Using Big Data to Measure Important Political Phenomena, Dr. Anton Sobolev, September 29|30, 2022

Many political phenomena are hard to quantify. Take electoral fraud. In the 21st century, both democracies and autocracies held regular elections. How do we know if the majority of voters indeed supported the winner? To answer this question, scholars and experts should be able to separate “real” votes from fakes. This ability is equally crucial for theory-testing and policy-making. In this talk, Dr. Sobolev will discuss the evolution of measurement approaches in social sciences, using an example of yet another paramount phenomenon: mass protest behavior. The ability of citizens to self-organize and protest serves as a key element in social science theories from economic growth, taxation, and property rights protection to democratization and civil conflict. At the same time, a vast strand of empirical studies shows that mass actions correlate with public policy changes. However, the credibility of their results depends on our ability to measure protest behavior accurately. How do scholars quantify protest behavior? How do we find out that a protest event took place? How many people participated in it? What about those who had incentives to protest but decided not to take to the streets? Early-day scholars approached these questions with tally counters and surveys. Nowadays, we take advantage of big data and AI. We will discuss the major stages of this evolution in quantifying protest behavior.