Privacy in Online Social Media project. Uses a confessions page to map out relationships between 600 members of an organization (about 80% of the population), finding relevant nodes, social dynamics. Reveals privacy flaws achieving success in determining friendships, and security concerns by means of diagnosing internet usage patterns in the institution. Exploits the Facebook Graph API.

1. Company Proprietary and Confidential Copyright Info Goes Here Just Like This
S No Questions Asked | On a scale of 1-5, p1 p2 p3 p4 p5
1
Please specify how accessible or public you thought this
information was.
5 5 5 4 5
2
How correctly/truly does the document reveal information/facts
about you?
2 3 3 5 5
3
To what extent do you feel the attached document reveals
information about your personal connections/friends?
2 3 2 3 4
4
How comfortable are you with people predicting your friend-
circle/relationships based on the above?
1 2 3 4 5
5
How likely do you think it is that future employers will try and dig
up this information about you?
3 2 4 4 5
6
If a future or current employer/company/college were to gain
access to this information, do you feel it might hurt your
career/professional growth?
2 4 3 4 3
7
Do you think this would be a problem were a parent or relative to
see this information?
5 1 4 3 4
8 Does this highlight a privacy concern in your opinion? 3 4 4 4 2
9
Do you regret any of this public information that is already up on
the social media platform?
2 3 3 2 1
10 Will you try and be more careful about your online profile hereon? 2 3 1 4 1
The IIITD Compliments Page | PSOSM | Aditya Gupta and Akanksha Singh
0
200
400
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Activity by day of the week/by hour of day
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
0
5
10
15
20
25
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
Online FB Activity of IIITians
by day of the Week
0
100
200
300
Feb Mar Apr May Jun Jul Aug
Month-wise Activity (2012)
0
100
200
300
7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Time-Based Frequency
of Posting
0
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400
600
800
0 2 4 6 8 10 12 14 16 18 20 22
Time-Based Frequency of
Comments
0
100
200
300
400
500
600
700
800
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
NUMBEROFCOMMENTS
NUMBER OF LIKES (EXACT)
# of Likes per comment
Posts on IIITD Comp: Total
Comments: Total (1082 liked)
Likes: Total (7400 on comments)
People: Total (tagged 324 posts)
900+
4081
14k
800
Above Graphs:
Entire Network coloured on Eigenvector Importance - with IIITD
Network on IIITD Compliments
0
20
40
60
80
100
120
20 Most Active (comments):
[ Hidden for your Privacy ]
# of
Conversations
Shared
Average # of
People shared
with (of 800)
1 8.9325
2 2.01
3 0.795
4 0.41
5 0.2075
Hour of Day 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Monday 1 3 8 8 4 8 2 2 7 3 24 48 26 33 87 34 46 25 7 1 0 0 0 0
Tuesday 1 1 1 1 3 8 8 4 3 1 9 8 12 12 6 7 28 28 21 5 8 4 3
Wednesday 1 1 3 1 1 3 2 0 2 17 2 3 7 2 7 7 53 72 86 138 47 15 0 3
Thursday 4 1 4 7 13 11 18 10 9 4 18 42 97 69 39 62 47 41 117 122 41 9 8 4
Friday 2 1 3 2 6 8 23 8 8 11 9 1 14 6 19 16 66 60 43 316 207 28 5 2
