Driving Regulation: Using Topic Models to Examine Political Contention in the U.S. Trucking Industry. (April 2014)

• Record Type: Journal Article
• Authors: Levy, Karen E. C.; Franklin, Michael
• Other Names: Mejova Yelena guest-editor.; Weber Ingmar guest-editor.
• Is Part Of: Social science computer review. Volume 32:Number 2(2014:Summer)
• Page Start: 182
• Page End: 194
• Publication Date: 2014-04
• Subjects: topic modeling -- latent Dirichlet allocation -- e-rulemaking -- public comments -- trucking; Social sciences -- Data processing -- Periodicals; Computers -- Social aspects -- Periodicals; Microcomputers -- Periodicals; Sciences sociales -- Informatique -- Périodiques; Micro-ordinateurs -- Périodiques; 300.285
• DOI: 10.1177/0894439313506847
• Languages: English
• ISSNs: 0894-4393

Abstract

Public comments submitted during agency rulemakings can provide rich insight into stakeholders' viewpoints around contentious political issues but have been largely untapped as a data source by social scientists. This is in part due to the lack of access to comments in machine-readable formats and in part due to the difficulty in analyzing large corpora of textual data. However, new online repositories and analytic methodologies are beginning to open up this trove of data for researchers. Using data from the online portal regulations.gov, we employ probabilistic topic modeling to identify latent themes in a series of regulatory debates about electronic monitoring in the U.S. trucking industry. Our model suggests that different types of commenters use alternative discursive frames in talking about monitoring. Comments submitted by individuals were more likely to place the electronic monitoring debate in the context of broader logistical problems plaguing the industry, such as long wait times at shippers' terminals, while organizational stakeholders were more likely than individuals to frame their comments in terms of technological standards and language suggesting cost / benefit quantification.