Visualization, Technologies, or the Public? Exploring the articulation of data-driven journalism in the Twittersphere

Xinzhi ZHANG*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Data(-driven) journalism has triggered debates about whether this innovative storytelling and investigative approach, using data analytical and computational methods, better serves the public. Applying the concept of articulation, wherein an array of terms are juxtaposed and expressed together, this paper examines how the term “data-driven journalism” is represented on social media. Focusing on the Twittersphere as the research context, the paper employed the Twitter search application programming interface (API) to harvest all available public tweets (N = 6951) containing hashtags or keywords related to data-driven journalism within a four-week period in late 2016. A text-mining analysis of the contents of these tweets found that they focused extensively on journalistic practices, data visualization, and data analytical techniques. Further analysis on the hashtag co-occurrence network revealed that a number of hashtags bridged and organized the discussion of data-driven journalism in the Twittersphere. Some hashtags on technologies and commercial applications, such as “#dataviz,” “#bigdata,” and “#datajournalism,” were located at important positions in the network. In contrast, public-related terms, such as “#opendata” or “#opengovernment,” were mentioned in a limited way and positioned peripherally. Implications for journalism and society are discussed.

Original languageEnglish
Pages (from-to)737-758
Number of pages22
JournalDigital Journalism
Volume6
Issue number6
DOIs
Publication statusPublished - 3 Jul 2018

Scopus Subject Areas

  • Communication

User-Defined Keywords

  • data-driven journalism
  • hashtag co-occurrence network
  • social network analysis
  • text mining
  • topic modeling
  • Twittersphere

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