With Shifts in National Mood Come Shifts in Words We Use, Study Suggests


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In the wake of the election, it’s clear American society is fractured. Negative emotions are running amok, and countless words of anger and frustration have been spilled. If you were to analyze this news outlet for the ratio of positive emotional words to negative ones, would you find a dip linked to the events of the past few weeks?

It’s possible, suggests a study published last week in Proceedings of the National Academy of Sciences. Analyzing Google Books and The New York Times’s archives from the last 200 years, the researchers examined a curious phenomenon known as “positive linguistic bias,” which refers to people’s tendency to use more positive words than negative words. Though the bias is robust — and found consistently across cultures and languages — social scientists are at odds about what causes it.

In this study, the authors shed light on some possible new patterns behind the effect. Across two centuries’ of texts, they found that people’s preference for positive words varied with national mood, and declined during times of war and economic hardship.

“It’s been shown that linguistic positivity bias exists, over and over again. What people haven’t actually looked at is how this phenomenon fluctuates over time, and whether there are certain predictors for it,” said Morteza Dehghani, a professor of psychology and computer science at the University of Southern California and an author of the paper.

To measure linguistic positivity, Dr. Dehghani’s team looked at catalogs of words associated with positive and negative emotions, from a collection called the linguistic inquiry and word count, or LIWC, database. The positive category included about 400 words, including “awesome,” “pretty” and “grace.” The negative one included about 500 words, including “suffer,” “grief” and “hatred.”

Then the researchers looked at how many times these positive and negative words appeared each year, across 1.3 million texts in Google Books and 14.9 million New York Times articles. They also analyzed word usage relative to unemployment and inflation rates, wartime casualty estimates and national happiness surveys.

Looking for changes over time can provide clues about the mechanism behind the linguistic positivity bias, said William Hamilton, a doctoral candidate at Stanford University who focuses on linguistic trends and was not involved in the study.

Many theories have been proposed: Maybe it’s because we’re social creatures, and affirmative language promotes group bonding and cooperation. Maybe we inherently privilege positive information. Maybe, optimistically, more good things than bad things happen overall, and the words we use reflect that.

“When you’re looking at a static snapshot of time, it’s hard to disentangle all these competing hypotheses,” Mr. Hamilton said.

The new study provides evidence that positive language use may change depending on objective circumstances, such as war and poverty, as well as subjective happiness. What may be less compelling is the researchers’ finding that there is an overall decrease in positive language use over the last 200 years.

Tools like the LIWC database were developed around “the way people write and talk today,” said Mark Liberman, a linguistics professor at the University of Pennsylvania who was not part of the study. As a result, the database doesn’t capture changes in word meanings and frequency of use. Over time the word “awesome,” for instance, changed from meaning “daunting” to being synonymous with “good.”

Additionally, experts in linguistics and textual analysis say that the composition of text collections like Google Books change over time, confounding attempts to extract chronological patterns.

“It’s a compelling trend they find,” Mr. Hamilton said, “but there needs to be more follow-ups for me to be totally confident this is something that’s happening.”

Rumen Iliev, a psychology researcher at Stanford University and a co-author of the paper, said these concerns are legitimate, but that this study is just the beginning.

“We hope that our research will generate novel research which will use both different dictionaries and different databases,” he said.

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