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News Organizations' News Link Sharing Strategies on Twitter: Economic Theory and Computational Text Analysis.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	News Organizations' News Link Sharing Strategies on Twitter: Economic Theory and Computational Text Analysis./
作者:	Pak, Chankyung.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	162 p.
附註:	Source: Dissertations Abstracts International, Volume: 80-06, Section: A.
Contained By:	Dissertations Abstracts International80-06A.
標題:	Economics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13422405
ISBN:	9780438737037

News Organizations' News Link Sharing Strategies on Twitter: Economic Theory and Computational Text Analysis.
Pak, Chankyung.

News Organizations' News Link Sharing Strategies on Twitter: Economic Theory and Computational Text Analysis. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 162 p.

Source: Dissertations Abstracts International, Volume: 80-06, Section: A.

Thesis (Ph.D.)--Michigan State University, 2018.

This item must not be added to any third party search indexes.

This dissertation explores news organizations' social media strategies to disseminate their news stories as emergent quasi-editorial decisions. As in traditional editorial decisions, how news organizations share their news links on social media determines the visibility of certain stories and makes certain aspects of news stand out or be unobtrusive. By illustrating how these practices on Twitter resemble and deviate from traditional ones, I argue that social media open up a new path by which news organizations mediate news information, relatively free from journalistic norms and routines embedded in the older media and traditional editorial processes. To answer a fundamental question, ?Is there a reason for a news organization to be strategic on social media?? I presented an economic model based on competition for limited attention of social media users. In this model, the scarcity of users' attention capacity relative to the volume of information propagated via social media creates competition between news\\ organizations. The model illustrates that one news organization's attempt to capture users' attention undermines the chance for other organizations to do so. Thus, a news organization should strategically decide how many news links it is going to share considering how many others would share. A simple empirical test confirms the model's prediction that news organizations will reduce the proportion of news links they share on social media as more news is published by all organizations. Computational text analyses shed light on the more qualitative aspects of news dissemination strategies on social media. First, using a recently developed machine learning technique, Structural Topic Model (STM), I investigate news organizations' selective news link sharing as a new layer of gatekeeping. The result indicates that the common concern that commercialized media drives news toward human interest rather than newsworthiness is crystallized more visibly on Twitter than on news websites. Further, a comparison of the selective link sharing across different media types shows that topic selection differs depending on a given topic's popularity on Twitter and a news organization's specialty in the topic. Even though a news organization may consider a certain topic to be important in its editorial decision, so that the organization has become specialized in that topic throughout its history, it would not share much about the topic on Twitter because popularity in the short term dominates link sharing strategies. I found that regional media convey less negative sentiment through news stories than other types of news organizations. This seems to be associated with the less controversial news topics they frequently cover compared to national and online media. However, news paraphrasing for Twitter homogenizes emotional framing across different types of organizations. In particular, regional media catch up to other types by adding even more negativity on news paraphrases for Twitter. This finding provides another significant indication that social media strategies are governed by different logic than that which governs traditional editorial practices. Major empirical findings provide evidence that the social media strategies of news organizations are already functioning as a separate information-mediating process. I argue that the distinctiveness of social media strategies as quasi-editorial decisions raises a practical need to publicly monitor news organizations' behaviors on social media to learn whether they will provide news that is informative and diverse enough for news readers' informed decisions. The automated data collection schemes and computational text analysis techniques I adopted in this dissertation will inform the design of infrastructure for such public monitoring.

ISBN: 9780438737037Subjects--Topical Terms:

517137
Economics.

News Organizations' News Link Sharing Strategies on Twitter: Economic Theory and Computational Text Analysis.
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506 $a This item must not be added to any third party search indexes.
506 $a This item must not be sold to any third party vendors.
520 $a This dissertation explores news organizations' social media strategies to disseminate their news stories as emergent quasi-editorial decisions. As in traditional editorial decisions, how news organizations share their news links on social media determines the visibility of certain stories and makes certain aspects of news stand out or be unobtrusive. By illustrating how these practices on Twitter resemble and deviate from traditional ones, I argue that social media open up a new path by which news organizations mediate news information, relatively free from journalistic norms and routines embedded in the older media and traditional editorial processes. To answer a fundamental question, ?Is there a reason for a news organization to be strategic on social media?? I presented an economic model based on competition for limited attention of social media users. In this model, the scarcity of users' attention capacity relative to the volume of information propagated via social media creates competition between news\\ organizations. The model illustrates that one news organization's attempt to capture users' attention undermines the chance for other organizations to do so. Thus, a news organization should strategically decide how many news links it is going to share considering how many others would share. A simple empirical test confirms the model's prediction that news organizations will reduce the proportion of news links they share on social media as more news is published by all organizations. Computational text analyses shed light on the more qualitative aspects of news dissemination strategies on social media. First, using a recently developed machine learning technique, Structural Topic Model (STM), I investigate news organizations' selective news link sharing as a new layer of gatekeeping. The result indicates that the common concern that commercialized media drives news toward human interest rather than newsworthiness is crystallized more visibly on Twitter than on news websites. Further, a comparison of the selective link sharing across different media types shows that topic selection differs depending on a given topic's popularity on Twitter and a news organization's specialty in the topic. Even though a news organization may consider a certain topic to be important in its editorial decision, so that the organization has become specialized in that topic throughout its history, it would not share much about the topic on Twitter because popularity in the short term dominates link sharing strategies. I found that regional media convey less negative sentiment through news stories than other types of news organizations. This seems to be associated with the less controversial news topics they frequently cover compared to national and online media. However, news paraphrasing for Twitter homogenizes emotional framing across different types of organizations. In particular, regional media catch up to other types by adding even more negativity on news paraphrases for Twitter. This finding provides another significant indication that social media strategies are governed by different logic than that which governs traditional editorial practices. Major empirical findings provide evidence that the social media strategies of news organizations are already functioning as a separate information-mediating process. I argue that the distinctiveness of social media strategies as quasi-editorial decisions raises a practical need to publicly monitor news organizations' behaviors on social media to learn whether they will provide news that is informative and diverse enough for news readers' informed decisions. The automated data collection schemes and computational text analysis techniques I adopted in this dissertation will inform the design of infrastructure for such public monitoring.
590 $a School code: 0128.
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856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13422405

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