This dissertation examines the efforts of journalists to expand their pool of potential sources beyond a group of people often called "the usual suspects." This group consists of public officials, business leaders, experts, spokespeople, and other people who are in the news often. Using interviews, participant observation, a survey, and online ethnography, this research investigates how a growing skepticism of the usual suspects and increasingly powerful technology have led to innovations in the source search process.
Some journalists have seen potential in digital search tools, including databases and social media, for finding sources that had once been too difficult or time-consuming to find. Journalists themselves have created two source-finding initiatives: a database called the Public Insight Network, and Storyful, which calls itself the "world's first social news agency." Storyful journalists specialize in finding and verifying social media content from the scenes of breaking news events. Journalists have also used other tools created by public relations professionals and technologists.
How did the availability of these tools change the reporting process? It varied by tool, and by journalist. Although the tools were designed to do similar things, journalists used them in different ways. This dissertation examines how journalists used these tools in three stages of the reporting process: finding sources, verifying sources, and managing sources. Ultimately, most journalists used these tools not to find new sources, but to follow and research sources they had already identified by name or location. Few journalists had discovered new sources and story ideas with the help of digital search tools. So while these tools opened new possibilities for finding sources, journalists were still more likely to cover some people and topics over others.
Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/D8FB5135 |
Date | January 2014 |
Creators | Fink, Katherine |
Source Sets | Columbia University |
Language | English |
Detected Language | English |
Type | Theses |
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