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individual and community WWBP

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by

Lyle Ungar

on 2 May 2016

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Transcript of individual and community WWBP

The World Well-Being Project
Inferring individual and county level traits from social media
Gender, Age & Personality
Gender
Neuroticism
Extraversion
Explicit Language Warning
Heart Disease
The Language of (less) Heart Disease
*
Twitter Predicting Heart Disease
|
Personality
Gender
Age
(public)
Personality
Gender
?
Accuracy:
92%
Openness
Conscientiousness
World-Well Being Project | wwbp.org
Age
13
typical life stage words
classes of emotions
alcohol
I / we
pro/anti-social topics
13-18
19-22
23-29
30+
Personality X Gender
Agreeableness

Language of emotionally stable
individuals









counties with Lower Suicide Mortality Rates


Takeaways
Language reveals demographics, personality
Also psychopathy, emotion, SES, empathy, political orientation, happiness, depression, health, disease
Language models generalize
across individuals
from Facebook to Twitter
from individuals to counties
Language allows data-driven hypothesis generation
As well as hypothesis testing
Social media biases can be handled!
Reduced Heart Disease
Increased Heart Disease
Emotionally stable individuals
Suicide
Counties with less suicide
Life Satisfaction
H. Andrew Schwartz et al. 2013. Characterizing Geographic Variation in Well-Being using Tweets
higher
satisfaction
lower
satisfaction
Community
Individual
Health & Personality
Community Personality
Introduction
Lyle Ungar

University of Pennsylvania
wwbp.org
Community SWL and Heart Disease
Twitter predicts heart disease
Natural Language Processing
Historically:
What people say
IE, question answering, …
Accuracy

Increasingly:
How people say it
Personality, gender, age variations …
Insight
Full transcript