Learn More
We analyzed 700 million words, phrases, and topic instances collected from the Facebook messages of 75,000 volunteers, who also took standard personality tests, and found striking variations in language with personality, gender, and age. In our open-vocabulary technique, the data itself drives a comprehensive exploration of language that distinguishes(More)
The language used in tweets from 1,300 different US counties was found to be predictive of the subjective well-being of people living in those counties as measured by representative surveys. Topics, sets of co-occurring words derived from the tweets using LDA, improved accuracy in predicting life satisfaction over and above standard demographic and(More)
Demographic lexica have potential for widespread use in social science, economic, and business applications. We derive predic-tive lexica (words and weights) for age and gender using regression and classification models from word usage in Facebook, blog, and Twitter data with associated demographic labels. The lexica, made publicly available, 1 achieved(More)
Although social media are widely studied, computational linguistics typically focuses on prediction tasks: • sentiment analysis • authorship attribution • personality prediction. .. Language analysis in social media can also be used to gain psychological insight. • 74,941 volunteers shared their gender and age, and took a personality questionnaire • 14.3m(More)
Hostility and chronic stress are known risk factors for heart disease, but they are costly to assess on a large scale. We used language expressed on Twitter to characterize community-level psychological correlates of age-adjusted mortality from atherosclerotic heart disease (AHD). Language patterns reflecting negative social relationships, disengagement,(More)
Language use is a psychologically rich, stable individual difference with well-established correlations to personality. We describe a method for assessing personality using an open-vocabulary analysis of language from social media. We compiled the written language from 66,732 Facebook users and their questionnaire-based self-reported Big Five personality(More)
Depression is typically diagnosed as being present or absent. However, depression severity is believed to be continuously distributed rather than dichotomous. Severity may vary for a given patient daily and seasonally as a function of many variables ranging from life events to environmental factors. Repeated population-scale assessment of depression through(More)
OBJECTIVE We present a new open language analysis approach that identifies and visually summarizes the dominant naturally occurring words and phrases that most distinguished each Big Five personality trait. METHOD Using millions of posts from 69,792 Facebook users, we examined the correlation of personality traits with online word usage. Our analysis(More)
We introduce a new method, differential language analysis (DLA), for studying human development in which computational linguistics are used to analyze the big data available through online social media in light of psychological theory. Our open vocabulary DLA approach finds words, phrases, and topics that distinguish groups of people based on 1 or more(More)
Mental illnesses, such as depression and post traumatic stress disorder (PTSD), are highly underdiagnosed globally. Populations sharing similar demographics and personality traits are known to be more at risk than others. In this study, we characterise the language use of users disclosing their mental illness on Twit-ter. Language-derived personality and(More)