Social Movement Data II

The availability of previously unimaginable volumes of text and network data on new forms of communication (e.g., “big data” from Facebook and Twitter) and other technological advancements that allow researchers to collect new kinds of data on old forms of communication and participation (e.g., using drones to photograph and analyze protests events, or using nationally representative survey data to map protest events), have led some to predict that social movement research is on the cusp of new discoveries concerning social movement processes. Others are less optimistic that more data leads to better theory.

For Mobilizing Ideas’ next essay dialogue, we are inviting contributors to reflect on both the promise and limitations of innovative techniques for collecting data on social movements and protest. In what ways, if any, do new sources or forms of data allow us to test classic theories (of movement emergence, recruitment, diffusion, outcomes, etc.) or to generate new ones? Which methodological problems of “old” collection techniques are solved by new methods, and which persist? What new hurdles are posed by the availability of “big data”? In what ways does our methodology restrict the kinds of questions we ask? What types of questions are new forms of data best suited to address? What impact will all of this have on the long-term future of social movement research?

Adding to last month’s essays, we are posting 4 new insightful contributions. Many thanks to our cast of contributors:

Jen Schradie – University of California, Berkeley (essay)
Fabio Rojas – Indiana University (essay)
Jennifer Earl – University of Arizona (essay)
Neal Caren – University of North Carolina, Chapel Hill (essay)

As always, we invite you to join the dialogue by posting your reactions to these essays in the comments sections.

Editors in Chief,
Grace Yukich, David Ortiz, Rory McVeigh, and Dan Myers

It’s All in How You Use It

By Jennifer Earl

When Internet use was beginning to grow in the 1990s, a now decades-old debate started over whether the Internet would bring vast improvements to society, social relations, and individuals or lead to greater inequality, more anomie, and a much thinner civic core. As time wore on, many scholars studying information communication technologies (ICTs) and society were influenced by earlier work in science and technology studies (STS), which suggested that technologies themselves had no direct impact on society, but rather that their impact depended on how the technologies were used (and misused). And, after watching conflicting findings on the impacts of Internet usage roll in for about a decade, the majority of researchers in this area began to support a much milder conclusion: Internet usage would produce some social benefits and likely some social difficulties and the mix and appearance of those would depend on its usage.

The Path to Big Data Sociology Isn’t Obvious

By Neal Caren

Hand in hand with the rise of the “big data” in the social sciences is an enthusiasm for incorporating new methods to analyze these data. Most prominent among these are topic models for analyzing text data and random forests for modeling categorical outcomes. Just as the rise of new, large-scale, real-time data sets presents challenges and opportunities to social movement researchers, many of the standard methods used to analyze this new data presents promise for scholars, but it won’t be necessarily be easy.

Big Data and Social Movement Research

By Fabio Rojas

“Big data” sounds fun and exciting but it has also been heavily criticized. But now, it’s time to step back and treat “big data” as we would treat any other form of data. We should identify its strengths and weaknesses and ask how it can help us with our own specific research goals. So let’s start with an obvious, but under-appreciated, point about empirical research: there is no such thing as perfection in data. Every method for generating and collecting data has strengths and weaknesses. Thus, we should be interested in data collection methods where the positive points outweigh the negative points. For example, experimental data has a great virtue – those who receive the treatment are randomly selected, thus eliminating bias. Experimental data also has a serious drawback. Experimental settings may not reflect “real world” processes and are often not generalizable. This is a serious problem for biomedical research, for example. A drug tested in a highly controlled environment may work differently than in the actual setting of a hospital. Yet, we value experiments because they do one thing exceptionally well – they eliminate selection bias and address the issue of confounding variables.

5 reasons why online Big Data is Bad Data for researching social movements

By Jen Schradie

I know, I know, it’s digital blasphemy to say that using Internet data is a terrible way to study social movements. What about all of those Twitter and Facebook revolutions of the Arab Spring? And Occupy Wall Street? #Ferguson and #BlackLivesMatter spread like wildfire, for God’s sake.

