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. Continue reading
Tag Archives: big data
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. Continue reading →
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. Continue reading →
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. Continue reading →
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. Continue reading →
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. Continue reading →
The proliferation of ample, publicly available information from varied sources is an outcome that we should celebrate as scholars. While this proliferation does not offer a panacea for all research needs, it does offer numerous insights unavailable to scholars a generation ago. This potential for insight stems not only from increased data availability, but also from the sociological imagination and creativity of movement scholars who can leverage the flexibility afforded by modern information systems.
For this dialog, I would like to focus exclusively on “quantitative data” from publicly available sources, using passive data collection methods, for the purpose of better understanding social mobilization. In doing so, I will sidestep or only lightly touch upon subjects such as online activism and its implications offline, active data collection (e.g., administering online surveys), questions of research ethics and privacy related to online media, and methods of data analysis. Though these related topics warrant significant attention from the subject at hand, serious attention to these issues would divert from my ability to succinctly address this dialog’s theme. Continue reading →