Trolls Identification within an Uncertain Framework

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by  Imen Ouled Dlala

Trolls Identification within an Uncertain Framework
Imen Ouled Dlala, Dorra Attiaoui, Arnaud Martin, and Boutheina Ben Yaghlane
Abstract—The web plays an important role in people’s social some other works tried to detect not just their characteristics
lives since the emergence of Web 2.0. It facilitates the interaction but also their presence in order to avoid them. To address
between users, gives them the possibility to freely interact, share
this problem Cambria et al. [14] proposed a technique based
andcollaboratethroughsocialnetworks,onlinecommunitiesforums,
on semantic computing to automatically detect and check
blogs,wikisandotheronlinecollaborativemedia.However,another
side of the web is negatively taken such as posting inflammatory web trolls. This work aims to prevent the malicious people
messages. Thus, when dealing with the online communities forums, from emotionally hurting other users or communities within
the managers seek to always enhance the performance of such the same social network. In another work Ortega et al. [15]
5 platforms.Infact,tokeeptheserenityandprohibitthedisturbanceof
proposed a method to classify users in a social network
1 the normal atmosphere, managers always try to novice users against
regarding to their trustworthiness. The goal of their method
0 these malicious persons by posting such message (DO NOT FEED
2 TROLLS). But, this kind of warning is not enough to reduce this is to detect trolls from the other users by preventing such
phenomenon.Inthiscontextweproposeanewapproachfordetecting malicious users to gain high reputation in the network. Patxi
n
malicious people also called ’Trolls’ in order to allow community et al. [16] dealt with Trolling users on twitter social network.
a
managers to take their ability to post online. To be more realistic,
J Thesestudieswereexploredindifferentsocialnetworkswithin
our proposal is defined within an uncertain framework. Based on
1 certain framework.
the assumption consisting on the trolls’ integration in the successful
2 discussion threads, we try to detect the presence of such malicious When dealing with real-world applications, the massive
users.Indeed,thismethodisbasedonaconflictmeasureofthebelief amounts of data are inseparably connected with imperfection.
]
I functiontheoryappliedbetweenthedifferentmessagesofthethread. In fact, this kind of data can be imprecise and/or uncertain
A In order to show the feasibility and the result of our approach, we
or even missing. Different theories have emerged to deal with
test it in different simulated data.
. this kind of data such as fuzzy set theory [21], possibility
s
c Keywords—Q&AC,trolls,belieffunctiontheory,conflictmeasure. theory [22] and belief function theory [1]. Thus, to be closer
[
to reality and to obtain more relevant results, we propose a

1 new method dealing with uncertain data. This method aims to
v I. INTRODUCTION detecttrollsinQ&ACusingtheframeworkofbelieffunction.
2 THEwaywelookfor,andacquireinformationhasshifted The paper proceeds as follows: in Section 2, we introduce
7
the Q&AC and briefly review related works. In section 3,
2 greatlyintotoinstant,easyandlowcostprocess.Infact,
we present the necessary background regarding the different
5 thanks to the Internet one can make a research in any given
0 topic, get a huge amount of information by a simple click. conceptsofthebelieffunctionstheory.Wedefinethedifferent
. steps of our proposal based on a conflict measure in section
1 Although, for some problems it is difficult to get satisfactory
0 answers by searching directly on a traditional search engine. 4. Finally, we present the feasibility of the proposed method
5 Instead, we prefer to find someone who has expertise or on an illustrated example.
1
experience. In order to have the best answer, one of the
:
v tools that has widened the scope of information exchange is II. Q&AC:QUICKOVERVIEW
i
X Question Answering Communities (Q&AC). These systems In this section we introduce some concepts related the
allow everyone to contribute as much as they can on a given
r Q&AC. First we will start by presenting the main actors in
a community.Unfortunately,notallmessagescanbeconsidered
these forums, then a little overview on sources identification
as reliable: some users claim themselves as experts, and
and finally the levels of uncertainty we can face in Q&AC.
