2015/04/09 by Stuart Soroka, Lori Young, Meital Balmas · 130 citations
Computer Science · Mathematics · Psychology · Social Sciences · #Affect (linguistics) #Anger #Cognitive psychology #Communication #Computer science #Construct (python library) #Content (measure theory) #Content analysis #Humor Studies and Applications #Mathematics #Misinformation and Its Impacts #Negativity effect #Psychology #Sentiment Analysis and Opinion Mining #Social psychology #Sociology
paper · doi:10.1177/0002716215569217
published in The Annals of the American Academy of Political and Social Science 659(1), 108-121 (SAGE Publishing)
openalex publication_date 2015/04/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30
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.