7/17/2023 0 Comments Bias by headline examples![]() ![]() Modern machine learning language models constitute an important tool for the automated analysis of text. Computational content analysis techniques circumvent some of the limitations of content analysis using human raters by permitting the quantification of textual attributes in vast text corpora. Unfortunately, this approach is limited by its inability to scale to large corpora and by low intercoder reliability when examining subtle themes. Here, we attempt to remedy this knowledge gap by documenting chronologically the sentiment and emotion of headlines in a representative sample of news media outlets.Įxamining written sources using human coders has been useful in the sociological analysis of text content. As far as we can tell however, a comprehensive longitudinal analysis of news media headlines sentiment and emotion remains lacking in the existing literature. Thus, studying the sentiment (positive/negative) and emotional payload (anger, disgust, fear, joy, sadness, surprise or neutral) of news articles headlines is of sociological interest. News content has also been shown to be predictive of public mood, public opinion and outlets’ biases. This creates a financial incentive for news outlets to maximize incoming web traffic by modulating the emotional saliency of headlines. Thus, user engagement can be maximized by news articles posts that trigger negative sentiment/emotions. 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. Emotionally charged fake news also spread further and fastest through social media. Textual content that evokes high arousal, such as text conveying an emotion of anger, diffuses more profusely through online platforms. The sentiment and emotionality of text has been shown to influence its virality. In doing so, headlines also set the tone about the main text body of the article and affect readers’ processing of articles’ content to the point of constraining further information processing and biasing readers towards specific interpretations of the article. News and opinion articles headlines often establish the first point of contact between an article and potential readers, with the reader often deciding whether to engage more in-depth with an article’s content after evaluating its headline. Headlines from written news media constitute an important source of information about current affairs. The prevalence of headlines denoting anger appears to be higher, on average, in right-leaning news outlets than in left-leaning news media. 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. While this market reaction is, to some extent, natural and expected, the headline effect can speed up and worsen the severity of the market reaction by bringing bad news to the forefront of the trading public's mind.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. Therefore, when a government agency or central bank releases an unfavorable economic report, traders, investors, and members of the investing public might disproportionately react to that bad news by converting, selling, or shorting funds away from any stocks, currencies, or other investments that have been affected. ![]() ![]() Whether it is justified or not, the investing public's reaction to a headline can be very dramatic and out of proportion when compared with the reaction to good news in the headlines. Understanding the Headline Effect Extension of the Headline Effect
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