Phases in the crowd : examining how traditional media outlets can best use crowdsourced data
Landis, Jacob Wesley.
Thesis (M.A.)--Georgetown University, 2011.; Includes bibliographical references.; Text (Electronic thesis) in PDF format. Journalism, traditionally, has been defined by the medium through which it is published; large institutions, scarce due to the cost of publishing but able to reach massive audience, were the sources of news. Today, as noted in the work of Clay Shirky and Jeff Jarvis, through new technology, scarcity is gone. On the Internet, everyone can publish.; At the intersection of "everyone can publish" and "only large institutions deliver news" a new concept is emerging. Information can now be gathered and curated by traditional media institutions from multiple individual publishers at low cost and high speed.; This gathering practice is called crowdsourcing, the "use of technology to foster unprecedented levels of collaboration and meaningful exchanges between people from every imaginable background in every imaginable geographical location." Twitter, a leading technology in the real-time delivery of information from millions of individual sources, provides numerous case studies of traditional news institutions using crowdsourcing to supplement their reporting. Two such examples are Andy Carvin's work curating the tweets of the Egyptian revolution for NPR and the PBS Newshour's use of the hashtag #TSATime to measure the impact of new security regulations during the 2010 holiday season.; These developments raise an important question: Considering Twitter's increasing role as a source of real-time information, how can traditional media outlets best use crowdsourced information from Twitter to supplement their own reporting, parsing out fact from rumor?; By applying a series of social network analysis measurements to the collective tweets of January's Egyptian revolution , this thesis answers the question by recommending measures that can be applied to Twitter's network of individual contributors to identify the sources closest to a given story. Based on this analysis, the thesis argues that reliable sources can be identified by the aggregation of a diverse, independent, decentralized crowd of individuals, but only if traditional media does not overwhelm Twitter with unverified information before the collection of the data occurs. The sources that emerge from the application of social network analysis can assist traditional media outlets in verifying the details of a breaking story, enabling reporting that is both more comprehensive and more informative than previous methods of sourcing made possible.
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