Detecting Real Money Traders in MMORPG by Using Trading Network
Atsushi Fujita

Hiroshi Itsuki

Hitoshi Matsubara

Future University Hakodate
116-2, Kameda-nakano-cho, Hakodate, 041-8655, Japan{fujita,g3106001,matsubar}@fun.ac.jp

Abstract
We have developed a method for detecting real money traders(RMTers) to support the operators of massively multiplayer
online role-playing games (MMORPGs). RMTers, who earncurrency in the real world by selling properties in the virtualworld, tend to form alliances and frequently exchange a hugevolume of virtual currency within such a community. The
proposed method exploits (i) the trading network, to identify
the communities of characters, and (ii) the volume of trades,
to estimate the likelihood of communities and characters becoming engaged in real money trading. The results of an experiment using actual log data from a commercial MMORPG
showed that using the trading network is more effective in
detecting RMTers than conventional machine learning methods that assess individual character without referring to the
trading network.

Introduction
These days, a variety of online services, including online
games, are operated on high-speed computer networks. Online games include not only traditional games that sharedata via the network but also those facilitating interaction
between numerous players. A representative genre of thelatter is massively multiplayer online role-playing games(MMORPGs), in which thousands of players share both timeand space. The motivation for playing online games is not
limited to having fun. Players often pursue wealth in the virtual world, which can be in the form of a character’s status,
special items, and virtual currency.
Online games have been rapidly gaining popularity andtheir business models have been improved so as to better satisfy players. However, as online games evolve andsocial function and economy in the virtual world comesto resemble that of the real world, various problems haveemerged, such as conflict and harassment between players
and abusive access to the game system servers. Most ofthese problems are not specific to online games. They arealso observed in other online services including social networking services and auction networks (Castronova 2005;
Lehdonvirta 2009).
One of the most controversial actions a user of online services, including online games, can perform is real moneyc 2011, Association for the Advancement of Artificial
Copyright ⃝Intelligence (www.aaai.org). All rights reserved.

trading (RMT). RMT is an economic activity in which virtual properties, such as currency, items, and even characters, are exchanged for currency in the real world, i.e., realmoney. Game operators have different attitudes toward RMTdepending on the type, design, and business model of theirgame. There are two major opposing attitudes. One regards
RMT as a harmless act and seeks ways to bring out its merit,
such as by accelerating personal trades between players andcutting down costs for setting up a physical store. One famous example of this attitude in action is Second Life1 . In
contrast, the other sees RMT as the cause of problems andprohibits it in their games.
Most of the MMORPGs in Japan prohibit RMT becauseof the considerable problems it causes. To keep the virtualworld sound and peaceful, operators of such MMORPGs
have been taking strong actions against RMT, for example
by banning the accounts of real money traders (RMTers).
The inspection process, however, requires an incredible
amount of human labor, and the online gaming community
is in urgent need of methodologies and tools to assist them.
To address this need, we developed a method of detecting RMTers by exploiting actual log data of MMORPGs, inparticular the trading network and the volume of trades.

Real Money Trading in MMORPGs
RMT in MMORPG causes serious problems for the operators, as exemplified below.
Imbalance of the virtual economy: The most critical influence is on economic balance. Dealing with a hugeamount of virtual currency for RMT causes inflation in
the virtual world and hampers the ability of general players to perform ordinary economic activities.
Direct harm of general players: RMTers often directly
harm other general players by, for example, occupying
specific locations for obtaining currency and items andattacking other players to rob their properties.
Encouragement of unauthorized deeds: RMTers
also
perform dishonest actions, such as cheating, using bots,and even taking over characters by fraud.
Discouragement of honest players: The unfair advantages of RMTers discourage honest players and often1

