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Dive into the research topics where Johannes C. Eichstaedt is active.

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Featured researches published by Johannes C. Eichstaedt.


Psychological Science | 2015

Psychological Language on Twitter Predicts County-Level Heart Disease Mortality:

Johannes C. Eichstaedt; Hansen Andrew Schwartz; Margaret L. Kern; Gregory Park; Darwin R. Labarthe; Raina M. Merchant; Sneha Jha; Megha Agrawal; Lukasz Dziurzynski; Maarten Sap; Christopher Weeg; Emily E. Larson; Lyle H. Ungar; Martin E. P. Seligman

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, and negative emotions—especially anger—emerged as risk factors; positive emotions and psychological engagement emerged as protective factors. Most correlations remained significant after controlling for income and education. A cross-sectional regression model based only on Twitter language predicted AHD mortality significantly better than did a model that combined 10 common demographic, socioeconomic, and health risk factors, including smoking, diabetes, hypertension, and obesity. Capturing community psychological characteristics through social media is feasible, and these characteristics are strong markers of cardiovascular mortality at the community level.


Journal of Personality and Social Psychology | 2015

Automatic personality assessment through social media language.

Gregory Park; H. Andrew Schwartz; Johannes C. Eichstaedt; Margaret L. Kern; Michal Kosinski; David Stillwell; Lyle H. Ungar; Martin E. P. Seligman

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 traits, and then we built a predictive model of personality based on their language. We used this model to predict the 5 personality factors in a separate sample of 4,824 Facebook users, examining (a) convergence with self-reports of personality at the domain- and facet-level; (b) discriminant validity between predictions of distinct traits; (c) agreement with informant reports of personality; (d) patterns of correlations with external criteria (e.g., number of friends, political attitudes, impulsiveness); and (e) test-retest reliability over 6-month intervals. Results indicated that language-based assessments can constitute valid personality measures: they agreed with self-reports and informant reports of personality, added incremental validity over informant reports, adequately discriminated between traits, exhibited patterns of correlations with external criteria similar to those found with self-reported personality, and were stable over 6-month intervals. Analysis of predictive language can provide rich portraits of the mental life associated with traits. This approach can complement and extend traditional methods, providing researchers with an additional measure that can quickly and cheaply assess large groups of participants with minimal burden.


empirical methods in natural language processing | 2014

Developing Age and Gender Predictive Lexica over Social Media

Maarten Sap; Gregory Park; Johannes C. Eichstaedt; Margaret L. Kern; David Stillwell; Michal Kosinski; Lyle H. Ungar; Hansen Andrew Schwartz

Demographic lexica have potential for widespread use in social science, economic, and business applications. We derive predictive 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 state-of-the-art accuracy in language based age and gender prediction over Facebook and Twitter, and were evaluated for generalization across social media genres as well as in limited message situations.


Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality | 2014

Towards Assessing Changes in Degree of Depression through Facebook

H. Andrew Schwartz; Johannes C. Eichstaedt; Margaret L. Kern; Gregory Park; Maarten Sap; David Stillwell; Michal Kosinski; Lyle H. Ungar

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 questionnaires is expensive. In this paper we use survey responses and status updates from 28,749 Facebook users to develop a regression model that predicts users’ degree of depression based on their Facebook status updates. Our user-level predictive accuracy is modest, significantly outperforming a baseline of average user sentiment. We use our model to estimate user changes in depression across seasons, and find, consistent with literature, users’ degree of depression most often increases from summer to winter. We then show the potential to study factors driving individuals’ level of depression by looking at its most highly correlated language features.


Assessment | 2014

The Online Social Self An Open Vocabulary Approach to Personality

Margaret L. Kern; Johannes C. Eichstaedt; H. Andrew Schwartz; Lukasz Dziurzynski; Lyle H. Ungar; David Stillwell; Michal Kosinski; Stephanie M. Ramones; Martin E. P. Seligman

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 method consists of feature extraction, correlational analysis, and visualization. Results: The distinguishing words and phrases were face valid and provide insight into processes that underlie the Big Five traits. Conclusion: Open-ended data driven exploration of large datasets combined with established psychological theory and measures offers new tools to further understand the human psyche.


Developmental Psychology | 2014

From "Sooo Excited!!!" to "So Proud": Using Language to Study Development.

