This page is about Daniel S. Griffin’s research direction.
See also:
My goal in research is to understand how people imagine, use, and make web search so that we might be better able to advocate for the appropriate role and shape of search in our work, lives, and society.1
I do multi-sited (Marcus, 1995) ethnographically-informed qualitative research. I conduct semi-structured interpretative in-depth interviews (Pugh, 2013)—“treating interviews as fieldwork” (Seaver, 2017, p. 8)—and document analysis (Prior, 2003). I draw on tactics from those who study algorithms and who see the performance of the algorithms as involving much more than the code itself (Bucher, 2017, Christin, 2017, Introna, 2016).
I write code to build out potential entry points (Burrell, 2009) and rapport (Lareau, 2021), to draw things together (Latour, 1990), to support my scavenging for documents and data from APIs and websites (Seaver, 2017), and to scaffold my reimagining (Benjamin, 2019, Dunne & Raby, 2013).
I am focused on the sociotechnical construction of search. I treat web search engines and web searching as sociotechnical constructions—explicitly recognizing these are constructed and experienced differently by different people. I look at the articulations and imaginaries that people develop, share, and adopt for the use of web search (and related search tools) and how that interacts with the perceived constraints and affordances of both design and policy. This research engages with values (such as access, autonomy, fairness, privacy, and responsibility) and conceptions of knowing and doing (such as skills, knowledge, and expertise, and technology).
I note the disruption from Large Language Models (LLMs) and their use in generative search (like You.com or Perplexity AI) and search-like systems (like OpenAI’s ChatGPT or Google’s Bard) as having helped or forced many to denaturalize web search and think that there may be a way to search outside the dominant approach of one firm.
Three questions frame my research, they look at:
This question drives an empirical strategy of exploring work involved in successful uses of web search in order to expand our understanding of search and people’s experience with it. This approach is inspired by the call for “desire-based research” from Tuck (2009), as set in contrast to “damage-centered research”. A significant body of research on the design and use of web search look for, identify, and advance our understanding of search harms and risks (Gillespie, 2017, Golebiewski & boyd, 2019, Haider & Sundin, 2019, Introna & Nissenbaum, 2000, Noble, 2018, Tripodi, 2022, Vaidhyanathan, 2011, Van Couvering, 2007). This question suggests a different tack. I look to see, as Latour & Woolgar (2013) ask, how order, or search success, is “constructed out of chaos” (p. 33) in situated practices. Success for the purposes of this question may be incomplete and partial, but I follow Vertesi (2019) to look at “users’ situated accomplishments” (p. 371) to extend our understandings of how people experience and perform web search. Success here leverages “practical knowledge” (Cotter, 2022) and may include maintenance and repair (Jackson, 2014), refusal (Cifor et al., 2019) and resistance (Christin, 2017, Nagel, 2018, Velkova & Kaun, 2019), or refashioning (Leonardi, 2011). I acknowledge these accomplishments within larger contests shaped by inequality, institutions, and ideology (Schradie, 2019).
People enlist and resist jokes, memes, and stories in articulations that shape how they see search engines and how they use search engines to learn about the world. People say “just google it”, “LMGTFY”, “google is your friend”, “google knows everything until you have an assignment”, “I am not your google”, “google told me so”, and “Dr. Google.” Such talk circulates and helps construct the knowledge of the acceptance of search for or by different people in different contexts. I look at these as part of “the social process by which [web search] is made into a legitimate system” (Gillespie, 2014, p. 192).
Mager (2017, 2018) has examined the construction of search engine imaginaries, though she notes her focus on legal and policy debates and on Google “obstructs the view of alternative imaginaries of search engines, that may be found at the edges of the material” (2017, p. 257). Amidst concerns of dominant technology companies “hegemonic position in imagining and shaping future society”, Mager & Katzenbach (2021) call for asking “how to support counter-imaginaries” (p. 233).
