branded with a scarlet “c”: cheaters in a … with a scarlet “c”: cheaters in a gaming ......
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BRANDED WITH A SCARLET “C”: CHEATERS IN A GAMING SOCIAL NETWORK Jeremy Blackburn, Ramanuja Simha, Nicolas Kourtelis, Xiang Zhou, Matei Ripeanu, John Skvoretz, and Adriana Iamnitchi University of South Florida University of British Columbia
1
Video games are a huge industry
• Modern Warfare 2 released Nov. 2009 • First 24 hours of release
• 4.7 million units sold • $310 million in revenue
• First 5 days of release • 8 million online players
• All these numbers eclipsed by MW3 in 2011!
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Biggest entertainment
launch in history!
Multiplayer gaming: growing eSports industry
Major League Gaming claims 225% growth from 2010 to 2011
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“Flash” makes $250k a year
playing StarCraft!
Team Na’Vi won $1 million in the DOTA Intl.
Tournament
But not all is well…
• Fame and fortune attracts deviant behavior • Virtual goods worth $ attract criminal element • Competitive gameplay attracts cheaters
• Multiplayer games are a distributed system • Some computation left to gamers’ machines • Susceptible to attacks
• $100k a year to cheat creators for single game
4
What can we learn from a gaming community? • Social systems have unethical actors • Cheating in games is black and white • Theories indicate unethical behavior has a social
component
6
What are the network characteristics of unethical actors in a large scale online community?
Steam Community
• Large online social network for PC gamers • Built on top of Steam digital delivery platform • Purchased games permanently tied to account • Steam account required to create Steam Community
profile • Steam Community profile not required to play games
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Steam Community Profile • Unique SteamID • Friends list • User specified location • Cheating flag (VAC ban) • Nickname (mutable) • Date of account creation • Screenshots • Videos • Comments (“wall posts”) • Profile information • Game reviews • Gameplay ownership/stats • Virtual goods inventory
8
Cheating flag
The cheating flag
• Cheating automatically detected via Valve Anti Cheat system • Method and timestamp not public • Delayed application
• Permanent • Publicly viewable
• Even private accounts
• Can’t play on VAC secured servers • Only applies to the game that was cheated in
• Most servers are VAC secured • 4,200 of 4,234 Team Fortress 2 servers
• Cheater not permanently removed from Steam Community
9
Steam Community data set
• Data collected March 16 – April 3, 2011 • Distributed BFS using Amazon EC2
• Cheaters make up 7% of profiles • 7% of cheaters have private profiles
• 3% of non-cheaters with private profiles
• Cheaters as likely to be friends-only as private • Non-cheaters about 3 times as likely to be friends-only as private
10
Cheaters more likely to be
private
Type Nodes Edges Profiles Public Private Friends-only
Location set
All users 12,479,765 88,557,725 10,191,296 9,025,656 313,710 851,930 4,681,829
Cheaters - - 720,469 628,025 46,270 46,714 312,354
Cheaters more likely to be private than non-cheaters
• How are cheaters positioned? • In the social community • Geographically
• What is the reaction to the cheating brand? • From cheaters themselves • In the social network • In game
• Does the social structure influence cheating?
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Observing the gaming community
• How are cheaters positioned? • In the social community • Geographically
• What is the reaction to the cheating brand? • From cheaters themselves • In the social network • In game
• Does the social structure influence cheating?
12
Observing the gaming community
13
CC
DF:
P(d
egre
e ≥
x)
10-8
10-7
10-6
10-5
10-4
10-3
10-2
10-1
100
100 101 102 103
Degree
Steam CommunityCheaters Only
Cheaters are well embedded…
…but are not central
• Cheaters under-represented among most central players • Cheaters make up 7% of player population, but far less than 7% of
the top 0.1% central users • Not adequately represented until top 5% central users
14
Top-N% 0.1 0.5 1.0 5.0 10.0 Degree 3.25 4.46 5.11 7.06 8.20
Betweenness 5.16 5.95 6.35 7.86 8.58
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1Fraction of cheater friends
CheatersNon-cheaters
Cheaters have more cheater friends
15
CD
F: P
(frac
tion ≤
x)
70% of cheaters’ friends lists are at least 10% cheaters
15% of cheaters have mostly cheater friends
Fraction of cheaters in neighborhood
0.000.020.040.060.080.100.120.140.160.18
USABrazil
RussiaGermany
France
UK Poland
Canada
Australia
Sweden
Denmark
Norway
Rat
io
Cheater : Non-cheater
Non-uniform geo-political distribution
16
Ratio of cheaters to non-cheaters
Cheaters are geographically closer
17
Network # of nodes # of edges <Duv> (km) <luv> (km) <NL> Steam Community 4,342,670 26,475,896 5,896 1,853 0.79 Cheater-to-Cheater 190,041 353,331 4,607 1,761 0.79 BrightKite 54,190 213,668 5,683 2,041 0.82 FourSquare 58,424 351,216 4,312 1,296 0.85
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1Node Locality
CDF
Steam CommunityCheater-to-Cheater
Cheaters Only
CD
F: P
(nod
e lo
calit
y ≤
x)
• How are cheaters positioned? • In the social community • Geographically
• What is the reaction to the cheating brand? • From cheaters themselves • In the social network • In game
• Does the social structure influence cheating?
