The document summarizes a study that aimed to understand personality traits derived from social media text and users' preferences for sharing those traits. The study involved: 1. Automatically deriving users' Big 5 personality traits, fundamental needs, and basic values from social media posts using psycholinguistic analytics. 2. Validating the derived traits against users' self-reports and psychometric tests, finding correlations over 80% of the time. 3. Surveying users about sharing preferences for the derived traits in the workplace. Preferences depended on trait type, value, recipient, and the user's own traits. 4. Most users saw benefits but also risks to sharing, and suggested controls like transparency,

1. Liang Gou, Michelle Zhou & Huahai Yang
IBM Almaden Research Center
KnowMe&
ShareMe:
Understanding Automatically Discovered
Personality Traits and User Sharing
Preferences

2. Overview
Background and Questions
Study Method
Results of Validation & Privacy Preferences
Discussion & Conclusion

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

4. 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 )

5. Social Media To Personality
Hundreds of millions of people leave text footprints on
social media
Psycholinguistic
Analytics
Personality
Portrait
This offers opportunities to understand individuals at
scale.
Big 5
Needs
Values

6. Two Questions
1 How good are the system-derived personality traits?
Derived Traits vs.
Users’ Perception
Derived Traits vs.
Psychometric Tests

7. 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

8. Our Method

9. The Method
The Experimental System: KnowMe
Two-part study
Model Validation Sharing Preferences

10. Big 5 Personality (Golbeck et. al.
'11; Yarkoni ’10)
KnowMe
Fundamental Needs (Ford. '05;
Yang et. al. '13)
Basic Values (Chen et. al. ’13)

11. 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

12. The Survey (Cont.)
Part2: Sharing Preferences
For 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

13. The Participants
Invited 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%)

14. Results

15. 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%

16. Derived Traits vs. User Perception
All 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

17. 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 (-)

18. Privacy Preferences: Effects of Actors
Effects 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
public
Group
Percentage
Setting
None
Range
Numeric

19. 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

20. Perceived Risks and Benefits
48.89 %
19.94 %
11.63 %
6.79 %
5.54 %
5.4 %
1.11 %
0.69 %Personalized IT Services
Workplace Learning
None
Work Fitness
Teaming
Self Branding
Self Awareness
People Underst. & Inter.
0 20 40 60
PercentagePerceivedBenifits
37.55 %
16.03 %
15.19 %
10.69 %
7.45 %
6.89 %
4.78 %
1.41 %Reveal Volunerability
Incomplete Image
Lost Privacy
None
Inaccurate Analytics
Misconception
Information Abuse
Prejudice
0 20 40 60
Percentage
PerceivedRisks
Top Benefits
• People understanding
and Interaction
• Self Awareness
• Self Branding
Top Risks
• Prejudice
• Information Abuse
• Misconception
• Inaccurate Analysis

21. Suggested Controls
Top 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 %
Grouping
System Refresh
Anonymity
No Sharing
Controlled Time
Opt Out
Security
System Trans - Func
System Trans - Usage
Controlled Data
Controlled User
0 10 20
Percentage
SuggestedControl

22. Implications
Support 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.”

23. Implications
Mixed-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

24. Challenges
Data 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 / Sharing
Source: kimbeach.com/wp-content/uploads/
2013/12/Fish-Facing-Challenge.jpg

25. Conclusion
This 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.

26. Thank you!
Liang Gou(lgou@us.ibm.com)
Questions?

27. • 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

28. Backup

29. 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
sensitive/nervous vs.
secure/confident
friendly/compassionate
vs.cold/unkind
outgoing/energetic vs.
solitary/reserved
efficient/organizedvs.easy-
going/careless

30. 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 tweets
An example: “Ideal”
Positively correlated: accomplish, chauffeur, goal,
license, special…
Negatively correlated: bad, fix, half, minimum, mix,
ugly, wrong, obvious, … (Yang, H. et al. , 2013)

31. Modeling Basic Human Values
Why 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)

32. RV Coef over Subsets of Population

33. Effects of Trait Value