other people post messages without any utility for the one
who is seeking for answers. Thus, the managers of these
communities seek to always enhance the performance of such A. Users within Q&AC
platforms. Although, the increase of the useless messages can
Users are considered as the main actors within Question
beattributedtothepresenceoftrolls.Thetermoftrollinghas
Answering Communities. We can define different types such
been defined in several works within different communities,
as experts, trolls and learners.
including [2], [6] and [17]. These Malicious people intend to
• Reliableuser/Expert:apersonwhoisveryknowledge-
insidiously mislead the subject of the discussion in order to
able about or skilful in a particular area.
provoke controversy and disrupt the discussion. They aim to
• Troll: a person who seeks to disturb the serenity of the
makenormalusersfallintotheirtrapsbydeviatingthemfrom
concernedcommunity.Hispurposeistocreatecontrover-
the main topic of the discussion. In fact, the only way to deal
sial debates by multiplying irrelevant messages that we
with a troll is to ignore him, or detect his presence in order
keep unanswered.
to notify him or take away his ability to post online. Thus,
• Learner: a normal user of the Question Answering
Community, trying to gain information and expertise.
Manuscriptreceived.
B. Sources Identification within Q&AC B. Basic belief assignment (bba)
AbbaisdefinedonthesetofallsubsetsofΩ,namedpower
Several researches have been exploring this field, trying to
set and noted 2Ω. It affects a real value from [0,1] to every
evaluatesourcesofinformationinQ&AC.SuchasBouguessa
subsetof2Ω reflectingsourcesamountofbeliefonthissubset.
et al. [7] who proposed a model to identify authoritative
A bba m verifies:
users based on the number of best answers provided by them.
A best answer is selected either by the asker or by other
users via a voting procedure. In [12], the author focused (cid:88) m(X)=1. (1)
on the selection of questions a user would choose for an-
X⊆Ω
swering. Based on these studies, experts prefer answering
We consider any positive elementary mass m(X)>0 as a
questions where they have a higher chance of making a
focal element such that X belongs to 2Ω.
valuablecontribution.Recentlyin[13],theauthorsproposeda
framework for evaluating both the reliability and the expertise
of an information provider. Considering some cognitive and C. Combination rules
behavioral criteria of the users, they were able to establish Many combination rules have been proposed taking in
a trust system. Using a response matrix summarizing the consideration the nature of the sources.
interactions between peers of persons, each one is capable 1) Dempster’s combination rule: The first one was pro-
of estimating and providing an opinion. Using the subjective posedbyDempsterin1967[1]whichisaconjunctivenormal-
logic to aggregate these evaluations, they provided later a ized combination rule also called the orthogonal sum. Given
global reliability and expertise value for each user within two mass functions m and m , for all X ∈ 2Ω, X (cid:54)= ∅, the
1 2
Q&AC. Dempster’s rule is defined by:
1 (cid:88)
m (X)=m ∩m (X)= m (Y )m (Y ) (2)
D 1 2 1−k 1 1 2 2
C. Uncertainty within Q&AC Y1∩Y2=X
(cid:80)
where k = m (Y )m (Y ) is the inconsistency
When dealing with information provided by humans, we Y1∩Y2=∅ 1 1 2 2
of the fusion (or of the combination) can also be called the
are facing severallevels ofuncertainty. Gjergjiet al.proposed
conflict or global conflict. (1−k) is the normalization factor
three levels for Q&AC [11], the first one is related to the
of the combination in a closed world.
