Tina Law is a Stone Center postdoctoral scholar and a coauthor of “A Three-Step Guide to Training Computational Social Science Ph.D. Students for Academic and Non-Academic Careers,” available on SocArXiv. She is also a co-founder of varycss.org, a web-based initiative that highlights and supports computational social scientists who identify with groups that are underrepresented in the field. Law recently spoke to the Stone Center about the origins and growth of computational social science, how she became a sociologist, and why receiving her doctoral degree was particularly meaningful to her.
How would you define computational social science, and what do you see as driving its growth in recent years?
Law: There are plenty of definitions out there of what computational social science is, but I would define it as the use of data science to pursue social science questions. That can involve the use of computational methods, like agent-based modeling, machine learning, or network analysis. Or that can involve the use of big data, or what’s more specifically defined as large N and high-dimensional data.
I would think of computational social science as having very deep roots in the social sciences. The mathematical and statistical foundations of a lot of what folks are doing now in computational social science have been around for decades. What’s contributed to this amazing growth in computational science in recent decades is, I think, two things. First are advancements in computing storage and computing power, which are allowing social scientists to finally run a lot of computational methods that they have been wanting to run for a really long time. And the second kind of advancement is just the growth in the availability of data, in terms of new forms of digital data that is occurring on social media platforms, like Twitter and Facebook. These are kinds of new data that social scientists have never really encountered before, and that’s spurring all kinds of new research questions. But what folks often forget is the growth in digital data among local government agencies, for instance, who are using digital platforms more and more to track their services to residents. For example, the New York City 311 system is this huge, awesome social science dataset that is being used to address a lot of research questions.
Have we reached the point where you could enroll in a program that focuses specifically on computational social science?
Law: I don’t think that there are currently computational sociology programs, or computational political science programs. You would typically just pursue a sociology Ph.D., or a political science Ph.D. But that’s part of what the paper is addressing: the fact that you would have to pursue training in computational methods on your own as a Ph.D. student in the social sciences.
Jae Yeon Kim and Aniket Kesari, who are two of my coauthors on this paper, met as grad students at UC Berkeley, and they had wanted for some time to write a paper to help social science Ph.D. students to prepare for careers as computational social scientists, in and outside of academia. And so Jae reached out to me last fall, while I was working my dissertation, to see if I’d be interested in working on a paper like this.
At the time, I thought, as someone who leads and is part of a lot of efforts to make training in computational methods more inclusive and accessible, and also as a sociology Ph.D. student myself who knows firsthand how confusing and challenging it can be to navigate this growing field of computational social science, I knew that it would be so very helpful to have a written and openly accessible guide that actually discusses the process of becoming a computational social scientist. And I definitely wanted to be part of an effort like that, and to help with that in whatever way that I could.
Our team just kind of went from there, and we were able to work together over multiple time zones to write the paper over the course of a few months.
How did you become first become interested in this field?
Law: After graduating from college, I ended up working at a policy evaluation firm, Social Policy Research Associates in Oakland, California. And I had never really done research before, so they took a great chance on me. I was there for three years, and I was part of these teams that would evaluate various policies and programs that were typically designed to serve low-income communities and communities of color. And so I had this awesome opportunity in my early twenties to go around the country conducting interviews, doing focus groups to really try to evaluate these programs and policies on the ground, to make sure that they were helping the folks that they were supposed to help.
And I thought it was really awesome that research had this ability to help people communicate their needs, to help them get the resources they needed. And particularly for marginalized communities, it really provides them with an opportunity to define themselves on their own terms. And I thought that made research a really powerful way to affect social change.
That’s how I got into social science research, and how I ended up in a sociology Ph.D. program. I had originally actually planned to go to law school, but I loved research so much, I ended up pursuing a Ph.D. and becoming a sociologist.
In terms of how I got into computational social science, it kind of started with a conversation I had very early on with my Ph.D. advisor, Andy Papachristos. I was maybe in my first or second year of Ph.D. studies. I wanted to study something and there wasn’t data on it. And my advisor said, “Well, what do we do when we want to study something, and there isn’t data on it?” I said, “You give up and you find something else to do.” But he said, “No. What you do is you find another way to measure what it is that you’re trying to measure.”
I then went looking around for data. And that’s how I found all kinds of datasets that cities had been collecting. At the time, I was studying New York City, and I discovered the New York City Open Data Portal, which is this huge repository of data that agencies around New York City collect and track on the services that they provide. I ended up using one of the datasets there to create my own measure of what I needed for my study.
That’s how I got into computational social science. I had some background in doing network analysis as a policy evaluator, and I think working on network analysis in that context lent itself very easily to continuing to doing network analysis in a more academic setting, as well as then leading me to get into natural language processing and text analysis, which is what I’m doing more of these days. And so, it’s sort of unexpected turns in my career, but I think they all, in retrospect, led very naturally to one another.