18
Observing the gaming community
Cheaters try to hide when caught…
• Recrawl in October, 2011 • 43,465 non-cheaters now flagged as cheaters • 13% had privacy setting change
• Compared to a bit more than 3% of non-cheaters
• 10% from public to more restrictive setting • Compared to less than 3% of non-cheaters
19
…and for good reason: the community disapproves
Change in Degree
Cheaters Non-cheaters
Net loss 44% 25%
Net gain 13% 36%
No change 43% 39%
20
Cheaters tend to lose friends while non-cheaters tend to gain friends
Gameplay logs
• Team-based, objective oriented • Two teams, nine classes • “Friend” interactions • “Foe” interactions
• Popular TF2 server • VAC secured • Community owned • April 1 - June 8, 2011
• Interaction network
• 10,354 players • 93 cheaters • 486,808 edges
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Cheaters not mistreated in games
22
10-5
10-4
10-3
10-2
10-1
100
100 101 102 103 104
Unique gameplay friends
CC
DF
Non-cheatersCheaters
10-5
10-4
10-3
10-2
10-1
100
100 101 102 103 104
Unique gameplay foes
Non-cheatersCheaters
CC
DF:
P(in
tera
ctio
n pa
rtner
s ≥
x)
Number of distinct interaction “friends” Number of distinct interaction “foes”
CC
DF:
P(in
tera
ctio
n pa
rtner
s ≥
x)
• How are cheaters positioned? • In the social community • Geographically
• What is the reaction to the cheating brand? • From cheaters themselves • In the social network • In game
• Does the social structure influence cheating?
23
Observing the gaming community
Does cheating spread over social links?
• Label nodes with the date of their VAC ban • 180-day snapshots of the cheater status of nodes over
time • For each snapshot, only those players whose ban date is from a
previous snapshot are treated as cheaters
24
Do the neighborhoods for newly-marked cheaters differ from those of non-cheaters?
Historical ban dates • 3rd party web site, vacbanned.com, provides historical data on when a VAC ban was first observed • Dates must be treated as banned “on or before”
25
0 0.2 0.4 0.6 0.8
1
2009-11-01
2010-01-01
2010-03-01
2010-05-01
2010-07-01
2010-09-01
2010-11-01
2011-01-01
2011-03-01
2011-05-01
2011-07-01
2011-09-01
2011-11-01
CD
F
Attempt made to populate database by vacbanned.com administrators in May, 2011
P(b
an o
bser
ved
befo
re d
ate)
26
10-6
10-5
10-4
10-3
10-2
10-1
100
100 101 102
Number of cheater friends
Interval starting 2009-12-30
CC
DF
CheatersNon-cheaters
10-6
10-5
10-4
10-3
10-2
10-1
100
100 101 102
Number of cheater friends
Interval starting 2010-06-29
10-6
10-5
10-4
10-3
10-2
10-1
100
100 101 102
Number of cheater friends
Interval starting 2010-12-26
10-6
10-5
10-4
10-3
10-2
10-1
100
100 101 102
Number of cheater friends
Interval starting 2011-06-25
10-610-410-2
101 102
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1Fraction of cheater friends
Interval starting 2009-12-30
CD
F
CheatersNon-cheaters
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1Fraction of cheater friends
Interval starting 2010-06-29
CheatersNon-cheaters
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1Fraction of cheater friends
Interval starting 2010-12-26
CheatersNon-cheaters
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1Fraction of cheater friends
Interval starting 2011-06-25
CheatersNon-cheaters
0.4
0.8
0.4 0.8
CC
DF:
P(n
um c
heat
er fr
iend
s ≥
x)
CD
F: P
(frac
che
ater
frie
nds ≤
x)
Evolution of cheaters’ social structure
100
101
102
103
104
105
106
107
10 20 30 40 50 60 70 0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
Num
ber o
f pla
yers
Prob
abilit
y
Number of cheater friends
New cheatersNon-cheaters
Probability to cheatSmoothed probablity
