Despite F-bombs, human languages exhibit clear 'positive' bias

A new research has revealed that human languages exhibit a clear positive bias across cultures far and wide, suggesting that people use more positive than negative words.

A team of scientists at the University of Vermont and The MITRE Corporation the researchers found that movie subtitles in Arabic, Twitter feeds in Korean, the famously dark literature of Russia, websites in Chinese, music lyrics in English and even the war-torn pages of the New York Times and probably all human language, skew toward the use of happy words.

UVM mathematician Peter Dodds said that they looked at ten languages and in every source we looked at, people use more positive words than negative ones.

This huge study of the atoms of language and 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, says Dodds.

A Google web crawl of Spanish-language sites had the highest average word happiness, and a search of Chinese books had the lowest, but all twenty-four sources of words that they analyzed skewed above the neutral score of five on their one-to-nine scale, regardless of the language.

In every language, neutral words like "the" scored just where you would expect: in the middle, near five and when the team translated words between languages and then back again they found that "the estimated emotional content of words is consistent between languages.

In all cases, the scientists found "a usage-invariant positivity bias," as they write in the study, in other words, by looking at the words people actually use most often they found that, on average, humanity "use more happy words than sad words," says UVM mathematician Chris Danforth says.

This new research study also describes a larger project that the team of fourteen scientists has developed to create "physical-like instruments" for both real-time and offline measurements of the happiness in large-scale texts "basically, huge bags of words," Danforth explains.

They call this instrument a "hedonometer," a happiness meter, which can now trace the global happiness signal from English-language Twitter posts on a near-real-time basis, and show differing happiness signals between days. For example, a big drop was noted on the day of the terrorist attack on Charlie Hebdo in Paris, but rebounded over the following three days.

The hedonometer can also discern different happiness signals in US states and cities: Vermont currently has the happiest signal, while Louisiana has the saddest. And the latest data puts Boulder, CO, in the number one spot for happiness, while Racine, WI, is at the bottom.

However, as the new paper describes, the team is working to apply the hedonometer to explore happiness signals in many other languages and from many sources beyond Twitter.

The new research "in no way asserts that all natural texts will skew positive," the researchers write, as these various books reveal, but at a more elemental level, the study brings evidence from Big Data to a long-standing debate about human evolution: our social nature appears to be encoded in the building blocks of language.

The study is published in the Proceedings of the National Academy of Sciences.