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

Polina Panichevappolin86@gmail.comOlga 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 Panichevappolin86@gmail.comOlga BogolyubovaYanina Ledovaya

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