Headlines from written news media constitute an important source of information about current affairs. įunding: The author(s) received no specific funding for this work.Ĭompeting interests: The authors have declared that no competing interests exist. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: The URLs sources of articles’ headlines, the Transformer models used for sentiment/emotion predictions, the sentiment and emotion labels annotations generated by the Transformer language models for each headline, the human sentiment/emotion annotations for a small subset of headlines used as ground truth to evaluate models’ performance and the analysis scripts are available in the following repository. Received: JanuAccepted: OctoPublished: October 18, 2022Ĭopyright: © 2022 Rozado et al. PLoS ONE 17(10):Įditor: Sergio Consoli, European Commission, ITALY The prevalence of headlines denoting anger appears to be higher, on average, in right-leaning news outlets than in left-leaning news media.Ĭitation: Rozado D, Hughes R, Halberstadt J (2022) Longitudinal analysis of sentiment and emotion in news media headlines using automated labelling with Transformer language models. The chronological analysis of headlines emotionality shows a growing proportion of headlines denoting anger, fear, disgust and sadness and a decrease in the prevalence of emotionally neutral headlines across the studied outlets over the 2000–2019 interval. Headlines from right-leaning news media have been, on average, consistently more negative than headlines from left-leaning outlets over the entire studied time period. Results show an increase of sentiment negativity in headlines across written news media since the year 2000. We use Transformer language models fine-tuned for detection of sentiment (positive, negative) and Ekman’s six basic emotions (anger, disgust, fear, joy, sadness, surprise) plus neutral to automatically label the headlines. This work describes a chronological (2000–2019) analysis of sentiment and emotion in 23 million headlines from 47 news media outlets popular in the United States.
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