KnowMe and ShareMe: understanding automatically discovered personality traits from social media and...

Liang Gou, Michelle Zhou & Huahai Yang IBM Almaden Research Center

KnowMe& ShareMe: Understanding Automatically Discovered Personality Traits and User Sharing Preferences

Overview

Background and Questions

Study Method

Results of Validation & Privacy Preferences

Discussion & Conclusion

Personality influences behaviors: occupational proficiency (Barrick & Mount’91) and economic decisions (Ford ’05)

Personality & behaviors

Deriving Personality

“I love food, .., with … together we … in… very…happy.”

Word category: Inclusive Agreeableness

Psycholinguistic studies: personality from text (Yarkoni '10; Tausczik & Pennebaker '10 )

Social Media To PersonalityHundreds of millions of people leave text footprints on social media

Psycholinguistic Analytics

Personality Portrait

This offers opportunities to understand individuals at scale.

Big 5Needs

Values

Two Questions1 How good are the system-derived personality traits?

Derived Traits vs. Users’ Perception

Derived Traits vs. Psychometric Tests

Two Questions(cont.)How would users like to share the derived personality traits in an enterprise context?

What and With whom

Friends Colleagues

Mgr.

Benefits and Risks of Sharing

2

Effect of the User’s Traits

Our Method

The MethodThe Experimental System: KnowMe

Two-part study

Model Validation Sharing Preferences

Big 5 Personality (Golbeck et. al. '11; Yarkoni ’10)

KnowMe

Fundamental Needs (Ford. '05; Yang et. al. '13)

Basic Values (Chen et. al. ’13)

The Survey

Part1: Model Validation• Three sets of psychometric tests

• 50-item Big 5 (IPIP), 26-item basic values (Schwartz ’06), and 52-item fundamental needs (our own)

• Rate the matches with their perception of themselves

The Survey (Cont.)Part2: Sharing PreferencesFor each type of traits, we asked users’ sharing preferences • For four groups

• “public”, “distant colleagues”, “management”, and “close colleagues”

• At three levels • “none”, “range”, and “numeric”

• State the expected benefits and risks in the work place

• and desired controls for sharing their traits

The ParticipantsInvited 1325 colleagues with Twitter presence and also producing at least 200 tweets.

256 completed the study among 625 responses

Source: www.acuteaday.com/blog/category/guinea-pig/

Source: www.backyardchickencoops.com.au/author/kassandra/page/5/

United States (42.0%), Europe (32.1%), other parts of the world (25.9%)

Results

Derived Portrait vs. Psycho-Metric Scores

Correlational analysis of each trait profile (RV Coef: considering all dimensions together within each type of trait)

Over 80% of population, the correlation is statistically significant. • Big 5: 80.8% • Needs: 86.6% • Basic values: 98.21%

Derived Traits vs. User PerceptionAll ratings are above 3 (“moderately matched”) out of 5-likert scale

Overall ratings • Big 5: u=3.4, sd = 1.14 • Values: u= 3.13, sd = 1.17 • Needs: u= 3.39, sd = 1.34

Privacy Preferences: Effects of Traits

Effects of Trait Type • PD(Values) < PD(Big5) or PD(Needs) *** • PD(Neuroticism) < others within Big5 ***

PD(∗) is probability of information disclosure. ◦ p<0.1, * p<0.05, ** p<0.01, *** p<0.001

Effects of Trait Value: tend to share “good things” • PD(High) < PD(Low) ** for traits with “positive names”

• Big 5: Openness (+), Conscientiousness (+) Agreeableness (+)

• PD(Low) < PD(High) ** for traits with “negative names” • Big 5: Neuroticism (-) • Values: Conservation (-), Hedonism (-)

Privacy Preferences: Effects of ActorsEffects of Recipient Type • Overall, 61.5% of partici-

pants were willing to disclose

• Sharing differences are significant: PD(distant/public)< PD(close/mgt.)***.

0.0

0.2

0.4

0.6

close.colleague

management

distant.colleague pub

lic

Group

Percentage Setting

None

Range

Numeric

Privacy Preferences: Effects of Actors(2)Effects of the Sender’s Traits • Certain dimensions of the participants’ personality traits

significantly impact their sharing preferences. • For example,

• Extroversion positively impacts one’s sharing preferences for Big 5 and needs, but not for basic values

• Conscientiousness negatively impacts the sharing of all three types of traits

Perceived Risks and Benefits48.89 %

19.94 %11.63 %

6.79 %5.54 %5.4 %

1.11 %0.69 %Personalized IT Services

Workplace LearningNone

Work FitnessTeaming

Self BrandingSelf Awareness

People Underst. & Inter.

