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Facebook and Privacy:
The Balancing Act of Personality, Gender, and Relationship Currency


@danielequercia
<who am i>
daniele quercia
offline & online
social media
   language        personality




        social media
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
YOU




      YOUR FRIENDS
Talk of the City   TODAY 5:30




   social media
   environment
   sports
   health             wedding parties
                      Spanish/Portuguese
                      celebrity gossips
<goal>
focus on privacy…
Assign a number to each Facebook user
Assign a number to each Facebook user


  = user’s disposition to disclose & conceal what
  is considered private and public in Facebook
Easiest way: Count!
   looking
   education
   residence
   political
   religion
   hometown
   position
   employer

   X disclosed all 8
   Y disclosed only 1
Our way: Item Response Theory (IRT)


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


Traditional Goal:
 Design tests & build scales




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


                         fields


                  ( )
                 users
                         1..0..




      field i discriminative power
      field i sensitive score

     user j disclosure attitude
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)
1 Smart Privacy Mob
2 Who are they
3 What they disclose
1 Smart Privacy Mob
Previously: Westin has divided people in

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

              1. privacy fundamentalists
              2. pragmatic majority
              3. marginally concerned


 We
2 Who are they?



 Those who share more sensitive info are:
  • Open to new experience
  • Self-monitoring
  • Male
  • More Active
  • Younger
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
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
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
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
3 What’s sensitive
3 What’s sensitive
3 What’s sensitive
      Social Currency

                        OK to disclose
                        OK to conceal
3 What’s sensitive




   Community vs. Social Circle
So what?
Theoretical Implications
Practical Implications
Short term - Language & RecSys & Crime
Example 1: Twitter & Personality



 1   listeners, popular, & influential:
     extrovert & emotionally stable


 2   highly-read: open to new experiences
predicting personality with twitter
predicting personality with twitter




                          YES, we can!
predicting personality with twitter


                                                 st ed!
                                             , li
                                         ing
                                     low
                               , fol
                           ers
                       low                 YES,     we can!
                 g fol
             sin
         y u
     onl
 And
Predicting Personality with Twitter
Estimate number of people in a place
share it all
[by Facebook CEO]
share it all
    [by Facebook CEO]




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




share fake data
         [by Us]




    share nothing
[by Wisdom of the Granmother’s
          Foundation]
share fake data
     [by Us]
Idea:
true location +
+ “99” fake locations
true location +
+ “99” fake locations

            true location +
            + “99” fake locations


true location +
+ “99” fake locations
#phones?
(random)
fake locations
Tube passengers in London & car drivers in Zurich:
 accurately estimate #people
@danielequercia

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Facebook and Privacy: The Balancing Act of Personality, Gender, and Relationship Currency

  • 1. Facebook and Privacy: The Balancing Act of Personality, Gender, and Relationship Currency @danielequercia
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  • 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
  • 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
  • 35. 3 What’s sensitive Social Currency OK to disclose OK to conceal
  • 36. 3 What’s sensitive Community vs. Social Circle
  • 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
  • 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
  • 45.
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  • 47.
  • 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
  • 59. Tube passengers in London & car drivers in Zurich: accurately estimate #people

Editor's Notes

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