2017). AnthroTools: An R Package for Cross-Cultural Ethnographic Data Analysis. Cross-Cultural Research, 51(1), 51–74. https://doi.org/10.1177/1069397116680352

Read, D., Fischer, M., & Leaf, M. (2013). What Are Kinship Terminologies, and Why Do We Care? A Computational Approach to Analyzing Symbolic Domains. Social Science Computer Review, 31(1), 16–44. https://doi.org/10.1177/0894439312455914

Read, D. W. (2006). Kinship Algebra Expert System (KAES) A Software Implementation of a Cultural Theory. Social Science Computer Review, 24(1), 43–67. https://doi.org/10.1177/0894439305282372

Read, D. W., & Behrens, C. A. (1990). KAES: An expert system for the algebraic analysis of kinship terminologies. Journal of Quantitative Anthropology, 2(4), 353–393.

Ribes, D., & Bowker, G. C. (2009). Between meaning and machine: Learning to represent the knowledge of communities. Information and Organization, 19(4), 199–217. https://doi.org/10.1016/j.infoandorg.2009.04.001

Romney, A. K., Weller, S. C., & Batchelder, W. H. (1986). Culture as Consensus: A Theory of Culture and Informant Accuracy. American Anthropologist, 88(2), 313–338. https://doi.org/10.1525/aa.1986.88.2.02a00020

Sahlins, M. (2002). Waiting for Foucault, Still. Prickly Paradigm Press.

Seaver, N. (2017). Algorithms as culture: Some tactics for the ethnography of algorithmic systems. Big Data & Society, 4(2), 1–12. https://doi.org/10.1177/2053951717738104

Seo, J., Moon, J., Choi, G. W., & Do, J. (2022). A Scoping Review of Three Computational Approaches to Ethnographic Research in Digital Learning Environments. TechTrends, 66(1), 102–111. https://doi.org/10.1007/s11528-021-00689-3

Sosna, D., Galeta, P., Šmejda, L., Sladek, V., & Bruzek, J. (2013). Burials and Graphs Relational Approach to Mortuary Analysis. Social Science Computer Review, 31(1), 56–70. https://doi.org/10.1177/0894439312453277

Stubbersfield, J., & Tehrani, J. (2013). Expect the Unexpected? Testing for Minimally Counterintuitive (MCI) Bias in the Transmission of Contemporary Legends A Computational Phylogenetic Approach. Social Science Computer Review, 31(1), 90–102. https://doi.org/10.1177/0894439312453567

Tallyn, E., Fried, H., Gianni, R., Isard, A., & Speed, C. (2018). The Ethnobot: Gathering Ethnographies in the Age of IoT. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 604:1-604:13. https://doi.org/10.1145/3173574.3174178

TEDx Talks (Director). (2011, July 14). A picture is worth 500 billion words: Erez Lieberman Aiden and Jean-Baptiste Michel at TEDxBoston. https://youtu.be/WtJ50v7qByE

Totaro, P., & Ninno, D. (2014). The Concept of Algorithm as an Interpretative Key of Modern Rationality. Theory, Culture & Society, 31(4), 29–49. https://doi.org/10.1177/0263276413510051

Van Der Leeuw, S. E. (2004). Why Model? Cybernetics and Systems, 35(2–3), 117–128. https://doi.org/10.1080/01969720490426803

Wagner, R. A. (1989). The Rise of Computing in Anthropology: Hammers and Nails. Social Science Computer Review, 7(4), 418–430. https://doi.org/10.1177/089443938900700403

Walsh, D., & Downe, S. (2005). Meta-synthesis method for qualitative research: A literature review. Journal of Advanced Nursing, 50(2), 204–211. https://doi.org/10.1111/j.1365-2648.2005.03380.x

Weingart, S., & Jorgensen, J. (2013). Computational analysis of the body in European fairy tales. Literary and Linguistic Computing, 28(3), 404–416. https://doi.org/10.1093/llc/fqs015

Wilf, E. (2013). Toward an Anthropology of Computer-Mediated, Algorithmic Forms of Sociality. Current Anthropology, 54(6), 716–739. https://doi.org/10.1086/673321

Wing, J. M. (2006). Computational Thinking. Communications of the ACM, 49(3), 33–35. https://doi.org/10.1145/1118178.1118215

Zheng, K., Hanauer, D. A., Weibel, N., & Agha, Z. (2015). Computational Ethnography: Automated and Unobtrusive Means for Collecting Data In Situ for Human–Computer Interaction Evaluation Studies. In V. Patel, T. Kannampallil, & D. Kaufman (Eds.), Cognitive Informatics for Biomedicine (pp. 111–140). Springer, Cham. https://doi.org/10.1007/978-3-319-17272-9_6


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