extraction and integration of uncertainty, the second deals
2) The conjunctive combination rule: In order to consider
with information sources uncertainty and finally the inherent
theissuesoftheopenworld,theconjunctivecombinationrule
knowledge related to the information itself. In our case, we
was introduced by Smets [9]. Considering two mass functions
are more interested in the evaluation of the sources and the
m and m , for all X ∈2Ω m is defined by:
part of uncertainty related to them. The main issue in these 1 2 conj
communitiesisthatwearefacingusersthatwedonotalways
haveanaprioriknowledgeaboutthem.Weignoreeverything m (X)= (cid:88) m (Y )m (Y ) (3)
conj 1 1 2 2
aboutthesources’credibility,reliability,relevance,objectivity
Y1∩Y2=X
and expertise. In this context, we will exploit all the mathe-
3) The disjunctive combination rule: First introduced by
matical background and large panel of sepcificities provided
Dubois and Prade 1986 [18], the induced results of two bbas
by the theory of belief functions to help us considering this
m and m is defined as follows:
problem in an uncertain point of view. 1 2
(cid:88)
∀X ⊆Ωm (X)= m (Y )m (Y ) (4)
disj 1 1 2 2
Y1∪Y2=X
III. THEORYOFBELIEFFUNCTIONS
The disjunctive combination rule can be used when one of
the sources is reliable or when we have no knowledge about
This section recalls the necessary background related to the
their reliability.
belief function theory It has been developed by Dempster in
his work on upper and lower probabilities [1]. Based on that,
he was able to represent more precisely the observed data. IV. INCLUSIONASACONFLICTMEASUREFORBELIEF
FUNCTIONS
A belief function must take into consideration all the
possibleeventsonwhichasourcecandescribeabelief.Based Recently Martin in [3] used a degree of inclusion as
on that, we can define the frame of discernment. involved in the measurement made in order to determine the
conflict during the combination of two belief functions. He
presented an index of inclusion having binary values where:
A. Frame of discernment (cid:26) 1,ifX ⊆Y
Inc(X ,Y )= 1 2 (5)
1 2 0,otherwise
It is a finite set of disjoint elements noted Ω where Ω =
{ω ,...,ω }.Thistheoryallowsustoaffectamassonasetof With X , Y being respectively the focal elements of m
1 n 1 2 1
hypothesesnotonlyasingletonlikeintheprobabilistictheory. and m . This index is then used to measure the degree of
2
Thus, we are able to represent ignorance, imprecision... inclusion of the two mass functions and defined as:
B. Users’ conflict
1 (cid:88) (cid:88) Detectingirrelevantmessagesdoesnotonlymeansthatthis
d = Inc(X ,Y ) (6)
inc |F1||F2| 1 2 user is a troll. Thus, it is not only the content of the messages
X1∈F1Y2∈F2
that can characterize the trolls. We can find a victim user that
Where |F | and |F | are the number of focal elements of m
1 2 1 responds to a message posted by a troll. Besides, the subject
and m . He define the degree of inclusion of m and m :
2 1 2 of the discussion can change gradually. In fact, to distinguish
σ (m ,m ) as follows:
inc 1 2 between trolls and other users in a community, we need to
σ (m ,m )=max(d (m ,m ),d (m ,m )) (7) quantify how a given user is in conflict with the rest of all the
inc 1 2 inc 1 2 inc 2 1
otherusers.Thus,wewillbaseourapproachonmeasuringthe
Where d is the degree of inclusion of m in m and
inc 1 2
conflictbetweenthemessagesofeachpersonpostinganswers.
