Paper on privacy and facebook http://tinyurl.com/77yzf2c

1. Facebook and Privacy:
The Balancing Act of Personality, Gender, and Relationship Currency
@danielequercia

2. <who am i>

3. daniele quercia

10. offline & online

11. social media
language personality
social media

12. The Social World of Twitter WEDNESDAY 12:10
1. Brokers tend to cover diverse topics
2. Users have a “typical” geo span
3. “Happy” (“sad”) users do cluster together

13. YOU
YOUR FRIENDS

14. Talk of the City TODAY 5:30
social media
environment
sports
health wedding parties
Spanish/Portuguese
celebrity gossips

15. <goal>

16. focus on privacy…

17. Assign a number to each Facebook user

18. Assign a number to each Facebook user
= user’s disposition to disclose & conceal what
is considered private and public in Facebook

19. Easiest way: Count!
looking
education
residence
political
religion
hometown
position
employer
X disclosed all 8
Y disclosed only 1

20. Our way: Item Response Theory (IRT)
Traditional Goal:
Design tests & build scales
respondent’s disposition to answer difficult questions

21. Our way: Item Response Theory (IRT)
Traditional Goal:
Design tests & build scales
Goal here:
Design disclosure&concealment models & build scales

22. Our way: Item Response Theory (IRT)
Traditional Goal:
respondent’s disposition to answer difficult questions
Goal here:
user’s disposition to disclose(conceal) what is private(public)

23. Our way: Item Response Theory (IRT)
fields
( )
users
1..0..
field i discriminative power
field i sensitive score
user j disclosure attitude

24. DATA representative for age, gender, #contacts, distribution of traits
N=1,323 Facebook Users in US (58% women)
Age [18,60] median 24
#contacts [32,998]
Apply IRT to what’s disclosed to
* Facebook Community at large
* Facebook Friends (one’s Social Circle)

25. 1 Smart Privacy Mob
2 Who are they
3 What they disclose

26. 1 Smart Privacy Mob
Previously: Westin has divided people in
1. privacy fundamentalists
2. pragmatic majority
3. marginally concerned

27. 1 Smart Privacy Mob
Previously: Westin has divided people in
1. privacy fundamentalists
2. pragmatic majority
3. marginally concerned
We

28. 2 Who are they?
Those who share more sensitive info are:
• Open to new experience
• Self-monitoring
• Male
• More Active
• Younger

29. 2 Who are they?
Those who share more sensitive info are:
• Open to new experience • Open to new experience
• Self-monitoring
• Male • Male
• More Active
• Younger

30. 2 Who are they?
Those who share more sensitive info are:
• Open to new experience • Open to new experience
• Self-monitoring
• Male • Male
• More Active
• Younger

31. 2 Who are they?
Those who share more sensitive info are:
• Open to new experience • Open to new experience
• Self-monitoring
• Male • Male
• More Active
• Younger

32. 2 Who are they?
Those who share more sensitive info are:
Results: Good new experience analysis; not for (linear) prediction
• Open to
for descriptive
• Open to new experience
BUT… • Self-monitoring
• Male • Male
• More Active • More Active
• Younger

33. 3 What’s sensitive

34. 3 What’s sensitive

35. 3 What’s sensitive
Social Currency
OK to disclose
OK to conceal

36. 3 What’s sensitive
Community vs. Social Circle

37. So what?

38. Theoretical Implications

39. Practical Implications

40. Short term - Language & RecSys & Crime

41. Example 1: Twitter & Personality
1 listeners, popular, & influential:
extrovert & emotionally stable
2 highly-read: open to new experiences

42. predicting personality with twitter

43. predicting personality with twitter
YES, we can!

44. predicting personality with twitter
st ed!
, li
ing
low
, fol
ers
low YES, we can!
g fol
sin
y u
onl
And

48. Predicting Personality with Twitter

49. Estimate number of people in a place

50. share it all
[by Facebook CEO]

51. share it all
[by Facebook CEO]
share nothing
[by Wisdom of the Granmother’s
Foundation]

52. share it all
[by Facebook CEO]
share fake data
[by Us]
share nothing
[by Wisdom of the Granmother’s
Foundation]

53. share fake data
[by Us]

54. Idea:

55. true location +
+ “99” fake locations

56. true location +
+ “99” fake locations
true location +
+ “99” fake locations
true location +
+ “99” fake locations

57. #phones?

58. (random)
fake locations

59. Tube passengers in London & car drivers in Zurich:
accurately estimate #people

60. @danielequercia