The Increasing Negativity and Emotionality of News Media Headlines

The author says, “I have recently published a paper where we describe a chronological (2000–2019) analysis of sentiment and emotion in 23 million headlines from 47 news media outlets popular in the United States. We used Transformer language models fine-tuned for detection of sentiment (positive, negative) and emotions (anger, disgust, fear, joy, sadness, neutral) to automatically label the headlines.”

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