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Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach
This represents the largest study, by an order of magnitude, of language and personality, and found striking variations in language with personality, gender, and age.
Automatic personality assessment through social media language.
- Gregory J. Park, H. A. Schwartz, M. Seligman
- PsychologyJournal of personality and social psychology
- 31 May 2015
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, and were stable over 6-month intervals.
Detecting depression and mental illness on social media: an integrative review
Characterizing Geographic Variation in Well-Being Using Tweets
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,…
Developing Age and Gender Predictive Lexica over Social Media
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 achieve state-of-the-art accuracy.
Facebook language predicts depression in medical records
- J. Eichstaedt, Robert J. Smith, H. A. Schwartz
- PsychologyProceedings of the National Academy of Sciences
- 15 October 2018
It is shown that the content shared by consenting users on Facebook can predict a future occurrence of depression in their medical records, and language predictive of depression includes references to typical symptoms, including sadness, loneliness, hostility, rumination, and increased self-reference.
Psychological Language on Twitter Predicts County-Level Heart Disease Mortality
Capturing community psychological characteristics through social media is feasible, and these characteristics are strong markers of cardiovascular mortality at the community level.
Modelling Valence and Arousal in Facebook posts
A new data set of 2895 Social Media posts rated by two psychologicallytrained annotators on two separate ordinal nine-point scales, which defines each post’s position on the circumplex model of affect, a well-established system for describing emotional states.
Towards Assessing Changes in Degree of Depression through Facebook
A regression model is developed that predicts users’ degree of depression based on their Facebook status updates, and shows the potential to study factors driving individuals’ level of depression by looking at its most highly correlated language features.
The role of personality, age, and gender in tweeting about mental illness
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 the authors' binary classification tasks.