Exploring the Fragmentation of the Representation of Data-Driven Journalism in the Twittersphere: A Network Analytics Approach

As an interdisciplinary field, data-driven journalism integrates the intellectual origins of investigative journalism, computer-assisted reporting, and the emerging paradigm of computational social science. Studies of news production have revealed, however, that news professionals are reinforcing ex...

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Published in:Social science computer review Vol. 40; no. 1; pp. 42 - 60
Main Authors: Zhang, Xinzhi, Ho, Jeffrey C. F.
Format: Journal Article
Language:English
Published: Los Angeles, CA SAGE Publications 01.02.2022
SAGE PUBLICATIONS, INC
Subjects:
Computer mediated communication
Data
Fragmentation
Geographical locations
Interdisciplinary aspects
Journalism
Mass media images
Networks
News
News media
Power structure
Production
Professional associations
Public sphere
Real time
Representations
Segmentation
Social factors
Social interaction
Social media
Social sciences
data-driven journalism
political polarization
representational network
exponential random graph model (ERGM)
co-retweeted network
social media
interpretive community
ISSN:0894-4393, 1552-8286
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Abstract As an interdisciplinary field, data-driven journalism integrates the intellectual origins of investigative journalism, computer-assisted reporting, and the emerging paradigm of computational social science. Studies of news production have revealed, however, that news professionals are reinforcing existing power structures via an interpretive community, where homophily-evoked social interactions—even in the social media context—create echo chambers and discussion fragmentation. Is the representation of data-driven journalism in the electronic public sphere breaking boundaries among people from different domains or does it resemble the existing power structure? This study adopts a network analytics approach and constructs a representational network among actors who joined the public discussion of data-driven journalism in the Twittersphere—the co-retweeted network—such that two accounts are connected if their tweets are retweeted by the same user. Public tweets containing search queries related to data-driven journalism published from February 2017 to February 2018 were collected with Twitter real-time streaming application programming interface (API). A co-retweeted network with 1,148 accounts was derived from verified accounts’ retweeting posts. Results found that several communities emerged, and news organizations, nongovernmental and nonprofit professional organizations, and academic institutions were in the crucial positions of the network. The exponential random graph models (ERGMs) based on this network revealed the extent to which gender, geographical location, and institutional type of the users were associated with the tie-formation. This study documents the major actors who are discussing the subject of data-driven journalism and raises critical reflections toward the interdisciplinary collaboration in the production of public knowledge.
AbstractList As an interdisciplinary field, data-driven journalism integrates the intellectual origins of investigative journalism, computer-assisted reporting, and the emerging paradigm of computational social science. Studies of news production have revealed, however, that news professionals are reinforcing existing power structures via an interpretive community, where homophily-evoked social interactions—even in the social media context—create echo chambers and discussion fragmentation. Is the representation of data-driven journalism in the electronic public sphere breaking boundaries among people from different domains or does it resemble the existing power structure? This study adopts a network analytics approach and constructs a representational network among actors who joined the public discussion of data-driven journalism in the Twittersphere—the co-retweeted network—such that two accounts are connected if their tweets are retweeted by the same user. Public tweets containing search queries related to data-driven journalism published from February 2017 to February 2018 were collected with Twitter real-time streaming application programming interface (API). A co-retweeted network with 1,148 accounts was derived from verified accounts’ retweeting posts. Results found that several communities emerged, and news organizations, nongovernmental and nonprofit professional organizations, and academic institutions were in the crucial positions of the network. The exponential random graph models (ERGMs) based on this network revealed the extent to which gender, geographical location, and institutional type of the users were associated with the tie-formation. This study documents the major actors who are discussing the subject of data-driven journalism and raises critical reflections toward the interdisciplinary collaboration in the production of public knowledge.
Author Zhang, Xinzhi
Ho, Jeffrey C. F.
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Keywords data-driven journalism
political polarization
representational network
exponential random graph model (ERGM)
co-retweeted network
social media
interpretive community
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Snippet As an interdisciplinary field, data-driven journalism integrates the intellectual origins of investigative journalism, computer-assisted reporting, and the...
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SubjectTerms Computer mediated communication
Data
Fragmentation
Geographical locations
Interdisciplinary aspects
Journalism
Mass media images
Networks
News
News media
Power structure
Production
Professional associations
Public sphere
Real time
Representations
Segmentation
Social factors
Social interaction
Social media
Social sciences
Title Exploring the Fragmentation of the Representation of Data-Driven Journalism in the Twittersphere: A Network Analytics Approach
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Volume 40
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