Margaret L. Kern; Johannes C. Eichstaedt; H. Andrew Schwartz; Gregory Park; Lyle H. Ungar; David Stillwell; Michal Kosinski; Lukasz Dziurzynski; Martin E. P. Seligman

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 characteristics. Using a data set of over 70,000 Facebook users, we identify how word and topic use vary as a function of age and compile cohort specific words and phrases into visual summaries that are face valid and intuitively meaningful. We demonstrate how this methodology can be used to test developmental hypotheses, using the aging positivity effect (Carstensen & Mikels, 2005) as an example. While in this study we focused primarily on common trends across age-related cohorts, the same methodology can be used to explore heterogeneity within developmental stages or to explore other characteristics that differentiate groups of people. Our comprehensive list of words and topics is available on our web site for deeper exploration by the research community.


north american chapter of the association for computational linguistics | 2015

The role of personality, age, and gender in tweeting about mental illness

Daniel Preoţiuc-Pietro; Johannes C. Eichstaedt; Gregory Park; Maarten Sap; Laura Smith; Victoria Tobolsky; H. Andrew Schwartz; Lyle H. Ungar

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 demographic estimates show surprisingly strong performance in distinguishing users that tweet a diagnosis of depression or PTSD from random controls, reaching an area under the receiveroperating characteristic curve ‐ AUC ‐ of around .8 in all our binary classification tasks. In fact, when distinguishing users disclosing depression from those disclosing PTSD, the single feature of estimated age shows nearly as strong performance (AUC = .806) as using thousands of topics (AUC = .819) or tens of thousands of n-grams (AUC = .812). We also find that differential language analyses, controlled for demographics, recover many symptoms associated with the mental illnesses in the clinical literature.


pacific symposium on biocomputing | 2016

PREDICTING INDIVIDUAL WELL-BEING THROUGH THE LANGUAGE OF SOCIAL MEDIA.

Hansen Andrew Schwartz; Maarten Sap; Margaret L. Kern; Johannes C. Eichstaedt; Adam Kapelner; Megha Agrawal; Eduardo Blanco; Lukasz Dziurzynski; Gregory Park; David Stillwell; Michal Kosinski; Martin E. P. Seligman; Lyle H. Ungar

We present the task of predicting individual well-being, as measured by a life satisfaction scale, through the language people use on social media. Well-being, which encompasses much more than emotion and mood, is linked with good mental and physical health. The ability to quickly and accurately assess it can supplement multi-million dollar national surveys as well as promote whole body health. Through crowd-sourced ratings of tweets and Facebook status updates, we create message-level predictive models for multiple components of well-being. However, well-being is ultimately attributed to people, so we perform an additional evaluation at the user-level, finding that a multi-level cascaded model, using both message-level predictions and userlevel features, performs best and outperforms popular lexicon-based happiness models. Finally, we suggest that analyses of language go beyond prediction by identifying the language that characterizes well-being.


north american chapter of the association for computational linguistics | 2016

Modelling Valence and Arousal in Facebook posts

Daniel Preoţiuc-Pietro; H. Andrew Schwartz; Gregory Park; Johannes C. Eichstaedt; Margaret L. Kern; Lyle H. Ungar; Elisabeth Shulman

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 psychologicallytrained annotators on two separate ordinal nine-point scales. These scales represent valence (or sentiment) and arousal (or intensity), which defines each post’s position on the circumplex model of affect, a well-established system for describing emotional states (Russell, 1980; Posner et al., 2005). The data set is used to train prediction models for each of the two dimensions from text which achieve high predictive accuracy – correlated at r = .65 with valence and r = .85 with arousal annotations. Our data set offers a building block to a deeper study of personal affect as expressed in social media. This can be used in applications such as mental illness detection or in automated large-scale psychological studies.


PLOS ONE | 2016

Women are warmer but no less assertive than men: gender and language on Facebook

Gregory Park; David B. Yaden; H. Andrew Schwartz; Margaret L. Kern; Johannes C. Eichstaedt; Michael Kosinski; David Stillwell; Lyle H. Ungar; Martin E. P. Seligman

Using a large social media dataset and open-vocabulary methods from computational linguistics, we explored differences in language use across gender, affiliation, and assertiveness. In Study 1, we analyzed topics (groups of semantically similar words) across 10 million messages from over 52,000 Facebook users. Most language differed little across gender. However, topics most associated with self-identified female participants included friends, family, and social life, whereas topics most associated with self-identified male participants included swearing, anger, discussion of objects instead of people, and the use of argumentative language. In Study 2, we plotted male- and female-linked language topics along two interpersonal dimensions prevalent in gender research: affiliation and assertiveness. In a sample of over 15,000 Facebook users, we found substantial gender differences in the use of affiliative language and slight differences in assertive language. Language used more by self-identified females was interpersonally warmer, more compassionate, polite, and—contrary to previous findings—slightly more assertive in their language use, whereas language used more by self-identified males was colder, more hostile, and impersonal. Computational linguistic analysis combined with methods to automatically label topics offer means for testing psychological theories unobtrusively at large scale.

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Lyle H. Ungar

University of Pennsylvania

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Gregory Park

University of Pennsylvania

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Maarten Sap

University of Pennsylvania

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David B. Yaden

University of Pennsylvania

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