Lexical, Morphological and Semantic Correlates of the Dark Triad Personality Traits in Russian Facebook Texts

1. Lexical, Morphological and
Semantic Correlates
of the Dark Triad Personality Traits
in Russian Facebook Texts
Polina Panicheva
ppolin86@gmail.com
Olga Bogolyubova
Yanina Ledovaya
St. Petersburg State
University
Clarkson University
Supported by SPBU research grant 8.38.351.2015:
"A cross-cultural study of the markers of stress,
health and well-being in social networks".

2. Linguistic correlates of the Dark Traits
◎Motivation
○ English background (Sumner 2012), (Schwartz
2013)
○ Dark traits
○ Russian LIWC
◎Data collection
○ Questionnaire
◎Statistics and interpretation
○ Text volume features
○ Lexical
○ Morphological
○ Content
◎Significance issues and future work

3. Background:
Author Profiling in English
◎Personality profiling
○ Big Five
○ Dark traits (Sumner 2012), Psychopathy (Hancock
2013)
○ Mental health issues: PTSD, depression (Harman
2014)
◎Data
○ Twitter, Facebook
○ Self-report narratives
◎Word-count statistics
○ Linguistic Inquiry and Word Count

4. http://wwbp.org/demos.html

5. The Dark Traits
◎Narcissism, Machiavellianism, Psychopathy
○ Related but distinct, lack of empathy – common feature
○ Subclinical, continuous scales
◎The Short Dark Trait Survey by Jones 2014
○ Russian adaptation by Egorova (2014)
○ Likert 5-item agreement scales (9 q. per trait)

6. The Dark Traits
◎Narcissism
○ self-focus, grandiosity
○ ‘People see me as a natural leader’
◎Machiavellianism
○ manipulating
○ ‘It’s not wise to tell your secrets‘
◎Psychopathy
○ impulsiveness, aggression and asocial behavior
○ ‘People often say I’m out of control’

7. The Dark Traits: structure
Narcissism Machiavellianism Psychopathy
Motivation Ego-identity
goals
Instrumental goals,
material gain
Temporal
focus
Distant future,
strategic
Immediate
Lack of empathy,
interpersonal manipulation
Facets Exploitative/
entitlement
Leadership/
authority
Cynical worldview
Machiavellian tactics
Manipulation
Callous affect
Erratic lifestyle
Antisocial
behavior
(Jones 2014)

8. Russian Linguistic Inquiry and Word Count
◎English LIWC – dictionary (Pennebaker 2001)
○ ~80 categories
◎Translated to Russian directly
○ Preserving the English-based morphological and
semantic structure
○ E.g content/function words – doesn’t hold in Russian
○ Different language type: affixation & fusion
◎Why impose the foreign top-down category
structure?
◎Induce native language-specific categories
in a bottom-up way

9. Task
◎Obtain significant linguistic correlates
of the Dark Traits
○ Bottom-up lexical approach
○ Morphological analysis-based features
○ Derive content features by semantic clustering

10. Dataset
◎Facebook application
https://apps.facebook.com/psytest
◎Questionnaire
○ Well-being, Dark traits, stress, demographic features
Subclinical, continuous scales
○ Consent to download public posts
◎Due to technical (API) restrictions only one
FB wall ~ 20 posts were downloaded
◎8K users by means of advertising
◎2K users with personal texts

11. Text length features
Text feature Na Ma Ps
Sentence length 0.022 -0.057 0.006
Post length, sentences 0.054 -0.109 -0.04
Post length, tokens 0.045 -0.101 -0.02
p < 0.05 p < 0.01
Machiavellianism: guard personal image, avoid oversharing (Rauthmann
2012)
Narcissism: expansive, attract attention (Raskin 1988)

12. Morphological and lexical features
◎PyMorphy analyzer [Korobov 2015]
◎Morphological gramemes:
○ POS
○ person, number (in verb and pronoun)
○ verb modality: tense, voice, mood, reflexivity
○ NE: names, organization, geo-location, abbr
○ adjective features: short/full, qualitative, superlative
○ possessive pronouns
○ style:
◉vernacular, slang

13. Semantic clustering
◎Words in >5 authors’ texts -> 3.7K words
◎k-means over a RNC-trained word-embeddings
semantic space, [Panicheva 2016]
◎182 thematic clusters
○ fully automatic
○ interpretable
Cluster Contents
Authority выборы дума комитет
парламент рада рф
election parliament
committee Rada Russia
Friend близкий друг незнакомый
приятель родные
folks friend stranger pal
relative
Female_na
me
алиса анна вера виктория
елена ирина мария
Alice Anna Vera Victoria
Elena Irina Maria
Passion безумие веселье желание
любовь страсть
madness joy desire love
passion