You may think that I’m a luddite who doesn’t see the sheer statistical splendor and speed of social network diagrams or automated text analyses made from Tweets.  Or, perhaps you’re thinking that old-school scholars just don’t get it: digital activism is the future, so we need to disrupt, innovate and flatten those hierarchical Marxist social movement sociologists.

But before you reach through your screen and strangle me with your iPhone charger cord, consider these ways in which online data, whether social media or otherwise, might not be as representative or generalizable as they are fast and efficient.

Social Movement Data

The availability of previously unimaginable volumes of text and network data on new forms of communication (e.g., “big data” from Facebook and Twitter) and other technological advancements that allow researchers to collect new kinds of data on old forms of communication and participation (e.g., using drones to photograph and analyze protests events, or using nationally representative survey data to map protest events), have led some to predict that social movement research is on the cusp of new discoveries concerning social movement processes. Others are less optimistic that more data leads to better theory. For Mobilizing Ideas’ next essay dialogue, we are inviting contributors to reflect on both the promise and limitations of innovative techniques for collecting data on social movements and protest. In what ways, if any, do new sources or forms of data allow us to test classic theories (of movement emergence, recruitment, diffusion, outcomes, etc.) or to generate new ones? Which methodological problems of “old” collection techniques are solved by new methods, and which persist? What new hurdles are posed by the availability of “big data”? In what ways does our methodology restrict the kinds of questions we ask? What types of questions are new forms of data best suited to address? What impact will all of this have on the long-term future of social movement research?

Thank you to all of our contributors, their essays are below.

Benjamin Lind, National Research University Higher School of Economics (essay)
Laura K. Nelson, Northwestern University (essay)
Austin Choi-Fitzpatrick, Central European University (essay)
Zachary C. Steinhart-Threlkeld, University of California-San Diego (essay)
Alex Hanna, University of Wisconsin-Madison (essay)
Thomas Elliott, University of California-Irvine (essay)

Editors in Chief,
Grace Yukich, David Ortiz, Rory McVeigh, Dan Myers

Advances in Understanding Protest with Big Data

By Zachary C. Steinert Threlkeld

The study of collective action can benefit greatly from big data. Collective action is the study of how large numbers of individuals engage each other to accomplish a common task; big data illuminate how large numbers of individuals engage each other over time. Yet these data have yet to show how they can improve our understanding of protests. Protests are one of the hardest collective action problems: large groups of individuals with little prior contact must come together and coordinate their behavior in risky situations for public goals. My research starts to show how, carefully used, big data generate new insights into protest processes.

Drones, Data, and Tactics

By Austin Choi-Fitzpatrick

Protest repertoires have been relatively stable for a really long time. Petitions have been with us since the first anti-slavery movement. Boycotts for almost as long. Nothing changes, and then it does. The recent explosion of new tactics rooted in technological innovation has spelled promise and peril for movements, most of whom face formidable obstacles and better-resourced incumbents.

I’m as excited by this turn of events as anyone; much of my recent work has taken place in rural India, where the most sophisticated technology in sight is usually a Nokia candybar phone. Sometimes an exploited laborer sends an urgent midnight text calling for help. Sometimes.

Text as Data: A Call to Standardize Access and Training

By Laura K. Nelson

Data come in all shapes and sizes, but in the past ten years we have seen huge leaps in the amount of data readily available in the form of unstructured or semi-structured text. This presents both opportunities and challenges for social science researchers, including social movements scholars.

Two sources of text-as-data have long been staples in social movements research: newspapers and organizational literature. Newspapers have been used as the basis for event counts (e.g., here, here, and here), as a measurement of movement frames, and as a movement outcome (e.g., here and here). Organizational literature is often used to show that the way social movements themselves express ideas is critical to their success (e.g. here, here, and here).

Big Data, Automated Textual Analysis, and Protest Events

By Alex Hanna

One of the longstanding issues with social movements research is the availability of reliable, timely, and comprehensive protest event data. Ideally, we would like to cover multiple movements and have adequate temporal and spatial variation. However, the generation of protest event data has usually meant many human hours dedicated to hand-coding, usually by farms of social science undergraduates. But the wide availability of electronic sources and advances in natural language processing – or in a word, “big data” – has the potential for pushing the boundaries of our field.