0 20 40 60Percentage

Per

ceiv

ed B

enifi

ts

37.55 %16.03 %

15.19 %10.69 %

7.45 %6.89 %

4.78 %1.41 %Reveal Volunerability

Incomplete ImageLost Privacy

NoneInaccurate Analytics

MisconceptionInformation Abuse

Prejudice

0 20 40 60Percentage

Per

ceiv

ed R

isks

Top Benefits • People understanding

and Interaction • Self Awareness • Self Branding

Top Risks • Prejudice • Information Abuse • Misconception • Inaccurate Analysis

Suggested ControlsTop Controls • Controlled Users • Controlled Data • System Trans - Usage/Function

23.09 %18.13 %

14.29 %12.64 %

7.69 %7.14 %

4.95 %

3.3 %3.3 %

2.75 %2.75 %

GroupingSystem Refresh

Anonymity No Sharing

Controlled TimeOpt OutSecurity

System Trans - FuncSystem Trans - Usage

Controlled DataControlled User

0 10 20Percentage

Sug

gest

ed C

ontro

l

ImplicationsSupport of System Transparency• Clearly explain the meaning of each trait and

intended use !!

!!• Prescriptive and clearly states what it is

capable of and its limitations

“It might happen that people could understand something else from the (trait) name… and this should be explained very carefully”

“ability to mark that certain attributes are inaccurate conveying the inability of system to gauge them properly.”

ImplicationsMixed-Initiative Privacy Preserving

• What to share: Control the granularity of personality traits

• Whom to share with: Be alerted or know when someone is accessing their profiles

• When to share. • Where to share: Sharing channels Source: www.hoax-slayer.com/images/

privacy.jpg

ChallengesData Variety and Model Effectiveness• Multiple Data Source: twitters, facebook

• Multiple Projected “Personality”

Cultural and Language Influence

• Western culture vs. Others / English vs. Others

• Modeling / Interpretation / SharingSource: kimbeach.com/wp-content/uploads/2013/12/Fish-Facing-Challenge.jpg

ConclusionThis work demonstrates the potential feasibility of automatically deriving one’s personality traits from social media with various factors impacting the accuracy of models.

Most people are willing to share their derived traits in the workplace, and a number of factors, including who/whom/when/where, and the perceived benefits/risks, significantly influence the users’ sharing preferences.

Thank you!

Liang Gou(lgou@us.ibm.com)

Questions?

• Chen, J., Hsieh, G., Mahmud, J., and Nichols, J. Understanding individuals personal values from social media word use. In ACM Proc. CSCW ’2014.

• Ford, J. K. Brands Laid Bare. John Wiley & Sons, 2005. • Schwartz, S. H. Basic human values: Theory, measurement, and applications.

Revue francaise de sociologie, 2006. • Tausczik, Y. R., and Pennebaker, J. W. The psychological meaning of words: LIWC

and computerized text analysis methods. Journal of Language and Social Psychology 29, 1 (2010), 24–54.

• Yang, H., and Li, Y. Identifying user needs from social media. IBM Tech. Report (2013).

• Yarkoni, T. Personality in 100,000 words: A large-scale analysis of personality and word use among bloggers. J. research in personality 44, 3 (2010), 363–373.

References

Backup

Modeling and Deriving One’s Personality

Why model personality • Psychological characteristics

reflecting individual differences • Consistent and enduring • Link to many aspects in one’s life

• Relationship selection • Problem, emotion coping • Brand/product choices • Occupational proficiency • Team performance

What do we model • Big 5 Personality (OCEAN)

[O’Brien ’96, Neuman ’99, Gosling ’03, Wholan’06]

inventive/curious vs.

consistent/cautious sensitiv

e/nervous v

s.

secure/confident

friendly/compassionate

vs. cold/unkind

outgoing/energetic vs. solitary/reserved

effic

ient

/org

anize

d vs

. eas

y-go

ing/

care

less

Modeling One’s Fundamental Needs (Cont.)

Psychometric empirical studies • Large-scale crowdsourcing of needs scores

and text descriptions from over 2000 people on Mechanical Turks

Statistic analysis to correlate • Psychometric scores with textual

descriptions

Predictive model to derive the needs from one’s tweetsAn example: “Ideal” Positively correlated: accomplish, chauffeur, goal, license, special… Negatively correlated: bad, fix, half, minimum, mix, ugly, wrong, obvious, … (Yang, H. et al. , 2013)

Modeling Basic Human ValuesWhy model human values • Values motivate people and guide their actions • Values transcend specific actions and situations

What do we model • 10-dimensional values as measured through

established psycho-metric surveys

[Schwartz  2006]  (Chen, J. et al. , 2013)

RV Coef over Subsets of Population

Effects of Trait Value