This focuses attention on how people enlist or resist web search articulations to support their autonomy and practices of knowing against dominant narratives. The platforms and “coding elite” (Burrell & Fourcade, 2021) construct their own search logics. These intersect with normative conceptions of access, responsibility, knowing, expertise, and information. Looking at counter-articulations will include examination of how different groups may pursue strategic behavior (or gaming (Burrell et al., 2019, Gillespie, 2017, Petre et al., 2019)) and other coordinated practices around search, whether that is SEO optimization (Karpf, 2017, Lewandowski et al., 2021, Ziewitz, 2019), seeding their own search queries in the face of propagandists (Golebiewski & boyd, 2018, Tripodi, 2018, 2022), custodian “social searching” (Gilbert, 2018, Gillespie, 2018, Morris, 2013), and ways of searching for information outside of the dominant technologies (Ochigame, 2020).
Vaidhyanathan (2011)’s study of the “Googlization of everything”, closes with “imagining a better way” for search (p. 200). Noble (2018) concludes her analysis of algorithmic oppression in web search with a call to “imagine search with a variety of other possibilities,” arguing “[s]uch imaginings are helpful in an effort to denaturalize and reconceptualize how information could be provided to the public vis-à-vis the search engine” (pp. 181-182). In Tripodi (2022)’s closing, she writes “we should pour more attention and resources into research that examines how epistemology shapes the who, what, where, when, and why of search” (p. 215). Following these calls and what Sundin et al. (2021) identify as a “move by Google towards a greater explicit interest in societal relevance” (p. 5) (i.e., “what is beneficial to society at large” (p. 4)), I ask who is included in the production of search. I also look at the configuration of the components of the larger system of the search engine, particularly with regard to new machine learning and “artificial intelligence” systems (Shah & Bender, 2022).
Benjamin, R. (2019). Introduction: Discriminatory design, liberating imagination. In R. Benjamin (Ed.), Captivating technology: Race, carceral technoscience, and liberatory imagination in everyday life (pp. 1–14). Duke University Press. https://doi.org/10.2307/j.ctv11sn78h [benjamin2019captivating_intro]
Bucher, T. (2017). The algorithmic imaginary: Exploring the ordinary affects of facebook algorithms. Information, Communication & Society, 20(1), 30–44. https://doi.org/10.1080/1369118X.2016.1154086 [bucher2017algorithmic]
Burrell, J. (2009). The field site as a network: A strategy for locating ethnographic research. Field Methods, 21(2), 181–199. https://doi.org/10.1177/1525822X08329699 [burrell2009field]
Burrell, J., & Fourcade, M. (2021). The society of algorithms. Annual Review of Sociology, 47(1), null. https://doi.org/10.1146/annurev-soc-090820-020800 [burrell2021society]
Burrell, J., Kahn, Z., Jonas, A., & Griffin, D. (2019). When users control the algorithms: Values expressed in practices on Twitter. Proc. ACM Hum.-Comput. Interact., 3(CSCW). https://doi.org/10.1145/3359240 [burrell2019control]
Christin, A. (2017). Algorithms in practice: Comparing web journalism and criminal justice. Big Data & Society, 4(2), 1–12. https://doi.org/10.1177/2053951717718855 [christin2017algorithms]
Cifor, M., Garcia, P., Cowan, T. L., Rault, J., Sutherland, T., Chan, A., Rode, J., Hoffmann, A. L., Salehi, N., & Nakamura, L. (2019). Feminist data manifest-no. https://www.manifestno.com/ [cifor2019feminist]
Cotter, K. (2022). Practical knowledge of algorithms: The case of BreadTube. New Media & Society, 1–20. https://doi.org/10.1177/14614448221081802 [cotter2022practical]
Dunne, A., & Raby, F. (2013). Speculative everything: Design, fiction, and social dreaming (the mit press). The MIT Press. [dunne2013speculative]
