Abstract

http://ssrn.com/abstract=2459325
 


 



Researchers to Crowds to Algorithms: Building Large, Complex, and Transparent Databases from Text in the Age of Data Science


Nicholas Adams


University of California, Berkeley - Sociology

July 6, 2014


Abstract:     
This article reviews available text analysis approaches and their limited success generating rich, transparent databases at scale. After elucidating the precise difficulties faced by previous projects and methods, the article introduces a new approach to text analysis featuring innovative procedures and open source tools to optimally combine the efforts of researchers, crowds, and algorithms. The article describes the first substantive project deploying the approach, imagines other projects on theoretical/empirical frontiers enabled by the approach, and closes with promising implications for social science when researchers are able to generate larger, more complex, transparent databases faster.

Number of Pages in PDF File: 94

Keywords: text analysis, crowdsourcing, machine learning, event analysis, crowd work, content analysis, algorithms


Open PDF in Browser Download This Paper

Date posted: June 27, 2014 ; Last revised: November 28, 2014

Suggested Citation

Adams, Nicholas, Researchers to Crowds to Algorithms: Building Large, Complex, and Transparent Databases from Text in the Age of Data Science (July 6, 2014). Available at SSRN: http://ssrn.com/abstract=2459325 or http://dx.doi.org/10.2139/ssrn.2459325

Contact Information

Nicholas Adams (Contact Author)
University of California, Berkeley - Sociology ( email )
Berkeley Institute for Data Science
190 Doe Library
Berkeley, CA 94720
United States
HOME PAGE: http://https://bids.berkeley.edu/people/nick-adams
Feedback to SSRN


Paper statistics
Abstract Views: 626
Downloads: 148
Download Rank: 147,853

© 2016 Social Science Electronic Publishing, Inc. All Rights Reserved.  FAQ   Terms of Use   Privacy Policy   Copyright   Contact Us
This page was processed by apollobot1 in 0.203 seconds