http://secondlife.com/

prompts them to quit the game. It also prevents new
players from joining the game.
To keep the virtual world sound and peaceful, operators of many MMORPGs have been taking strong actions
against RMT. The procedure of inspecting characters in an
MMORPG can be done in the following steps.
Step 1. Identify suspects: Operators identify characterssuspected of involvement in RMT on the basis of,for example, tip-offs from general players or selfadvertisements of RMTers.
Step 2. Verify each suspect: Operators verify whether
each suspect is an RMTer or not by referring to his/her
previous actions and utterances in log data that are
accumulated in the game server.
Step 3. Ban the account: Once a player is determined to
be an RMTer, operators ban the account with/without exhortations. Players are allowed to use multiple charactersin many MMORPGs, so all the characters operated by thesame account are disabled.
The ultimate goal of MMORPG operators is to eliminate
all RMTers from their game. However, to avoid kicking out
honest players, an incredible amount of human labor is required for the manual verification in step 2. The amount ofavailable human resources for steps 2 and 3 depends on thenumber of players/characters, the seriousness of RMT in thegiven MMORPG, and the budget for the operation.
There has been a recent trend of RMTers forming alliances for the division of RMT labor: each RMTer plays
a specific role to maximize the effectiveness of earning realmoney. A rough classification of roles is shown below.
Sellers: Sell virtual property to general players and earncurrency in the real world.
Earners: Acquire virtual property, such as currency anditems, from the virtual world, non-player characters(NPCs), and general players by repeating specific actionsin the virtual world.
Collectors: Convey virtual property from earners to sellers.
Virtual property trades can only be carried out in the virtualworld by using the MMORPG infrastructure, so in general,
the facts and figures of trading are recorded on log data in
the game server, as are other actions. The history of a user’s
actions is supposed to be a strong clue in RMTer detection.

Related WorkTo date, a few researchers have exploited log data to classify
players and characters in MMORPGs. Matsumoto and Thawonmas (2004) proposed a method for classifying players
on the basis of sequential patterns of action. However, theclasses in their definition are specific to the game title, thus
raising doubts as to the method’s applicability. Soeda andMatsubara (2008) clustered characters in an MMORPG on
the basis of the distribution of 428 types of action recorded
in a certain period. However, each cluster does not necessarily correspond to some pre-determined type of character.
Thus, their method cannot be directly applied to identification of specific type of characters, such as RMTers and bots.

There have also been several studies that attempt to detect title-independent types of characters. The most fundamental issue in such studies is to identify information thatis useful for detecting the specific character type. Thawonmas, Kashifuji, and Chen (2008) and Chen et al. (2009), whotargeted bots, reported that the frequency of actions and thedeviation of traffic to the server are useful indicators.
RMTers in MMORPGs have also been investigated. Ahmad et al. (2009) regarded the task of detecting RMTers
as a binary classification of individual character into either RMTer or not and applied various supervised machinelearning algorithms. In contrast, Itsuki et al. (2010) attempted to generate an RMTer ranking, assuming the succeeding manual inspection. However, both of them could
not achieve reasonable performance. Keegan et al. (2010)
reported several statistical tendencies observed through thetrading network in an MMORPG, and made several findings: e.g., RMTers tended to repeat trading with each other.
However, they did not investigate the behavior of RMTers in
the entire network to find distinctive features; nor did theyexamine how their findings are helpful for detecting RMTersin practice. Even in their recent work, which augmented thecharacter network with other elements (Ahmad et al. 2011),
they did not address the task of detecting RMTers.

RMTer Detection Based on Communities
Aiming at reducing the amount of human labor in the succeeding manual inspection by MMORPG operators, we regard the task of detecting RMTers as ranking all the characters through estimating their likelihood of being an RMTer.
This approach enables the operators to flexibly control theamount of human labor by the number of top-ranked suspects to be investigated. If we could give true RMTers ahigher rank than honest characters, human labor would bevastly reduced.
Unlike previous work, we exploit the trading network for
RMTer detection. We regard characters as nodes and trades
between characters as edges because most MMORPGs only
allow trades between two characters. Figure 1 depicts a trading sub-network surrounding manually identified RMTers,
which is extracted from the actual log data used in our experiment. The figure demonstrates that RMTers tend to form
dense relationships with each other. A large portion (61.5– 83.3% in our data) of the RMTers’ counterparties werealso RMTers. In contrast, general players who traded with
RMTers were significantly few (<1%). It is thus supposedthat RMTers can be detected one after another once one ofthem is detected.
On the basis of this observation, we developed a methodthat exploits the trading network to detect RMTers. The entire procedure is comprised of the following two steps.
Step 1. Extracting communities: Identify the groups ofcharacters who traded closely with each other.
Step 2. Ranking communities and characters: Rank theextracted communities in descending order of the total
volume of in-house trades and then rank characters ineach community in descending order of the total volumeof trades.

