Media: Since 2000, news headlines became increasingly negative, angry, sad and fearful
Abstract
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. 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. Results show an increase of sentiment negativity in headlines across written news media since the year 2000. 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. 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. The prevalence of headlines denoting anger appears to be higher, on average, in right-leaning news outlets than in left-leaning news media.
The reason is because emotional jolts per minute increases the viral-ness of stories on social media – meaning more comments and more sharing of the story.
The sentiment and emotionality of text has been shown to influence its virality [4]. Textual content that evokes high arousal, such as text conveying an emotion of anger, diffuses more profusely through online platforms [5, 6]. Emotionally charged fake news also spread further and fastest through social media [7]. A study measuring the reach of tweets found that each moral or emotional word used in a tweet increased its virality by 20 percent, on average [8]. Thus, user engagement can be maximized by news articles posts that trigger negative sentiment/emotions [9]. This creates a financial incentive for news outlets to maximize incoming web traffic by modulating the emotional saliency of headlines.