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Lexical, Morphological and Semantic Correlates of the Dark Triad Personality Traits in Russian Facebook Texts
Polina [email protected] BogolyubovaYanina Ledovaya
St. Petersburg State UniversityClarkson 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".
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
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
http://wwbp.org/demos.html
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)
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’
The Dark Traits: structureNarcissism Machiavellianism Psychopathy
Motivation Ego-identity goals
Instrumental goals,material gain
Temporal focus
Distant future, strategic
Immediate
Lack of empathy,interpersonal manipulation
Facets Exploitative/entitlementLeadership/
authority
Cynical worldviewMachiavellian tactics
ManipulationCallous affectErratic lifestyle
Antisocial behavior
(Jones 2014)
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
Task
◎Obtain significant linguistic correlates of the Dark Traits
○ Bottom-up lexical approach○ Morphological analysis-based features○ Derive content features by semantic clustering
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
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)
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
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_name
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
Results: NarcissismFeature Lexical Clustering Morphology
Self focus I, my Perfection, High_low
1st pers sing, 1st pers poss
Social involvement
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 < 0.05Facets of Narcissism: Leadership/Authority, Exploitativeness/Entitlement (Jones 2014)
Results: MachiavellianismFeature 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)
Results: PsychopathyFeature 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)
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)
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
Thank you!
Questions?
St. Petersburg State UniversityClarkson University
Polina [email protected] BogolyubovaYanina Ledovaya
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