Table 1: Definition of trading networks.
Option
tb
tt
cb
ct
cv

Figure 1: Trading sub-network surrounding manually identified RMTers (red: seller, green: earner, blue: collector) andnon-RMTers (small nodes) in period B of our data.

Edge
All trades
Currency trades

Weight of edge
Existence of trades (uniform)
Number of times of trades
Existence of trades (uniform)
Number of times of trades
Volume of exchanged currency

This algorithm can effectively solve the problem as in (Newman and Girvan 2004; Newman 2004) despite its naiveness.
As summarized in Table 1, the effectiveness of focusing
on currency trades and incorporating the weight of edges
were examined by taking sum of the edge weights instead ofcounting the number of edges for calculating eii and ai .

Step 2. Ranking Communities and Characters
Step 1. Extracting Communities“Community” is defined as a set of nodes in a network thatare tightly linked to each other. Methods of extracting communities have recently been applied to various networks,
such as those of Web sites, citation networks, and users ofsocial networking services.
The task of identifying communities in a given network is
tied to determining the best division of the network. The notion of modularity, given by the following equation, was proposed by Newman and Girvan (2004) to quantify the qualityof a division of a given network.
∑
Q =(eii − a2i ),iwhere eii and ai are calculated for each community ci as
eii

=

ai

=

# of edges between nodes in ci,# of all edges in the network# of edges connecting to a node in ci.
# of all edges in the network

The more edges each community includes and the fewer
number of edges that link different communities, the larger
the modularity Q is. Note that the expected value of link ratio ai is subtracted to avoid a trivial solution, i.e., regarding
the whole network as one community.
The problem of finding a division that maximizes modularity is NP-hard. Thus, bottom-up algorithms for finding
reasonable solutions at lower complexity have been studied(Newman 2004; Clauset, Newman, and Moore 2004). We
extract communities from a given trading network by using the algorithm proposed in (Clauset, Newman, and Moore
2004). Let ∆Qij be the gain of Q if two communities ci andcj are merged. The algorithm is briefly explained below.
Step 1-1. Regard each node as a community and calculate∆Qij for each connected pair of communities ci and cj .
Step 1-2. Merge ci and cj whose ∆Qij is largest and positive into ci′ . Increment Q by ∆Qij .
Step 1-3. Update ∆Qi′ k (= ∆Qki′ ) for each community ckthat is connected to at least one of ci or cj .
Step 1-4. Repeat steps 2 and 3 while Q gains.

The main focus of previous studies on community extraction
was to discuss the properties of the extracted communities
and the original network, such as scale-free and small world,
and the appropriateness of the extracted communities. However, community extraction alone cannot function as either aclassification nor ranking method for detecting RMTers.
We therefore give ranks to communities and characters.
Following the finding in (Itsuki et al. 2010) that RMTers tend
to deal with a huge volume of virtual currency, we determine
the rank of characters by the following two steps.
Step 2-1. Communities are ranked in descending order of
the total amount of trades in the community. We examine
those of “tt,” “ct,” and “cv” as alternative options.
Step 2-2. Characters in each community are then ranked indescending order of the number of times they traded, thenumber of times they traded currency, and the volume ofexchanged currency, alternatively. For convenience, they
are also labeled “tt,” “ct,” and “cv,” respectively.

Experiment with Actual Log Data
We performed an experiment to evaluate the performanceof the proposed method, using the commercial MMORPG“Uncharted Waters Online” (Japanese version)2 as the sample game. The data provided by the game operator (TECMOKOEI GAMES Co., Ltd.) included actual log data (actionsof individual character and system alerts, but no personal information) and IDs of RMTers that are manually identified
by the operators. In our experiment, we refer to only certain
periods of log data for which manual inspection of RMTers
was carried out. Table 2 shows the volume of corresponding records in the log data and the distribution of manually
identified RMTers.