14. Results: Narcissism
Feature Lexical Clustering Morphology
Self focus I, my Perfection,
High_low
1st
pers sing,
1st
pers poss
Social
involveme
nt
you, gratitude,
thank, company,
invitation
Appeal,
Take_give, Want,
Feeling
imperative,
2nd
pers plur
Positive
emotion
honorable,
important
Awful, Passion,
Pos_quality,
Perfection
Goal focus become, vocation,
skill, solution
Reasoning, Goal,
Achievement
Speech
involvement
interj, punct,
prons, verbs
p < 0.05
Facets of Narcissism: Leadership/Authority, Exploitativeness/Entitlement
(Jones 2014)

15. Results: Machiavellianism
Feature Lexical Clustering Morphology
Social
involvement
friend, we Affirm, Friend, Feeling_vb,
Tender_adj, male/female
names
1st
pers plur,
names, 2nd
, 3rd
pers
Positive affect love, heart Wellbeing, Impress
Mental
processing
Faith, Religion, Perception,
Sensation, Appearance
Politics russia, isis,
president
Personal
detachment
prons, verbs
Common daily
topics
Neg_action, Trouble,
Face_part, Body_situation,
Number, Age, Being
Facets of Machiavellianism: Cynicism, Manipulative Tactics
(Jones 2014)

16. Results: Psychopathy
Feature Lexical Clustering Morphology
Politics russia, russian,
president, nation, putin,
usa
Political,
Powerful_male,
Authority
Organization
Style vernacular, slang
Basic needs Food, Money,
Money_affair,
Money_operation
Relationships grant, betray, be glad,
inspire, endow
Friend
Aesthetic
concern
Sky Adjective
Reflexivity think,
attentive/thoughtful
Facets of Psychopathy: Manipulation, Callous Affect, Erratic Lifestyle,
Antisocial Behavior (Jones 2014)

17. Significance discussion
◎Significance filtering:
○ Bonferroni correction – too strict
○ False Rate Discovery
◎Very modest correlation values
○ r ~ 0.1, but p < 0.01!
○ Sumner (2012), Schwartz (2013) report similar values
for English
○ Much more data needed, pref. more texts p.person
○ Are r’s supposed to be very high?
◎Prediction: just above baseline (cf. Sumner)

18. Future work
◎More data
○ Identify meaningful n-grams
○ Technical features: TTR, POS ratio
◎Prediction experiment – are high results
possible in this setting?
◎Identify ‘repost language’ correlations
◎Covariates: investigate difference in language
caused by sex, age, …
◎Project goal: language of well-being and
online aggression
◎More remote goal: compare Russian VS US
well-being and Dark traits

19. Thank you!
Questions?
St. Petersburg State
University
Clarkson University
Polina Panicheva
ppolin86@gmail.com
Olga Bogolyubova
Yanina Ledovaya

20. References
◎ M. Egorova and M. Sitnikova, “The dark triad,” psystudy.ru, 2014
◎ J. T. Hancock, M. T. Woodworth, and S. Porter, “Hungry like the wolf: A word-
pattern analysis of the language of psychopaths,” 2013.
◎ G. Harman, M. Coppersmith, and C. Dredze, “Quantifying mental health signals in
twitter,” ACL 2014, p. 51, 2014.
◎ D. N. Jones and D. L. Paulhus, “Introducing the short dark triad (sd3) a brief
measure of dark personality traits,” Assessment, 2014
◎ M. Korobov, “Morphological analyzer and generator for russian and ukrainian
languages,” AIST, 2015
◎ P. Panicheva, Y. Ledovaya, and O. Bogoliubova, “Revealing interpetable content
correlates of the dark triad personality traits,” RuSSIR, 2016
◎ J. W. Pennebaker, M. E. Francis, and R. J. Booth, “Linguistic inquiry and word
count: Liwc 2001,”
◎ R. Raskin and H. Terry, “A principal-components analysis of the narcissistic
personality inventory and further evidence of its construct validity,” 1988.
◎ J. F. Rauthmann and G. P. Kolar, “How “dark” are the dark triad traits? Examining
the perceived darkness of narcissism, machiavellianism, and psychopathy,” 2012
◎ H. A. Schwartz, J. C. Eichstaedt, M. L. Kern, L. Dziurzynski, S. M. Ramones, M.
Agrawal, A. Shah, M. Kosinski, D. Stillwell, M. E. Seligman et al., “Personality,
gender, and age in the language of social media: The open-vocabulary approach,”
2013.
◎ C. Sumner, A. Byers, R. Boochever, and G. Park, “Predicting dark triad personality
traits from twitter usage and a linguistic analysis of tweets,” ICMLA, 2012