Study Shows All Languages Skew Towards Happiness
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Yes, even on Twitter, more words—of all languages—are positive than negative.
A team of scientists at the University of Vermont and The MITRE Corporation set out to test the 1969 Pollyanna Hypothesis—which posited that human language skews positive, indicating a overall optimist outlook—by tracking many billions of words across 10 languages and 24 types of sources including books, news outlets, social media, websites, television and movie subtitles, and music lyrics. The researchers published their result in a paper called «Human Language Reveals a Universal Positivity Bias,» which appeared in the February 9 online edition of the Proceedings of the National Academy of Sciences.
According to UVM mathematician Peter Dodds, who co-led the study, this huge study of the «atoms of language—individual words» indicates that language itself—perhaps humanity’s greatest technology—has a positive outlook. And, therefore, «it seems that positive social interaction» is built into its fundamental structure.
To create a scale, the team identified about 10,000 of the most frequently used words in each of 10 languages, then had native speakers rate each word—for a total of five million individual ratings—on a nine-point scale of positivity. These ratings were averaged to create a score for each word; in English, for example, «laughter» rated 8.50, «food» 7.44, «truck» 5.48, «the» 4.98, «greed» 3.06, and «terrorist» 1.30.
The study found that a Google web crawl of Spanish-language sites had the highest average word happiness, and a search of Chinese books had the lowest—but more importantly, all 24 sources and all languages scored above a five (a neutral score). Or, as UVM mathematician Chris Danforth, who co-led the new research, puts it, «[we] use more happy words than sad words.»
This new research contributes to the same group of scientists’ work on a global «hedonometer»—a happiness meter based on language use. They’re currently able to track world-wide, and even city-specific, happiness based on Twitter language, but hope to expand beyond the social media platform.