Extracted Communities
First, we extracted action records describing trades between
two characters from the log data. Table 3 summarizes thestatistics of the extracts. By targeting only traders, the number of suspects was reduced to half (all trades) and one-third(currency trades), but only one character in period D was2

http://www.gamecity.ne.jp/dol/†PeriodPeriod A
Period B
Period C
Period D

Table 2: Statistics of the provided data.

Neither characters who did not play in each period nor NPCs are included.
# of action# of chars. played# of manually identified RMTers
records
in the period†
Total Seller Earner Collector
August 30 - September 13, 2009(15 days) 308,921,785
15,24929
10
15
4
November 18 - December 8, 2009 (21 days) 417,516,270
16,47152
20
25
7February 23 - March 17, 2010(23 days) 479,468,978
18,745106
29
54
23
May 10 - May 24, 2010(15 days) 300,809,905
17,114
130
19
91
20

Table 3: Statistics of the identified trading networks.
tt: the total number of trading transactions, ct: the total number of currency trade transactions, cv: the total volume of exchanged currency,
Cov.: the number of RMTers included in the extracts among all of the manually identified ones.
All trades
Currency trades
Period# of nodes # of edgestt
Cov. # of nodes # of edges
ct
cv
Cov.
Period A
8,152
13,452 193,395
29
4,624
4,590 15,164 313,591,306,074
29
9,440
17,152 278,728
52
5,423
5,718 18,633 392,631,400,843
52
Period B
Period C
10,265
19,140 316,849
106
6,317
7,272 28,950 912,677,938,945
106
9,358
15,785 211,041
130
5,174
5,413 18,682 683,310,026,086
129
Period D

Table 4: Statistics of the extracted communities.

Option
tb
tt
cb
ct
cv

Q: modularity of the partitioned network, C: set of the extracted communities,
cm : the maximum size of the communities (maxc∈C |c|), CT : set of communities containing at least one RMTer.
Period A
Period B
Period C
Period D
Q
|C|
cm
|CT |
Q
|C|
cm
|CT |
Q
|C|
cm
|CT |
Q
|C|
cm
0.931 690 397
4 0.917646 553
3 0.903729 709
6 0.920 720 411
0.986 818 280
1 0.986
812 199
3 0.985902 397
3 0.984 885 264
0.965 895 127
5 0.956929 178
4 0.930
945 255
4 0.951 896 194
0.965 909
96
2 0.965947 153
2 0.952986 326
3 0.961 911 140
0.886 976 104
3 0.882 1,036 175
4 0.885 1,089 183
5 0.923 982 153

missed. This demonstrates that trading is a strong clue in
detecting RMTers. Another notable feature of the trading
network is the significant sparsity of edges. Only 0.04% of
pairs of traders traded with each other. This highlights thecomputational efficiency of the method’s use of community
extraction compared to clustering algorithms that compute
distance/similarity between two arbitrary nodes.
Next, we extracted communities from each of five typesof trading network (see Table 1), individually. Table 4 showssome of the results. RMTers were condensed into a few communities: |CT | was at most eight. This demonstrates the usefulness of the trading network and the community extraction
method. By taking the weight of edges into account (tt, ct,cv), we could extract larger numbers of relatively smaller
communities. In the “cv” network, the Q values were lower
and |C| values were higher than the others. We speculatethat the pairs of characters who traded less currency were
not preferably merged into the same community due to thelarge deviation of the volume of exchanged currency.
Newman and Girvan (2004) predicted that the Q value
for general networks would fall between 0.3 and 0.7. However, all of the values for our networks were higher than that
range. This implies that trades tend to be carried out between
specific pairs of characters.

Evaluation Setting: Models and Measures
We combined the following three parameters, and obtained
45 types of ranking: five types of trading network for com-

|CT |
5
6
7
6
8

munity extraction (tb, tt, cb, ct, cv), three measures for ranking communities (tt, ct, cv), and three measures for ranking
characters (tt, ct, cv).
We employed three baselines3 . The simplest baseline, “cvonly,” ranks characters in the descending order of the volume of exchanged currency4 . Note that it can only rank currency traders. The remaining two baselines are well-known
machine learning methods: naı̈ve Bayes with multinomial
distribution (MNB) (McCallum and Nigam 1998) and support vector machines (SVMs) (Vapnik 1999). While thesemethods are normally used to classify individual character
into either RMTer or non-RMTer, here we use their classification score to rank the characters, i.e., a score is calculated
for each character and then all the characters are ordered on
the basis of the score. The score of MNB was calculated
based on the posterior probability of each type of action,
because the prior p(RMTer)/p(non-RMTer) does not affectthe ranking. We estimated the posterior probability by maximum likelihood estimation with Laplace smoothing. As thescore of SVMs, we used the distance between the given char3

