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What you want is not what you get:
Predicting sharing policies for text-based content on Facebook
Arunesh Sinha*, Yan Li †, Lujo Bauer*
*Carnegie Mellon University
†Singapore Management University
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Motivation
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Problem for Social Networks
o Report in dailymail.co.uk†
† http://www.dailymail.co.uk/sciencetech/article-2423713/Facebook-users-committing-virtual-identity-suicide- quitting-site-droves-privacy-addiction-fears.html
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More User Control ⇏ Better Privacy
o Users fail to comprehend controls
o Users fails to comprehend consequences
o Though concerned, often no effort towards better use of controls
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More user controlSmarter user control
Our goal: Help users pick correct policy for new Facebook posts
Facebook Wall
Post n+1Facebook’s Strategy
Post n-2
Post n-1
Post n
Friends
Public
Public
Default:Public
Our Goal and Approach
Facebook Wall
Post n+1
Post n-2
Post n-1
Post n
Friends
Public
Public
Default:?
ML
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Outline
o Data collection methodologyo Survey resultso Machine learning approacho Results and analysiso Limitations / Conclusion
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Survey Methodology
o Created an online survey o Advertised on Craigslist and at CMU
Data Collection Method
Participate in a Carnegie Mellon research study on Facebook sharing. Earn $5 for participating in a ~20 minute online study.
We’re looking for English speaking adults, who have used Facebook for at least 4 months, update their Facebook status or post on Facebook at least every other day, and have used more than one privacy setting for their posts.Please click on the following link to start the online study: http://greyw1.ece.cmu.edu/survey/survey.phpUpon completion of the study, you will receive a $5 Amazon gift card.
Filtering Users
Data Collection Method
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Survey Questions
o Collected demographic data– Age, gender, country, level of education
o Degree of agreement with the statements: – I have a strong set of privacy rules.– I find Facebook's privacy controls confusing.
o Have you ever posted something on a social network and then regretted doing it? If so, what happened?
Data Collection Method
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o Fetched 4 months of users’ posts
Facebook App
Data Collection Method
Policy
Text in post
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Survey Results: Demographics
o 42 participants (avg. 146 posts and 4.6 policies)o Age: 18 to 65, with an average of 29.1o 35 female, 7 maleo 39 from USA Level of education
High SchoolCollegeAdvanced
Survey Results
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Survey Results: Sentiment
Survey Results
Regretted posting ever? (No/Yes)
Find privacy control confusing
Have a strong set of privacy rules
0% 20% 40% 60% 80% 100%
Disagree
Neutral
Agree
ML Usage Plan
Facebook Wall
Post n+1
Post n-2
Post n-1
Post n
Friends
Public
Public
Default:?
ML
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Machine Learning
o We use MaxEnt as the ML tool– Used Stanford NLP software
o MaxEnt: provides good generalization– I.e., prevents overfitting– Learns probabilistic hypothesis h that outputs
probability over labels given data x– Chooses hypothesis h with maximizes entropy
• Subject to a form of agreement with training data
Machine Learning Approach
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Features Considered
o Words and 2-grams in the Facebook posto Presence of multimediao Time of day – morning, evening, nighto Previous post’s policy
o Model (feature set) chosen using cross validation
Machine Learning Approach
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Temporal Testing o The data is temporalo Picked 10 posts randomly as test datao We simulate a real-world scenario
Test
Test
Train to predict
Train to predict
Machine Learning Approach
Time
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Trainingo Cross-validation to choose featureso May have different model for different test point
Machine Learning Approach
Test
Test
Train to predict
Train to predict
Time
Baseline Approach
o Previous policy (Facebook’s approach)– Use the policy of the last post as the prediction
o Surprisingly, pretty good accuracy– 0.85 on average
Results and analysis
MaxEnt Accuracy
Technique Accuracy
Baseline Previous Policy
0.85
MaxEnt 0.86
Results and analysis
Prediction Mismatch
o Problem: We are not predicting intended policy– Instead, predicting implemented policy
o Conjecture:– Implemented policy is often incorrect– Users just use Facebook’s default policy
Results and analysis
Ground Truth Collection
o Feedback on 20 randomly chosen posts– Provides ground truth (intended policy)
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Results and analysis
All policie
sever used
Text of post
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Datasets
Original data Clean data
Correct 20 posts basedon feedback
Prunedclean data
Remove 80%
Implemented Policy
Results and analysis
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Temporal Testing o 20 intended policy knowno Picked 8 of these randomly as test datao We simulate a real-world scenario
Test
Test
Train to predict
Train to predict
Results and analysis
Baselineo Same previous policy approach as beforeo Measure intended accuracy
– Predict only for posts with known intended policy– Better measure of performance
o Baseline intended accuracy: 0.67– 0.85 obtained previously on implemented policies
Results and analysis
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MaxEnt Intended Accuracy
Results and analysis
Baseline
67%
MaxEnt(clean)
71%
MaxEnt(pruned clean)
81%
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Confidence About PolicyConfidence Factor (CF): Fraction of posts for which intended policy matched implemented policy
Results and analysis
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7
12
16
Users binned by confidence factor
0.00-0.250.26-0.500.51-0.750.76-1.00
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Analysis of Improvement
0.00-0.25 (7)
0.26-0.50 (7)
0.51-0.75 (12)
0.76-1.00 (16)
00.10.20.30.40.50.60.70.80.9
1
Baseline
MaxEnt (Clean)
MaxEnt (Pruned Clean)
Confidence factor (#users)
Intended Accuracy
Results and analysis
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Limitations
o Only 20 intended policy available
o 42 participants is not a huge number– Other studies have used similar numbers
o Richer feature space possible– By processing the attachments of the post
o Could use more sophisticated ML techniques
Limitations
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Conclusion
o Accuracy: 67% 81%o Accuracy for CF>0.5: 78% 94%
An approach demonstrating feasibility of learning intended
privacy policy of Facebook posts
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Discarding “Bad” Data Helps
20% 40% 60% 80%0
0.10.20.30.40.50.60.70.80.9
1
Percentage of “bad” data discarded
Accuracy
Result and analysis
Improvement #Participants
0-0.25 0.26-0.50
0.51-0.75
0.76-1.00
02468
1012141618
#Partic-ipants
#Im-provement Clean#Improve-ment Pruned Clean
Confidence factor
Number ofPartici-pants
Result and analysis
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