You’ve said that one of the goals of your work is to make U.S. cities more democratic and equitable for racially minoritized residents. How is your research working toward that?
Law: Part of what I’m really interested in understanding is what happens when there are large-scale changes in cities. Cities are constantly changing. What happens when those changes come about, and how are racially minoritized residents affected by them?
We know from a longstanding tradition in social science research that a lot of these changes often impact racially minoritized residents the most. It’s really important to me that, when these sorts of big transformations happen in cities, we pay careful attention to how racially minoritized communities are impacted. Especially how they’re impacted socially. I think there is fortunately greater attention focused these days on how large-scale transformations can affect racially minoritized communities in economic terms. But there have been a lot of situations in which transformations, like the demolition of public housing, had not only economic effects on low-income residents of color, but also had a tremendous impact on them socially, in terms of disrupting their social networks, really transforming the social fabric of entire neighborhoods and cities. It’s important to me that we find ways to attend to that.
The other part of my work focuses on understanding how racially minoritized residents create social change in cities. There’s a longstanding tradition in sociology, as well as in other social sciences, of understanding how racially minoritized residents in cities have very unequal experiences living in their cities, and that things are constantly happening to them, and that the things that happen to them can often affect their lives in really negative ways. But it’s important at the same time to recognize that racially minoritized residents have made tremendous contributions — economically, culturally, and politically — to cities, and that we highlight those contributions and the active role that Black residents, in particular, and other racially minoritized residents, have had in making sure that our cities are more equal, more democratic, and more just. And a lot of this work has happened not only in the voting booth and in city hall, where most of the attention is, but out in the streets and on the ground.
When your Ph.D. arrived in the mail, you tweeted that you were standing on the shoulders of giants, referring to your grandmother and mother. What did it mean to you to receive your doctoral degree?
Law: This is something I’ve talked to my advisor about a lot. There are so many resources out there that are available to students who are dissertating and completing their Ph.D.’s on how to make sure they finish their dissertations and graduate on time. But there aren’t a whole lot of resources to help you deal with all the feelings that come along with finishing a Ph.D. And I think this is especially true of first-generation graduates. Of folks who were not only the first in their families to receive an undergraduate degree, but now are the first in their families to receive a Ph.D. And nothing quite prepares you for that.
I think for the longest time while I was dissertating, and then defending my dissertation, and then moving, it all was kind of a blur to me, and I was just exhausted. But I think receiving my Ph.D. in the mail, and looking at it for the first time, it really kind of takes your breath away. Because I think for a lot of folks like me, who come from backgrounds like mine, where educational opportunities were not always readily available and economic opportunities were not always readily available, it’s a very surreal feeling.
When I first looked at my Ph.D., it made me think about my mother, who had always cherished going to school, but never quite had any opportunities to do so. She grew up in Vietnam and her family was very poor, so they were unable to send all of their kids to school. So the boys in her family got to go to school, but she didn’t get to go, which was very typical of that time there. And then, my mother and her entire family fled Vietnam as a result of the Vietnam War. They were part of the “boat people,” an exodus of refugees who fled the Vietnam War by boat, and my mother and her family traveled to multiple refugee camps before arriving in the U.S. in the early ’80s.
And from there, there was never a good opportunity for my mother to go to school, whether it was when she first got here as a young woman and needed to find work very quickly and learn an entirely new language, or later on, when she was raising my younger sister and me, and she was trying to attend night school. But it’s very difficult to do that when you work full time. You work very hard, labor-intensive hours as well, and you are raising two little girls. And so I think that’s something that she had always wanted to pursue, but could never quite do.
And that extends also to my grandmother, my mother’s mother, who also couldn’t go to school. She grew up as an orphan, and she had to raise an entire family as well. And I remember being very little, and going to doctor’s appointments and pharmacies with my grandmother, and she would sign things with Xs. And I would ask my mom, “Why does she sign her name with Xs?” Because I was just learning to sign my own signature, and I thought it was so cool to have a signature. And my mother would explain that she does it that way, because she can’t read or write, and she’s illiterate. And that has always really stuck with me.
So I think looking at my Ph.D. for the first time, I was thinking that my mother and my grandmother probably couldn’t actually read this degree, but they could understand what it means to have that. I think that brought everything together for me, for the first time, that this was a big step. Not just for myself, but for multiple generations of my family to get to this point. I was so grateful for their efforts that allowed me to get to this point.
Read the Paper: A Three-Step Guide to Training Computational Social Science Ph.D. Students for Academic and Non-Academic Careers, by Aniket Kesari, Jae Yeon Kim, Sono Shah, Taylor Brown, Tiago Ventura, and Tina Law