We also applied agglomerative hierarchical clustering and kmeans to the same data, varying the number of clusters from 2 to(the number of characters) −1. However, they produced only poor
results despite their significant computation time, so we excluded
them from comparison.
4This is not equivalent to the handled currency proposed in (Itsuki et al. 2010). They also counted currency flow between eachcharacter and the game system, such as shops and treasures.

acter and the learned hyperplane multiplied by the estimatedlabel l ∈ {−1, +1}. For a fair comparison to the proposed
method, we applied MNB and SVMs to three sets of characters: all characters, traders, and currency traders. The models
were trained5 on data from three periods other than the target
period, referring to all of the 338 types of action recorded in
the log data and their frequency that individual character hadtaken.
As mentioned earlier, the amount of human labor available for the verification of suspects depends on the situation.
Thus, recall and precision were used to evaluate the performance at an arbitrary amount of human labor. Let N be thenumber of RMTers. They can be calculated for arbitrary k
top-ranked characters to be investigated.
# of correctly identified RMTers,
Recall =# of RMTers (= N )# of correctly identified RMTers.
Precision =# of players identified as RMTer (= k)The total performance of a ranking measure was also evaluated by average precision given by the following equation.
1 ∑P (n),
N n=1
N

AveragePrecision(N ) =where P (n) is the precision when n RMTers are identified.
We used Wilcoxon signed rank-sum test to compare two arbitrary ranking measures having the N corresponding precision rates, at the significance level p < 0.01 (one-sided).

Results of RMTer Detection
We first evaluated each parameter of the proposed methodby fixing the other two parameters; for example, five types of
trading networks were compared for each of the 3×3 pairs of
the other two parameters. We observed the following, eachof which was common to all four periods.
Extracting the communities: Weighting the edges of the
network (tt, ct, cv) did not necessarily yield a better performance than models that only took the existence oftrades (tb, cb) into account. Moreover, different measuresachieved the best result depending on the period.
Ranking the communities: Rankings based on the volume
of exchanged currency (cv) were superior to those basedon the number of trades (tt, ct). This is natural because
RMTers tend to exchange a large volume of currency atonce to effectively earn real money.
Ranking the characters: Currency trades (ct, cv) produced better results than relying on all trades (tt) in ranking the characters. This implies that virtual currency ismore popular in RMT than other tradable elements.
Ten combinations of the above three parameters (5×1×2)
remained as comparable versions of the proposed method.
Henceforth, a combination of the three parameters is denoted by concatenating them with a single dot, e.g.,“tt.cv.ct.”
5

While we implemented MNB, as an implementation of SVMs,
we used SVMlight V6.02 (http://svmlight.joachims.org/) on its
default setting, such as linear kernel.

∗

Table 5: Average precision of selected models.

P (130) is given assuming the worst case (130 / 17,114)
Model
Period A Period B Period C Period D
Char. set
N = 29 N = 52 N = 106 N = 130∗
cv only
0.320
0.440
0.484
0.466
MNB
All chars
0.239
0.305
0.342
0.357
Traders
0.273
0.367
0.381
0.416
∗
Cur. traders
0.336
0.391
0.420
0.469
SVMs
All chars
0.340
0.198
0.438
0.517
Traders
0.310
0.567
0.408
0.553
∗
Cur. traders
0.356
0.554
0.421
0.599
Proposedtb.cv.ct
0.385
0.900
0.499
0.404
tb.cv.cv
0.393
0.860
0.503
0.388
tt.cv.ct
0.328
0.882
0.459
0.648
tt.cv.cv
0.362
0.837
0.448
0.624
∗
0.570
cb.cv.ct
0.167
0.883
0.524
∗
cb.cv.cv
0.179
0.832
0.510
0.554
∗
ct.cv.ct
0.764
0.626
0.515
0.557
∗
0.756
0.606
0.498
0.540
ct.cv.cv∗
cv.cv.ct
0.522
0.573
0.513
0.547
∗
cv.cv.cv
0.547
0.564
0.498
0.529