inversely. This inclusion is used as a conflict measure for two
The list of notations is shown in table I. Using the inclusion
mass functions, using it like presented:
Conf(m ,m )=(1−σ (m ,m )d(m ,m )) (8) TABLEI
1 2 inc 1 2 1 2
LISTOFNOTATIONS
where d(m ,m ), is the distance of Jousselme [10]:
1 2
Notations Description
(cid:114)
1 U Users
d(m ,m )= (m −m )TD(m −m ) (9)
1 2 2 1 2 1 2 N Numberofusers
NP Numberofallthepreviousmessages
where D is a metric based on the measure of Jaccard: NPj NumberofthepreviousmessagesofauserUj
(cid:40) Ni NumberofallmessagespostedbyauserUi
D(A,B)= |1A,∩iBf|A,∀=A,BB=∈∅2Ω (10) mNkj NktuhmmbeerssoafgaellofmaesussaegresUpiostedbyauserUj
|A∪B| ms sth messageofauserUj
Rank(m) Rankofthemessagem
V. TROLLSIDENTIFICATIONBASEDINACONFLICT
Tab1 Containsineachtimetheconflictofamessage
MEASURE relativetoeachuser
Based on the assumption that consists of the trolls’ integra- Tab2 Containsineachtimethenumberoftheprevious
messagesofamessage
tion in the successful discussion threads, we propose a new
Tab3 Containsthetotalconflictofeachuser
method for detecting malicious people in online communities Conft Containsthesumofconflictofeachuser
forums. This approach is defined within the framework of
belief functions. Indeed, it is based on a conflict measure as a conflict measure for belief functions, for each user Ui we
of this theory applied between the different messages of the will measure:
thread.Wecansummarizeourproposedmethodinthreemajor • Confmsg/U: measures the conflict between the kth mes-
steps that will be discussed in depth in the following. sage posted by U and the messages that were posted
i
before it by each other users U .
j
A. Users’ messages
Conf (m (U ),m(U ))=
Hardarker proposed primary characteristics of a troll [2] msg/U k i j
(Aggression, Deception, Disruption, Success). In 2014, Buck- 1 N(cid:88)Pj
els et al. [6] specified the behavioral characteristics of a troll. NP Conf(mk(Ui),ms(Uj)),(i(cid:54)=j) (12)
j
They described them as persons having sadism, psychopathy s=1
andmachiavilism.Tothem,trollingisa”deceptive,destructive • Confmsg:measurestheconflictbetweenthekthmessage
or disruptive manner in social media”. postedbyU andtheallmessagesthatwerepostedbefore
i
In the context of this work, to distinguish between the troll it by all the other users U based on a weighted mean.
andtheotherusers,wetriedtomanuallyextractedthecharac- This measure takes into account the number of messages
teristics of their responses from the answers and comments in posted by every user in order to determine the level of
differentforums.Basedonthesecharacteristics,thecontentof conflict especially between a troll and an expert.
themessagescanbe:Off-topic,senselessorcontroversy.Using
Conf (m (U ),m(U))=
thesecharacteristics,wehavedefinedtheframeofdiscernment msg k i
that can characterize a message in a forum: (cid:88)N NPjConf (m (U ),m(U )) (13)
Ω ={Off −topic,Senseless,1,...,N} (11) NP msg/U k i j
msg j=1
• Senseless: how much the response is empty of meaning? • Confuser: measures the global conflict of the user Ui
• Off-topic: How irrelative the answer can be? 1 (cid:88)Ni
• [1..N] : number of topics where, [1..N]\i with i being Confuser = N Confmsg(mk(Ui),m(U)) (14)
therelevanttopic,and[1..N]\iarethecontroversytopics i k=1
posted by a troll. The value of the total conflict of a user can be risen when
During this step, we assume that a method of analysis this user launches into an interminable debate with a troll. In
expresses a piece of evidence concerning the nature of each thiscase,thisvictimuserbecomesinhisturnatroll.Thus,the
message. This method aims to analyze the messages relative managers have to control the behavior of the users in many
to the posted question or topic. discussion threads.
C. Users’ clustering
The last step consists on the classification of the users
according to their conflict results into two groups. Therefore,
to make decision we base our approach on an unsupervised
classification method using the k means algorithm.
It was introduced by McQueen [19] and implemented in
its current forms by Forgy [20]. The Kmeans algorithm aims
to construct from the objects of the training set K partitions
(clusters) concentrated and isolated from each other. In our
case, we will devise the users into two partitions: K= 2. Since
the value of the troll ’s conflict is bigger than the conflict of
the other users:
- Trolls belong to the group having the biggest value of
center.
-Theotherusersbelongtothegrouphavingtheleastvalue
of center.
VI. EXPERIMENTATION
To illustrate the comportment of our proposed method, we
have tested it in different simulated data. In this section, we
will present two different examples.
