Contribution to the CSCW 2022 Workshop: Solidarity and Disruption
by Christopher de Freitas & Marina Kogan
University of Utah, School of Computing
CSCW was established as a way to explore “the technical, social, material, and theoretical challenges of designing technology to support collaborative work and life activities” (CSCW, 2022). As part of that exploration, a key mandate of the ﬁeld has been to develop first-order approximations of social phenomena (Ackerman, 2000). There’s a necessary gap between the nature of a social phenomenon and its manifestation through online metrics. Since its inception, CSCW has identified a wide “base-set” of findings that have made significant progress in defining and shrinking that gap (Ackerman, 2000). These include the development of well-deﬁned proxies for social engagement (Palen & Anderson, 2016; Geiger & Halfaker, 2013), the identification of interesting emergent social properties in online spaces (Jost et al., 2018; Starbird et al., 2019), and the production of effective analytical frameworks for understanding social media data (Kogan & Palen, 2018; Liu et al., 2017). These advances have been essential to our modern understanding of online social dynamics, but as our ﬁeld’s collection of empirical observations grows, key limitations are beginning to reveal themselves.
In this paper, we argue that social computing research, particularly in the areas of social media analysis, ought to expand their methodological approaches to include deductive theoretical analysis. We claim broad acceptance of this approach in our ﬁeld will enable the modeling of more complex social phenomena, facilitate better communication channels with industry stakeholders, and vastly expand the kinds of questions researchers can ask. In general, we advocate for a community-wide focus on the nature of our relationship with sociology and the social sciences at large.
Computing has historically had a strong relationship with the natural sciences. But as technology becomes more and more integrated into existing social systems, it tends to produce its own novel social phenomena worth studying. This observation forms the ideological basis for social computing as a distinct interdisciplinary ﬁeld at the crossroads of computer science and sociology (Arif, 2020). However, that historical relationship to the natural sciences has had a lasting impression on the development of all subﬁelds in computing, including social computing. Of interest here is our heavy reliance on an empirical approach to inquiry.
Research can be said to have taken an “empirical approach” when its primary goal is to explore a novel or interesting dataset, document important features about it, and conclude by attempting to infer certain social phenomena that have occurred. This recipe is so ubiquitous in STEM spaces it risks invisibility, but in the world of social sciences there is a much greater diversity to the conceptual framing of quantitative research. Throughout this paper I’ll be referring to one such method as “deductive theoretic analysis”, deﬁned as an approach wherein a set of empirics is analyzed through the lens of an established theory. Though widely prevalent in social science research, and present in some CSCW research, many areas of social media analysis in particular could really beneﬁt from this approach. Looking towards the future, there is an urgent need to expand our approaches if we hope to tackle large-scale online social phenomena.
Today, online misinformation, disinformation, and other forms of complex networked manipulation are growing to intractable levels (Arif, 2020). In spite of attempts within the ﬁeld to provide high level conceptual models to guide the development of appropriate research questions and aid in the analysis of this new phenomenon (Baines & Elliott, 2020; Arif et al., 2017), there has been no consensus on how we, as a ﬁeld, ought to approach these topics. More worrisome is the sense that we, as a ﬁeld, may not have the appropriate vocabulary to evaluate any of these conceptual models to begin with, making consensus challenging. In finding the right set of tools to tackle this emerging problem, we ought to start by reaching for the large corpus of existing sociological research.
By approaching our behavioral observations within the framework of an existing behavioral model, we add constraints to our conclusions that keep us from easy generalizations. As an example, a common approach to understanding social movements and collective action frames goes back to Benford and Snow (2000). Brieﬂy, this approach calls for the stratiﬁcation of rhetoric within collective action movements into “prognostic”, “diagnostic”, and “motivational” bins which can then be individually analyzed to generate an overall impression of the movement. It’s an intuitive, eﬀective, and popular model that sees a lot of use in many ﬁelds interested in collective action.
Inferring social phenomena from social media data is an act of reduction. Our datasets are often far too large to see every corner of, so we draw our conclusions from metrics designed to approximate behavioral patterns at scale. But without committing to some set of heuristics or a theoretical framing, at the end of the day a researcher must rely on their own intuition to make the inferential jumps in determining “what do these features represent?”. For simple phenomena, this is appropriate. As we tackle more complex interactions in larger and larger datasets, results generated from this intuition will prove less and less useful. A “theory-ﬁrst” approach can help focus the earliest and latest moments in a research project by drawing our attention to known facts about human socialization already established by social sciences.
Klein (2021) sees gaps in interdisciplinary understanding as primarily a communications issue. Even when our conceptual models appear wholly diﬀerent on the surface to those of sociological ﬁelds, translation is tractable. The goal with this perspective is to “reject the naive faith that everything will work out if everyone just sits down and talks to each other” while working to develop an integrated analytic framework and common vocabulary. (Klein 2005)
Interdisciplinarity tends to be rife with miscommunication. As an example, the general concept of “desertiﬁcation” is of interest to a wide variety of ﬁelds in the natural sciences (climatology, hydrology, soil science, etc.) and the social sciences (economics, political science, geography, etc.), making it a prime candidate for interdisciplinary work. Additionally, however, each ﬁeld has their own set of criteria for judging the utility of any particular deﬁnition. The natural consequence? “Desertiﬁcation” now has more than 100 deﬁnitions scattered around the literature, driving severe communication rifts between academics and policy makers (Klein, 2005). Maintaining a wider common vocabulary is key to reducing the incidence of these rifts between CSCW and social science.
Further integrating established theoretical perspectives also opens up a lot of new space for the kinds of claims researchers will be able to make. Empirical research is very valuable, but alone it’s restricted by its inability to eﬀectively ask questions of political, ethical, or aesthetic natures. Merely describing the phenomena we study puts us at risk of adopting a "narrowly strategic reason" (Schecter, 2007), which, though very capable of establishing facts, would leave us incapable of engaging in reﬂexivity or criticality (Berry, 2015). Opening the door to theoretical perspectives as framing devices also opens up space for more than just sociological analysis.
Important work is happening at our boundary with decoloniality (Wong-Villacres et al., 2020), feminist epistemology (Steinhardt et al., 2015), and elsewhere (Berry, 2015). Encouraging deductive theoretic analysis draws attention to research like this, inspiring more creative and critical work from our ﬁeld.
As a fast-growing interdisciplinary ﬁeld, we ought to be concerning ourselves not with achieving an eﬀective paradigm for interdisciplinarity, but with deﬁning one. We ought to be occupying ourselves not just with the outcomes of our integration with the social sciences, but with the quality of that integration itself. To make that happen, we need to rely more on the work that’s already been done in those ﬁelds to model social phenomena.
Without taking these steps we, at best, cheat ourselves by not leveraging the excellent work already done by ﬁelds outside our own. At worst, we risk uncritically replicating the structural incentives and inductive biases in perpetuity. To make this change, however, it’s clear we need a strategy. Shooting from the hip isn’t going to cut it anymore. Researchers all across our community need to be invested together in regrouping with our roots in the social sciences.
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