Table 5 summarizes the average precision of various models. The bold-faced scores indicate those significantly better
than all of the baselines, while the best score for each period is underlined. For all of the four periods, one of ourmodels achieved the best result, thus demonstrating the effectiveness of using the trading network. However, none ofthem always beat all of the baselines. Interestingly, MNB
and SVMs tended to give a large weight to the actions associated with trading, which corroborates the usefulness ofthe trading information. In the scoring phase, however, thesemodels also regarded the trades of honest characters as “badbehavior” and consequently pushed them up in ranking.
As shown in Table 3, RMTers are inseparable from trades.
We therefore expected that precision could be higher when
we targeted only traders. However, only MNB showedmonotonic improvements. Surprisingly, SVMs were not stable at all: the average precision in period C deteriorated
when it dealt with only traders. The rankings of the proposed
method based on currency trades were not necessarily better
than those based on all trades.
Figure 2 displays the recall-precision curves of the best
version of the proposed method and the best baseline foreach period. Similar to these curves, most of the models
demonstrated the trade-off between recall and precision. In
periods A and B, some versions of the proposed method hadsignificantly higher precision than the baselines at a wide
range of recall. In contrast, however, the advantage of the
proposed method over the baselines was limited to a narrow
range of recall and small in periods C and D.
By definition, communities themselves do not guarantee
to contain only RMTers. Thus, the strict order of ranking
strategy of the proposed method, i.e., communities first andcharacters second, was too naive to achieve a reasonable performance. Even if RMTers in a community were exhaus-

1
0.8

0.6
0.4
0.2
0

0.2

0.4

0.6

0.4
0.2

Best
Best BL

0

0.6

0.8

1

0

0.2

Recall

0.4

0.6

0.4

0.8

(b) Period B

1

0

0.2

0.4

0.6

Recall(c) Period C

0.6
0.4
0.2

Best
Best BL0

Recall(a) Period A

0.6
0.2

Best
Best BL0

Precision1
0.8

Precision1
0.8

Precision

Precision

1
0.8

Best
Best BL

0
0.8

1

0

0.2

0.4

0.6

0.8

1

Recall(d) Period D

Figure 2: Recall-Precision curves of the best version of the proposed method and the best baseline.
tively identified, the remaining honest characters in the community were still prioritized over RMTers in the succeeding
communities. In reality, the operators can conduct the inspection more intelligently, exploiting the ranked communities. For example, the operators should look ahead characters in the succeeding communities if a certain number ofhonest characters in a community under investigation are incorrectly identified as RMTer. With such a dynamic ranking
mechanism, the performance can be improved further.

Conclusion and Future WorkIn this paper, we addressed the task of detecting RMTersin MMORPGs to support game operators and proposed amethod that exploits the trading network and the volume oftrades. The results of an experiment using actual log data
demonstrated the effectiveness of using the trading data.
The trading network and the volume of trades are substantial clues in detecting RMTers, because the exchange rate
tends to be extremely low and all the trades are recorded at
the game server. Thus, further investigation into trading networks is promising to boost the performance. For example,
several aspects of trading, such as currency and items, can be
distinguished by introducing mixture models (Newman andLeicht 2007). Augmentation of the network with other components than characters (Ahmad et al. 2011) is also worth investigating. We also intend to evaluate the proposed methodfrom two aspects. The one is to apply it to a shorter period oflog data to determine the robustness of the method: whether
it can be used to prevent the disposable use of characters.
The other is its application to other MMORPG titles to empirically justify its generality.

Acknowledgments
The authors are deeply grateful to TECMO KOEI GAMESCo., Ltd. who provided actual log data of MMORPG and
the anonymous reviewers for their valuable comments. This
work was supported by JST Core Research of Evolutional
Science & Technology (CREST) Project entitled “Development Support and Evaluation of On-line Games.”

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