JOURNAL ARTICLE

Examining semantic (dis)similarity in news through news organizations' ideological similarity, similarity in truthfulness, and public engagement on social media: a network approach.

  • Published In: Human Communication Research, 2023, v. 49, n. 1. P. 47 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Li, Yue; Bond, Robert M 3 of 3

Abstract

This article investigates the processes underlying semantic divergence in U.S. news coverage, focusing on how similarity in media ideology, news truthfulness, and public engagement on social media influence the language used by news organizations. Using a network approach that integrates news organizations, content, and audiences, the study analyzes text and social media engagement data from over 100 news outlets covering mask policies during the COVID-19 pandemic. Findings indicate that news organizations closer in media ideology and news truthfulness produce more semantically similar content, contributing to news polarization, while higher public engagement on social media is associated with decreased semantic similarity across outlets. The study advances mass communication theory by emphasizing relational structures over individual attributes and demonstrates a methodological integration of network analysis with natural language processing and social media data.

Additional Information

  • Source:Human Communication Research. 2023/01, Vol. 49, Issue 1, p47
  • Document Type:Article
  • Subject Area:Communication and Mass Media
  • Publication Date:2023
  • ISSN:0360-3989
  • DOI:10.1093/hcr/hqac020
  • Accession Number:161161748
  • Copyright Statement:Copyright of Human Communication Research is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.