A. Example 1
Aswepresentedourmethod,ithasthreemainsteps.Indeed,
we will present the results of each step:
1) Users’ messages: Our assumption consists on the in-
tegration of the trolls in the successful discussion threads.
From this point of view, we simulate the data of analyze of
messages as depicted in Figure 1. In fact, in this example
we will try to detect a troll in a group of 4 users. In
this scenario, the discussion thread contains 16 messages
posted by different users and among whom three messages Fig.1. SimulationResults
are published by a troll. In this example, our frame of
TABLEII
discernment is composed by 4 elements: Relevant=X1, off- TOTALCONFLICTOFEACHUSER
topic=X , senseless=X , controversy-topic=X . As shown in
2 3 4
Figure 2 each row presents: the owner of the message, the U1 U2 U3 U4
order of the message in the discussion thread and the mass Confuser 0.0610 0.0639 0.0489 0.2030
function of this message (as mention in section III. B each
bba must be equal to 1).
3) Users clustering: Applying the K-means algorithm to
In this example, the first message of the troll (U ) is con-
4 the different values of total conflict of all users we obtained
troversy: m(X ) = 0.9210. His second message is empty of
4 two clusters.
meaning: m(X )=0.9716. His third message is controversy:
3 - Trolls={U }
4
m(X )=0.8387.
4 - Other users={U ,U ,U }
1 2 3
2) Usersconflict: Basedonthemethodoftheinclusionand
Ourproposalprovidesusacorrectclassificationoftheusers.
applying our algorithm, we will present the total conflict of
This result shows the feasibility of our proposed method.
eachuserofourexampleinTableII:U hasthebiggestvalue
4
of conflict. The total conflicts of users U and U is small
1 2
B. Example 2
relative to the total conflict of U despite the fact that they
4
responded to the first message of the troll by posting each For this simulation we will assume that we are dealing
one a controversy message. this result can be explained by with 8 users, among them two trolls. The discussion thread
the answers provided by these two users who have published contains 31 messages. The result of the total conflict of each
relevant messages. U has a small value of conflict, he user expressed in equation 14 is illustrated in figure 2.
3
published three relevant messages where in his first message The first troll U published 2 controversy messages and the
4
m(X ) = 0.9732, in his second message m(X ) = 0.7782, second troll U published 3 messages: The two first ones are
1 1 8
and his third message m(X )=0.9632. off-topic, and the last one is controversy.
1
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messages of the different users of the thread. First of all,
this method aims to analyze the messages relative to the Imen Ouled Dlala Univeristy of Tunis, LARODEC laboratory, High
posted question or topic. But detecting irrelevant message is Institut of Management, Cite Bouchoucha, le Bardo, TUNISIA; E-mail:
[email protected]
not enough to judge if this user is a troll or not. Thus, not
only the content of the messages that can characterize the
Dorra Attiaoui Univeristy of Tunis, LARODEC laboratory, High Insti-
trolls but also their behaviors. Next, using the results of this
tut of Management, Cite Bouchoucha, le Bardo, TUNISIA; University of
analysis we measured the conflict between the different users. Rennes 1, IRISA, Rue E. Branly, 22300 Lannion, FRANCE; E-mail: at-
Finally, after calculating the conflict of each user we applied [email protected]
the kmeans method in order to distinguish trolls from the
other users. Indeed, we have classified the users according Arnaud Martin University of Rennes 1, IRISA, Rue E. Branly, 22300
Lannion,FRANCE;E-mail:[email protected]
to their conflict results into two clusters. This method was
testedindifferentsimulateddatatocheckitsfeasibility.Since
our proposed method for detecting malicious users dealt only Boutheina Ben Yaghlane University of Carthage, IHEC Carthage,
Carthage Presidence, TUNISIA, LARODEC laboratory; E-mail:
with one discussion thread, we aim to extend this approach to
[email protected]
detect trolls inside the community.