Research
The below is out-of-date, please see tooling to support people in making hands-on and open evaluations of search for my latest research project.
This page is about Daniel S. Griffin's research direction.
See also:
- Peer-reviewed publications
- Dissertation
- CV for full list of publications
- the relevance of my research to building search
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, 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
Three questions frame my research, they look at:
1. How do people make successful use of web search?
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 @tuck2009suspending, 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, 2019; Haider, 2019; Introna, 2000; Noble, 2018; Tripodi, 2022; Vaidhyanathan, 2011; Couvering, 2007). This question suggests a different tack. I look to see, as @latour2013laboratory 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 @vertesi2019affordances 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, 2019) and resistance (Christin, 2017; Nagel, 2018; Velkova, 2019), or refashioning (Leonardi, 2011). I acknowledge these accomplishments within larger contests shaped by inequality, institutions, and ideology (Schradie, 2019).
2. How do people articulate and legitimate the use of web search against dominant narratives in their work and lives?
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 (Mager, 2017; Mager, 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 (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, 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, 2019; Gillespie, 2017; Petre, 2019)) and other coordinated practices around search, whether that is SEO optimization (Karpf, 2016; Lewandowski, 2021; Ziewitz, 2019), seeding their own search queries in the face of propagandists (Golebiewski, 2018; Tripodi, 2018; Tripodi, 2022), custodian "social searching" (Gilber, 2018; Gillespie, 2018; Morris, 2013), and ways of searching for information outside of the dominant technologies (Ochigame, 2020).
3. How can web search be produced and practiced to promote human flourishing?
@vaidhyanathan2011googlization's study of the "Googlization of everything", closes with "imagining a better way" for search (p. 200). @noble2018algorithms 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-a-vis the search engine" (pp. 181-182). In @tripodi2022propagandists'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 @sundin2021relevance 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, 2022).
[^1]: See Hendry (2008, p. 277).
References
Benjamin, R. (2019). Introduction. In Captivating technology. Duke University Press.
Bucher, T. (2017). The algorithmic imaginary: Exploring the ordinary affects of Facebook algorithms. Information, Communication & Society, 20(1), 30-44.
Burrell, J. (2009). The field site as a network: A strategy for locating ethnographic research. Field Methods, 21(2), 181-199.
Burrell, J., Kahn, Z., Jonas, A., & Griffin, D. (2019). When users control the algorithms. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW), 1-20.
Burrell, J., & Fourcade, M. (2021). The society of algorithms. Annual Review of Sociology, 47, 213-237.
Christin, A. (2017). Algorithms in practice: Comparing web journalism and criminal justice. Big Data & Society, 4(2).
Cifor, M., et al. (2019). Feminist data manifest-no.
Cotter, K. (2022). Practical knowledge of algorithms. Journal of Communication.
Van Couvering, E. (2007). Is relevance relevant? Journal of Information Science, 33(3), 355-371.
Dunne, A., & Raby, F. (2013). Speculative everything. MIT Press.
Gilbert, E. (2018). Social searching motivations.
Gillespie, T. (2014). The relevance of algorithms. In Media technologies (pp. 167-194). MIT Press.
Gillespie, T. (2017). Algorithmically recognizable: Santorum's Google problem, and Google's Santorum problem. Information, Communication & Society, 20(1), 63-80.
Gillespie, T. (2018). Custodians of the internet. Yale University Press.
Golebiewski, M., & boyd, d. (2018). Data voids: Where missing data can easily be exploited. Data & Society.
Golebiewski, M., & boyd, d. (2019). Data voids. Data & Society.
Haider, J., & Sundin, O. (2019). Invisible search and online search engines. Routledge.
Hendry, D. G. (2008). A conceptual framework for research on digital library design. Library & Information Science Research, 30(4), 277-283.
Introna, L., & Nissenbaum, H. (2000). Shaping the web: Why the politics of search engines matters. The Information Society, 16(3), 169-185.
Introna, L. (2016). Algorithms, governance, and governmentality. Science, Technology, & Human Values, 41(1), 17-49.
Jackson, S. J. (2014). Rethinking repair. In Media technologies (pp. 221-240). MIT Press.
Karpf, D. (2016). Analytic activism. Oxford University Press.
Lareau, A. (2021). Listening to people. University of Chicago Press.
Latour, B. (1990). Drawing things together. In Representation in scientific practice. MIT Press.
Latour, B., & Woolgar, S. (2013). Laboratory life. Princeton University Press.
Leonardi, P. M. (2011). When flexible routines meet flexible technologies. MIS Quarterly, 35(1), 147-167.
Lewandowski, D. (2021). The influence of commercial intent of search results on their perceived relevance. Information Services & Use, 41(1-2).
Mager, A. (2017). Search engine imaginary. Social Studies of Science, 47(2), 240-262.
Mager, A. (2018). Internet governance as joint struggle. Internet Policy Review, 7(3).
Mager, A., & Katzenbach, C. (2021). Future imaginaries in the making and governing of digital technology. New Media & Society, 23(2), 223-236.
Marcus, G. E. (1995). Ethnography in/of the world system: The emergence of multi-sited ethnography. Annual Review of Anthropology, 24, 95-117.
Morris, M. R. (2013). Collaborative search revisited. Proceedings of CSCW, 1181-1192.
Nagel, S. (2018). Networks of resistance.
Noble, S. U. (2018). Algorithms of oppression. NYU Press.
Ochigame, R. (2020). Informatics of the oppressed. Logic Magazine.
Petre, C. (2019). Gaming metrics.
Prior, L. (2003). Using documents in social research. SAGE.
Pugh, A. J. (2013). What good are interviews for thinking about culture? American Journal of Cultural Sociology, 1(1), 42-68.
Schradie, J. (2019). The revolution that wasn't. Harvard University Press.
Seaver, N. (2017). Algorithms as culture: Some tactics for the ethnography of algorithmic systems. Big Data & Society, 4(2).
Shah, C., & Bender, E. M. (2022). Situating search. CHIIR.
Sundin, O., et al. (2021). Relevance and the constitution of search. Journal of Documentation.
Tripodi, F. (2018). Searching for alternative facts. Data & Society.
Tripodi, F. (2022). The propagandists' playbook. Yale University Press.
Tuck, E. (2009). Suspending damage: A letter to communities. Harvard Educational Review, 79(3), 409-428.
Vaidhyanathan, S. (2011). The Googlization of everything. University of California Press.
Velkova, J., & Kaun, A. (2019). Algorithmic resistance. European Journal of Communication, 34(4), 423-432.
Vertesi, J. (2019). Seamful spaces. Science, Technology, & Human Values, 44(2), 371-400.
Ziewitz, M. (2019). Rethinking gaming: The ethical work of optimization in web search engines. Social Studies of Science, 49(5), 707-731.