Harm-based morality, focused on suffering and care, rose sharply after 1980. Ingroup-based morality, emphasizing group loyalty, rose steadily over the 20 th century. Words conveying general morality (e.g., good, bad, moral, evil), and those representing Purity-based morality, implicating sanctity and contagion, declined steeply in frequency from 1900 to around 1980, when they rebounded sharply. Each moral language set displayed unique, often nonlinear historical trajectories. Relative frequencies of 304 moral terms, organized into six validated sets corresponding to general morality and the five moral domains proposed by moral foundations theory, were charted for the years 1900 to 2007. Trends in the cultural salience of morality across the 20 th century in the Anglophone world, as reflected in changing use of moral language, were explored using the Google Books (English language) database.
Our results are the first finding relating internally valid measures of moral concerns to observations of language, motivating several new avenues for exploring and investigating how the moral domain intersects with language usage. In follow-up analyses, each moral concern was found to be related to distinct patterns of relational, emotional, and social language. Across a diverse selection of Natural Language Processing methods, cross-validated R2 values ranged from 0.04 for predicting Fairness concerns to 0.21for predicting Purity concerns.
Overall, we found consistent evidence that participants’ self-reported moral concerns can be predicted from their language, though the magnitude of this effect varied considerably among concerns. In order to understand how moral concerns relate to observed language usage, we paired Facebook status updates (N = 107,798) from English-speaking participants (n = 2,691) with their responses on the Moral Foundations Questionnaire, which measures Care, Fairness, Loyalty, Authority, and Purity concerns. While it is often used in moral psychology as an intermediary between researcher and participant, much of the human experience that occurs through language - our relationships, conversations, and, in general, the everyday transmission of our thoughts - has yet to be studied in association with moral concerns. Language is a psychologically rich medium for human expression and communication. We validate the performance on an annotated twitter dataset and then use it to quantify the framing bias and partisanship of news. We propose an unsupervised method that extracts the framing Bias and the framing Intensity without any external framing annotations provided. This theory is a psychological concept which explains how every kind of morality and opinion can be summarized and presented with five main dimensions. We capture the frames based on the Moral Foundation Theory. In this paper, we describe methods for characterizing moral frames in the news.
Therefore, understanding the framing in the news stories is fundamental for realizing what kind of view the writer is conveying with each news story. This framing implicitly shows their biases and also affects the reader's opinion and understanding. However, outlets with different political slants, when talking about the same news story, usually emphasize various aspects and choose their language framing differently. News outlets are a primary source for many people to learn what is going on in the world.
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