Health Journalists’ Social Media Sourcing During the Early Outbreak of the Public Health Emergency

Xinzhi Zhang*, Rui Zhu

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

2 Citations (Scopus)

Abstract

Although journalists’ social media sourcing can empower non-elite sources and diversify public discussions, counterarguments maintain that social media sourcing relies on a small group of elites and reinforces social division. To contribute to that debate, we examined how health journalists from the mainstream news organizations in the U.S. used Twitter’s @mention for sourcing during the first three months of the COVID-19 outbreak. Using a sample of public Twitter posts published by the journalists, we formed co-@mentioned networks (i.e., two sources were connected if @mentioned in the same post) to examine the structure of the networks and identify important sourcing informants. Among the results, elite sources (e.g., health journalists and health experts in the public sector) and influential users (i.e., verified users with a large number of followers and who post frequently) dominated the sourcing repertoire. Moreover, the networks were fragmented because the sources were clustered into several close-knit subgroups. Analyzing exponential random graph models to examine the formation mechanism of the networks revealed that, as the pandemic’s severity increased, influential users played a more salient role in the sourcing repertoire, and a homogeneous cluster consisting of journalists and news organizations emerged. © 2022 Informa UK Limited, trading as Taylor & Francis Group.
Original languageEnglish
Pages (from-to)1660-1680
JournalJournalism Practice
Volume18
Issue number7
Online published22 Aug 2022
DOIs
Publication statusPublished - 2024
Externally publishedYes

Funding

This work is supported by the General Research Fund (GRF) from the Research Grants Council (RGC) of the Hong Kong SAR (no. 12602420) granted to the first author. The authors also appreciate the help from Bingyang Chu, Ziqian Wang, and Yixue Wang.

Research Keywords

  • computational journalism
  • computational social science‌
  • health reporting
  • news sourcing
  • Social media
  • social network analysis

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