Human language reveals a universal positivity bias
Table S7: Pearson correlation coefficients and p-values, Spearman correlation coefficients and p-values, and linear fit coefficients for standard deviation of word happiness hstd as a function of word usage frequency rank r. We consider the fit is hstd=αr+β for the most common 5000 words in each corpora, determining α and β via ordinary least squares, and order corpora according to their emotional variance (descending).
['Language: Corpus Portuguese: Twitter', '[ITALIC] ρp +0.090', '[ITALIC] p-value 2.55×10−14', '[ITALIC] ρs +0.095', '[ITALIC] p-value 1.28×10−15', '[ITALIC] α 1.19×10−5', '[ITALIC] β 1.29']
July 16, 2020 \newmdenv [linecolor=lightgrey,backgroundcolor= white]myframe Human language—our great social technology—reflects that which it describes through the stories it allows to be told, and us, the tellers of those stories. In 1969, Boucher and Osgood framed the Pollyanna Hypothesis: a hypothetical, universal positivity bias in human communication Boucher and Osgood From a selection of small-scale, cross-cultural studies, they marshaled evidence that positive words are likely more prevalent, more meaningful, more diversely used, and more readily learned. However, in being far from an exhaustive, data-driven analysis of language—the approach we take here—their findings could only be regarded as suggestive. To deeply explore the positivity of human language, we constructed 24 corpora spread across 10 languages (see Supplementary Online Material). Our global coverage of linguistically and culturally diverse languages includes English, Spanish, French, German, Brazilian Portuguese, Korean, Chinese (Simplified), Russian, Indonesian, and Arabic. The sources of our corpora are similarly broad, Our work here greatly expands upon our earlier study of English alone, where we found strong evidence for a usage-invariant positivity bias Kloumann et al. We address the social nature of language in two important ways: (1) we focus on the words people most commonly use, and (2) we measure how those same words are received by individuals. We take word usage frequency as the primary organizing measure of a word’s importance. Such a data-driven approach is crucial for both understanding the structure of language and for creating linguistic instruments for principled measurements Dodds et al. By contrast, earlier studies focusing on meaning and emotion have used ‘expert’ generated word lists, and these fail to statistically match frequency distributions of natural language Osgood et al. ; Stone et al. , confounding attempts to make claims about language in general. For each of our corpora we selected between 5,000 to 10,000 of the most frequently used words, choosing the exact numbers so that we obtained approximately 10,000 words for each language. (see also Supplementary Online Material). This happy-sad semantic differential Osgood et al. Participants were restricted to certain regions or countries (for example, Portuguese was rated by residents of Brazil). Overall, we collected 50 ratings per word for a total of around 5,000,000 individual human assessments, and we provide all data sets as part of the Supplementary Online Material.