Gilbert, S. A. (2018). Motivations for participating in online initiatives : Exploring motivations across initiative types [PhD thesis, University of British Columbia]. https://doi.org/10.14288/1.0372890 [gilber2018motivations]
Gillespie, T. (2014). The relevance of algorithms (T. Gillespie, P. J. Boczkowski, & K. A. Foot, Eds.; pp. 167–193). The MIT Press. https://doi.org/10.7551/mitpress%2F9780262525374.003.0009 [gillespie2014relevance]
Gillespie, T. (2017). Algorithmically recognizable: Santorum’s google problem, and google’s santorum problem. Information, Communication & Society, 20(1), 63–80. https://doi.org/10.1080/1369118X.2016.1199721 [gillespie2017algorithmically]
Gillespie, T. (2018). Platforms are not intermediaries. Georgetown Law Technology Review, 2(2), 198–216. [gillespie2018platforms]
Golebiewski, M., & boyd, danah. (2018). Data voids: Where missing data can easily be exploited. Data & Society. https://datasociety.net/library/data-voids-where-missing-data-can-easily-be-exploited/ [golebiewski2018data]
Golebiewski, M., & boyd, danah. (2019). Data voids: Where missing data can easily be exploited. Data & Society. https://datasociety.net/library/data-voids/ [golebiewski2019data]
Haider, J., & Sundin, O. (2019). Invisible search and online search engines: The ubiquity of search in everyday life. Routledge. https://doi.org/10.4324/9780429448546 [haider2019invisible]
Hendry, D. G., & Efthimiadis, E. N. (2008). Conceptual models for search engines. In A. Spink & M. Zimmer (Eds.), Web search: Multidisciplinary perspectives (pp. 277–307). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-75829-7_15 [hendry2008conceptual]
Introna, L. D. (2016). Algorithms, governance, and governmentality. Science, Technology, & Human Values, 41(1), 17–49. https://doi.org/10.1177/0162243915587360 [introna2016algorithms]
Introna, L. D., & Nissenbaum, H. (2000). Shaping the web: Why the politics of search engines matters. The Information Society, 16(3), 169–185. https://doi.org/10.1080/01972240050133634 [introna2000shaping]
Jackson, S. J. (2014). Rethinking repair (T. Gillespie, P. J. Boczkowski, & K. A. Foot, Eds.; pp. 221–240). The MIT Press. https://doi.org/10.7551/mitpress/9780262525374.003.0011 [jackson2014rethinking]
Karpf, D. (2017). Analytic Activism: Digital Listening and the New Political Strategy. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780190266127.001.0001 [karpf2016analytic]
Lareau, A. (2021). Listening to people: A practical guide to interviewing, participant observation, data analysis, and writing it all up. The University of Chicago Press. [lareau2021listening]
Latour, B. (1990). Drawing things together. In M. Lynch & S. Woolgar (Eds.), Representation in scientific practice. MIT Press. [latour1990drawing]
Latour, B., & Woolgar, S. (2013). Laboratory life: The construction of scientific facts (princeton paperbacks) (J. Salk, Ed.). Princeton University Press. https://muse.jhu.edu/book/57399 [latour2013laboratory]
Leonardi, P. M. (2011). When flexible routines meet flexible technologies: Affordance, constraint, and the imbrication of human and material agencies. MIS Quarterly, 35(1), 147–167. http://www.jstor.org/stable/23043493 [leonardi2011flexible]
Lewandowski, D., Sünkler, S., & Yagci, N. (2021). The influence of search engine optimization on google’s results: A multi-dimensional approach for detecting seo. 13th Acm Web Science Conference 2021, 12–20. https://doi.org/10.1145/3447535.3462479 [lewandowski2021influence]
Mager, A. (2017). Search engine imaginary: Visions and values in the co-production of search technology and europe. Social Studies of Science, 47(2), 240–262. https://doi.org/10.1177/030631271667143 [mager2017search]
Mager, A. (2018). Internet governance as joint effort: (Re)ordering search engines at the intersection of global and local cultures. New Media & Society, 20(10), 3657–3677. https://doi.org/10.1177/1461444818757204 [mager2018internet]
Mager, A., & Katzenbach, C. (2021). Future imaginaries in the making and governing of digital technology: Multiple, contested, commodified. New Media & Society, 23(2), 223–236. https://doi.org/10.1177/1461444820929321 [mager2021future]
