Johannes C. Eichstaedt

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 cooccurring words derived from the tweets using LDA, improved accuracy in predicting life satisfaction over and above standard demographic and(More)
Standardized frequency of topics and words across age. A. The best topic for each of the 4 age groups. B. Select social topics. C. ‘I’ and ‘we’ unigrams. Conclusions • A case-study on analyzing language in social media for psychological insight: – some results were known or obvious: ∗ extraverts mention ‘party’ ∗ neuroticism and ‘depressed’ – other revealed(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)
Age and Gender on Social Media Language, behavior and health correlate with age and gender • Women tend to live longer (CDC, 2014); people, with age, tend to be more agreeable, more conscientious and less open to experience (McCrae et al. 1999) • most social scientific studies on social media data use biased samples of age and gender There is a need for(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)
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 Twitter. Language-derived personality and(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)
Access to expressions of subjective personal posts increased with the popularity of Social Media. However, most of the work in sentiment analysis focuses on predicting only valence from text and usually targeted at a product, rather than affective states. In this paper, we introduce a new data set of 2895 Social Media posts rated by two(More)
People vary widely in their temporal orientation—how often they emphasize the past, present, and future—and this affects their finances, health, and happiness. Traditionally, temporal orientation has been assessed by self-report questionnaires. In this paper, we develop a novel behavior-based assessment using human language on Facebook. We first create a(More)