Social ties as predictor of cheating 27
(*plot not in paper)
Num
ber o
f pla
yers
Pch
eat(n
um c
heat
er fr
iend
s)
• Increasing probability of a player becoming a cheater as the number of cheaters in his social neighborhood increases*
• Decision tree classifier had ROCA of 0.61 based on number of cheater friends
Summary of results
• Homophily between cheaters • Even though cooperation not necessary
• Cheaters’ distribution not uniform • In social network • Geo-politically
• Cheaters face social penalty • But not in game
• Cheating behavior spreads via social links • Number of cheater friends predictor of future cheating
28
Impact
• Large scale study of unethical actors in online community • Correlation of unethical behavior and network structure • Useful for building models of unethical behavior
• Cheating is a social problem • Community serves out social punishment • Suggests exploring other social solutions for deviant behavior
• Scale of cheating of particular concern for gamified systems • Our study exposes a likely lower bound on cheating behavior • Social predictors can narrow focus to at-risk cheaters
29
Social Network Data Collection
• Distributed BFS • 6,445 random seeds • Up to 6 `m1.medium’
instances • Coordinated with
Amazon SQS queue • Two crawls
• March 16th – April 3rd, 2011 • October 18th – October 29th, 2011
32
Internet
steamcommunity.com
CrawlerCrawler
Results Database
CrawlerCrawler
Crawler
ID2ID3ID4
SQS Queue
ID1
• More interaction partners (even on a single server) than declared Steam friends
• Steam Community friendships have meaning in-game • Declared friends likely to have more interactions with each other
than non-friends
33
10-810-710-610-510-410-310-210-1100
100 101 102 103 104
Degree
CCDF
SocialInteraction
10-810-710-610-510-410-310-210-1100
100 101 102 103 104
Number of interactions per pair
Non-declaredDeclaredC
CD
F: P
(deg
ree ≥
x)
CC
DF:
P(n
um in
tera
ctio
ns ≥
x)
Interaction vs. declared relationships
0.7
0.75
0.8
0.85
0.9
0.95
1
100 101 102 103
Net change in neighborhood size
CDF
Cheaters NegativeCheaters Positive
Non-cheaters NegativeNon-cheaters Positive
Change in Degree Cheaters Non-cheaters
No change 43% 39% Net gain 13% 36% Net loss 44% 25%
34
Cheaters tend to lose friends while non-cheaters tend to gain them Cheaters lost 2x the number of friends that they gained
CD
F: P
(cha
nge
in d
egre
e ≤
x) The community is disapproving
• Overlap between two neighborhoods is an indicator of social strength
• Cheaters form stronger relationships with each other than with non-cheaters • Overlap of cheater-cheater and non-cheater-non-cheater
neighborhoods greater than overlap of mixed neighborhoods
CD
F: P
(ove
rlap ≤
x)
Do cheaters form strong relationships?
35
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
1
0.001 0.01 0.1 1Overlap in friendships network
CDF
CheatersNon-cheatersCheaters, C-CNon-cheaters, NC-NC
Distribution of cheater friends
• Cheaters likely to have more cheater friends when compared to non-cheaters
36
10-810-710-610-510-410-310-210-1100
100 101 102 103
Cheater Degree
CC
DF
CC
DF:
P(d
egre
e ≥
x)
Privacy settings and inferred degree
• Position in network inferable from your public friends
37
10-810-710-610-510-410-310-210-1100
100 101 102 103
Degree
No ProfilePublicFriends-OnlyPrivate
Cheating behavior outside of gaming • Academics
• Plagiarism • Cheating on exams
• Personal relationships • Cheating on your spouse
• Real-world crime • Confidence scams
• Minor law breaking • Speeding
• Real-world sports • Steroids • “Diving” in soccer
• Cyber crime • Spam • Malware distribution
• Business • Accounting fraud • Corporate espionage
39