Bad News or Mad News? Sentiment Scoring of Negativity, Fear, and Anger in News Content

Stuart Soroka, Lori Young, Meital Balmas

Research output: Contribution to journalArticlepeer-review

79 Scopus citations

Abstract

This article examines the prevalence and nature of negativity in news content. Using dictionary-based sentiment analysis, we examine roughly fifty-five thousand front-page news stories, comparing four different affect lexicons, one for general negativity, and three capturing different measures of fear and anger. We show that fear and anger are distinct measures that capture different sentiments. It may therefore be possible to separate out fear and anger in media content, as in psychology. We also find that negativity is more strongly related to anger than to fear for each measure. This result appears to be driven by a small number of foreign policy words in the anger dictionaries, rather than an indication that negativity in U.S. coverage reflects “anger.” We highlight the importance of tailoring lexicons to domains to improve construct validity when conducting dictionary-based automation. Finally, we connect these results to existing work on the impact of emotion on political preferences and reasoning.

Original languageAmerican English
Pages (from-to)108-121
Number of pages14
JournalAnnals of the American Academy of Political and Social Science
Volume659
Issue number1
DOIs
StatePublished - 15 May 2015

Bibliographical note

Publisher Copyright:
© 2015 by The American Academy of Political and Social Science

Keywords

  • dictionary-based sentiment analysis
  • discrete emotions
  • negativity

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