Their work revealed interesting patterns, but even with this monastic devotion, they could analyze only a small number of newspapers in a year, compared with the number available. Frequently, graduate students assisting in research projects burrowed through stacks of newspapers and checked off every time a word was used in an article. It wasn’t for lack of trying: as recently as the early 1990s, researchers used a laborious process to transform text into a usable data set. “I realized there was a ton of data for this industry that people hadn’t really exploited before,” he says. He began developing his ideas about the economics of media-and about the process of text analysis-just as technology was beginning to give researchers far more access to text, through online databases and internet archives that could be analyzed with keyword searches and other methods. Gentzkow’s work won him the 2014 John Bates Clark Medal, given annually to the American economist under age 40 whom a committee of the American Economic Association deems has made the most significant contribution to economic thought and knowledge. An important vein of his research seeks to uncover economic reasons behind seemingly ideological choices, such as whether newspapers choose political affiliations to differentiate from their competitors, or whether papers in markets that skew politically liberal or conservative tend to use the words and phrases favored by their readers. Ryan Professor of Economics and Neubauer Family Faculty Fellow at Chicago Booth, who first became interested as a graduate student in using text analysis to tease out the economics of the media industry. One of the pioneers of text analysis is Matthew Gentzkow, Richard O. Two years later, researchers are increasingly doing just that. Einav and Levin suggested in 2013 that economists must begin to study computer-programming languages and machine-learning algorithms if they hope to tackle cutting-edge questions. It’s an engineering feat simply to ensure that all those computers are communicating properly with one another. With the advent of cloud computing, the data can be stored on thousands or millions of machines. They have what researchers call “high dimensionality,” meaning there can be a huge number of variables, and an enormous number of ways to organize them in a form that can be analyzed. In contrast to those simple textbook models, text data-where observations might be tweets, or phrases from the Congressional Record-are unstructured. Levin for the National Bureau of Economic Research, in their survey of how economists are using big data. “In econometrics textbooks, data arrives in ‘rectangular’ form, with N observations and K variables, and with K typically a lot smaller than N,” write Liran Einav and Jonathan D. Investors can also benefit from analyzing text. Political junkies can use text to understand why the phrase “mashed potato” boded ill for Newt Gingrich’s presidential aspirations-and learn from that, too. Economists could pinpoint the start of a financial crisis and determine which policy remedies are most effective. Businesses may be able to learn about a product defect before anyone calls customer service. They are parsing fragments of language, encountering issues of syntax, tone, and emotion-not to mention emoticons-to discern what we are saying, what we mean when we say it, and what the relationship is between what we say and what we do.
Aided by powerful computers and new statistical methods, they are dissecting newspaper articles, financial analyst reports, economic indicators, and Yelp reviews. Researchers have recognized something in all this text: data. On Facebook, 864 million of us log in to post status updates, comment on news stories, and share videos. We perform more than 250 million searches on eBay. We use language to describe, instruct, argue, praise, woo, debate, joke, gossip, relate, compare, reassure, berate, suggest, appease, threaten, discuss, forgive, respond, propose, inspire, complain, interject, boast, agree, soothe, harangue, confess, question, imply, express, verify, interrupt, lecture, admonish, report, direct, explain, persuade.Įvery day, we express ourselves in 500 million tweets and 64 billion WhatsApp messages.
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