Saturday 1 1 1 7 15 15 37 23 25 22 38 41 36 30 54 47 30 51 62 29 7 5 2 6
Sunday 0 1 0 4 4 5 7 5 5 10 15 23 62 59 56 42 87 158 86 28 9 15 4 3
Scoring Metric:
Directed +1 per like, Undirected +4(3) per comment conversation -
with past work suggesting 4/1 marketing rate of comments/likes
Below:
In order, top likers, top liked, most “popular”, least “popular”, top
relationships, and top one-sided relationships – with
at least 10 likes in or out (unless specified)
Person Likes Received
Akshit Nanda 264
Prakhar Gupta 206
Lakshay Pandey 157
Sumit AggerWal 142
Arjun Ahuja 139
Person Likes committed
Jahnavi Kalyani 153
Arjun Ahuja 146
Sakshi Saini 137
Tuhi Nanshu 136
Purujit Negi 136
Most Popular Score
Maneet Singh 0.95
Sanchit Sharma 0.944444
Tushar Gupta 0.928571
Ankit Agarwal 0.928571
Prateek Gaur 0.923077
Akanksha Cullen 0.923077
Abhi. Gautam 0.921053
Sanchit Garg 0.916667
Score
Threshold
Relationships
identified
Density
1 6359 0.009948
3 3719 0.005818
5 1249 0.001954
7 875 0.001369
9 539 0.000843
Relationship Score Person1 Person2
79.5 PrEeti Singh Khushboo Mandal
62.5 Sakshi Saini Purujit Negi
59.5 PrEeti Singh Jyoti Gangwar
57.25 Purujit Negi Akshit Nanda
56.5 Utkarsha Bhardwaj Purujit Negi
55.25 Anish Kumar Akshit Nanda
52 Purujit Negi Apoorv Saini
49.25 Priyanshi Mittal Mannika Solanki
48.25 PrEeti Singh Kriti Pandey
47.5 Tuhi Nanshu Akshit Nanda
47 Utkarsha Bhardwaj Akshit Nanda
45.25 Surabhi Kabby Kabra Priyanshi Mittal
45 Sumit AggerWal Purujit Negi
44.5 Kshitiz Bakshi Kirti Lamba
42.5 Srishty Grover Priyanshi Mittal
41 Srishty Grover Akshit Nanda
41 Tanya Mishra Sakshi Saini
Ratio of
likes of
liker/liked
Total likes
between
(filtered for 7+)
Liker Liked
1 14 Khushboo Mandal Kriti Pandey
1 11 Tanya Mishra Sakshi Saini
1 10 Inshu Kumar Chugh Prakhar Gupta
1 9 Tuhi Nanshu Lakshay Pandey
1 9 Jahnavi Kalyani Lakshay Pandey
1 8 Jahnavi Kalyani Prakhar Gupta
1 7 Tuhi Nanshu Anish Kumar
1 7 Sampoorna Biswas Akshit Nanda
1 7 Jahnavi Kalyani Sumit AggerWal
1 7 Divya Bansal Lakshay Pandey
1 7 Arjun Ahuja Sumit AggerWal
1 7 Apoorv Saini Sumit AggerWal
0.92 25 Tuhi Nanshu Akshit Nanda
0.866667 15 Purujit Negi Sumit AggerWal
Person
Eigenvector
Centrality
Purujit Negi 0.011791
Prakhar Gupta 0.010965
Nikhil Nagpal 0.010064
Arjun Ahuja 0.010064
Sakshi Saini 0.009914
Siddharth Gupta 0.008787
Srishty Grover 0.008487
Akshit Nanda 0.008487
Deepak Wali 0.008412
Utkarsh Bhardwaj 0.008412
Bijender Rai 0.008261
Amol Verma 0.008261
Shayan Lahiri 0.008036
1
2
4
8
16
32
64
128
256
1 20 400
Likes in/Likes Out Correlation
Pearson’s Correlation: 0.65
Avg. of 4.10 likes per
comment with likes Person
Hub or
Importance
Purujit Negi 0.011791
Prakhar Gupta 0.010965
Nikhil Nagpal 0.010064
Arjun Ahuja 0.010064
Sakshi Saini 0.009914
Siddharth Gupta 0.008787
Srishty Grover 0.008487
Akshit Nanda 0.008487
Deepak Wali 0.008412
Utkarsha Bhardwaj 0.008412
Bijender Rai 0.008261
Amol Verma 0.008261
Shayan Lahiri 0.008036
Clustered Groups
(7 clusters)
“Importance”
Highest In-Between-ness
(Occurrence in shortest paths)
Min-Hops to Farthest
Self-Likers # of Likes
Akshit Nanda 2
Aman Singhal 2
Anjali Ujjainia 4