Marcus, G. E. (1995). Ethnography in/of the world system: The emergence of multi-sited ethnography. Annual Review of Anthropology, 24, 95–117. http://www.jstor.org/stable/2155931 [marcus1995ethnography]
Morris, M. R. (2013). Collaborative search revisited. Proceedings of Cscw 2013. https://doi.org/10.1145/2441776.2441910 [morris2013collaborative]
Nagel, E. van der. (2018). “Networks that work too well”: Intervening in algorithmic connections. Media International Australia, 168(1), 81–92. https://doi.org/10.1177/1329878X18783002 [nagel2018networks]
Noble, S. U. (2018). Algorithms of oppression how search engines reinforce racism. New York University Press. https://nyupress.org/9781479837243/algorithms-of-oppression/ [noble2018algorithms]
Ochigame, R. (2020). Informatics of the oppressed. In Logic. https://logicmag.io/care/informatics-of-the-oppressed/ [ochigame2020informatics]
Petre, C., Duffy, B. E., & Hund, E. (2019). “Gaming the system”: Platform paternalism and the politics of algorithmic visibility. Social Media + Society, 5(4), 2056305119879995. https://doi.org/10.1177/2056305119879995 [petre2019gaming]
Prior, L. (2003). Using documents in social research. SAGE Publications Ltd. https://doi.org/10.4135/9780857020222 [prior2003using]
Pugh, A. J. (2013). What good are interviews for thinking about culture? Demystifying interpretive analysis. Am J Cult Sociol, 1(1), 42–68. https://doi.org/10.1057/ajcs.2012.4 [pugh2013good]
Schradie, J. (2019). The revolution that wasn’t: How digital activism favors conservatives. Harvard University Press. https://www.hup.harvard.edu/catalog.php?isbn=9780674972339 [schradie2019revolution]
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 [seaver2017algorithms]
Shah, C., & Bender, E. M. (2022, March). Situating search. ACM SIGIR Conference on Human Information Interaction and Retrieval. https://doi.org/10.1145/3498366.3505816 [shah2022situating]
Sundin, O., Lewandowski, D., & Haider, J. (2021). Whose relevance? Web search engines as multisided relevance machines. Journal of the Association for Information Science and Technology. https://doi.org/10.1002/asi.24570 [sundin2021relevance]
Tripodi, F. (2018). Searching for alternative facts: Analyzing scriptural inference in conservative news practices. Data & Society. https://datasociety.net/output/searching-for-alternative-facts/ [tripodi2018searching]
Tripodi, F. (2022). The propagandists’ playbook: How conservative elites manipulate search and threaten democracy (Hardcover, p. 288). Yale University Press. https://yalebooks.yale.edu/book/9780300248944/the-propagandists-playbook/ [tripodi2022propagandists]
Tuck, E. (2009). Suspending damage: A letter to communities. Harvard Educational Review, 79(3), 409–428. https://doi.org/10.17763/haer.79.3.n0016675661t3n15 [tuck2009suspending]
Vaidhyanathan, S. (2011). The googlization of everything:(And why we should worry). Univ of California Press. https://doi.org/10.1525/9780520948693 [vaidhyanathan2011googlization]
Van Couvering, E. J. (2007). Is relevance relevant? Market, science, and war: Discourses of search engine quality. Journal of Computer-Mediated Communication, 12(3), 866–887. https://doi.org/10.1111/j.1083-6101.2007.00354.x [couvering2007relevance]
Velkova, J., & Kaun, A. (2019). Algorithmic resistance: Media practices and the politics of repair. Information, Communication & Society, 0(0), 1–18. https://doi.org/10.1080/1369118X.2019.1657162 [velkova2019resistance]
Vertesi, J. (2019). From affordances to accomplishments: PowerPoint and excel at NASA. In digitalSTS (pp. 369–392). Princeton University Press. https://doi.org/10.1515/9780691190600-026 [vertesi2019affordances]
Ziewitz, M. (2019). Rethinking gaming: The ethical work of optimization in web search engines. Social Studies of Science, 49(5), 707–731. https://doi.org/10.1177/0306312719865607 [ziewitz2019rethinking]