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Quantifying the qualities of language.

ABSTRACT: We here quantify the qualities of language. Specifically, we derive numerical values, and associated variability scores for statements of general probability as well as for anchor terms commonly expressed on Likert-type scales. Our results show significant inter-responder variability in the understanding of such terms. We also demonstrate scale compression such that average estimates are poorly aligned to the a priori expectations of equal response spacing in commonly used verbal response instruments. Terms further show intriguing propensities such that people agree more about statements of agreement, but manifestly disagree more about statements of disagreement. The valence of these terms is thus systematically related to their degree of common understanding. The accuracy of language, especially when used in conjunction with modern advanced technologies, proves vital for effective communication. Our work demonstrates that the tool of language still remains a fairly blunt one, but one that can be sharpened by systematic quantitative evaluation, as we demonstrate here.

SUBMITTER: Hancock PA

PROVIDER: S-EPMC7199931 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundMental health challenges are thought to affect approximately 10% of the global population each year, with many of those affected going untreated because of the stigma and limited access to services. As social media lowers the barrier for joining difficult conversations and finding supportive groups, Twitter is an open source of language data describing the changing experience of a stigmatized group.ObjectiveBy measuring changes in the conversation around mental health on Twitter, we aim to quantify the hypothesized increase in discussions and awareness of the topic as well as the corresponding reduction in stigma around mental health.MethodsWe explored trends in words and phrases related to mental health through a collection of 1-, 2-, and 3-grams parsed from a data stream of approximately 10% of all English tweets from 2010 to 2021. We examined temporal dynamics of mental health language and measured levels of positivity of the messages. Finally, we used the ratio of original tweets to retweets to quantify the fraction of appearances of mental health language that was due to social amplification.ResultsWe found that the popularity of the phrase mental health increased by nearly two orders of magnitude between 2012 and 2018. We observed that mentions of mental health spiked annually and reliably because of mental health awareness campaigns as well as unpredictably in response to mass shootings, celebrities dying by suicide, and popular fictional television stories portraying suicide. We found that the level of positivity of messages containing mental health, while stable through the growth period, has declined recently. Finally, we observed that since 2015, mentions of mental health have become increasingly due to retweets, suggesting that the stigma associated with the discussion of mental health on Twitter has diminished with time.ConclusionsThese results provide useful texture regarding the growing conversation around mental health on Twitter and suggest that more awareness and acceptance has been brought to the topic compared with past years.

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Quantifying the qualities of language.

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