Credibility-based Model for News Spreading on Online Social Networks

Vincenza Carchiolo

1 a

, Alessandro Longheu

2 b

, Michele Malgeri

2 c

, Giuseppe Mangioni

2 d

and Marialaura Previti

DMI, University of Catania, Italy

DIEEI, University of Catania, Italy

Keywords:

Credibility, Trust, Social Network, Information Diffusion.

Abstract:

Trustworthiness in Online Social Networks has become essential to discriminate the goodness of both different

information as well as the users it originates. Here a model for news spreading in directed online social

networks (OSNs) that takes into account trustworthiness-related issues is introduced. In particular we add

a credibility network on top of the acquaintance network naturally present in OSNs to model the changing

of each node’s opinion about his/her neighbors every time a piece of news comes from them over the OSN.

We examine three different scenarios of news spreading over OSNs and propose a model suitable for each

scenario, evaluating its applicability using a real world weighted directed network.

1 INTRODUCTION

Thanks to the rapid growth of Online Social Networks

(OSNs) (Persia and D’Auria, 2017) and the enormous

amount of information available every day, users of

these platforms tend to acquire and disseminate news

over them. This process, called social contagion (Ho-

das and Lerman, 2014), can amplify the spread of in-

formation in a social network. The decision to propa-

gate or not news can depend on the news contents or

the trust in people that publish news.

The goal of the work presented in this paper is to

provide a model for information propagation in OSNs

that takes into account trust-related issues. In partic-

ular, we consider the directed acquaintance network

naturally presents in OSNs (e.g., Twitter) as the ba-

sis through which news spreads over the network and

we introduce an overlay weighted directed network

(the credibility network) that have edges in the oppo-

site direction respect to edges in the acquaintance net-

work, because for each piece of news spreads from an

individual the receivers form or modify their opinion

about the spreader’s credibility. The credibility values

are led by two metrics related to the topology of the

https://orcid.org/0000-0002-1671-840X

https://orcid.org/0000-0002-9898-8808

https://orcid.org/0000-0002-9279-3129

https://orcid.org/0000-0001-6910-0112

network: the trust and the reliability. After provid-

ing a quantitative deﬁnition for each metric, we apply

the model with such credibility network in three dif-

ferent simulation scenarios of information spreading

exploiting as starting point a real network. This paper

follows our previous works on the same topic (Car-

chiolo et al., 2018a)(Carchiolo et al., 2018b).

The paper is organized as follows: in section 2

an overview of existing related works is presented,

while in sec. 3 metrics and credibility network are

introduced and the different way in which a piece of

news can be propagated over the social networks are

detailed through a progressively improved model re-

adapted for each kind of propagation. In section 4 the

simulations are illustrated and discussed, and ﬁnally

in section 5 we outline our concluding remarks and

future works.

2 RELATED WORKS

Several attempts to model the news spreading over

OSNs through implicit and explicit use of trust has

been performed. The concept of trust as a way

to assess the quality of information, people, goods,

and/or virtual entities spans different research areas,

from recommendation systems (Massa and Avesani,

2007) (Carchiolo et al., 2015b) to e-commerce (Fung

and Lee, 1999), distributed on-line services (Wang

Carchiolo, V., Longheu, A., Malgeri, M., Mangioni, G. and Previti, M.

Credibility-based Model for News Spreading on Online Social Networks.

DOI: 10.5220/0009340500340042

In Proceedings of the 5th International Conference on Complexity, Future Information Systems and Risk (COMPLEXIS 2020), pages 34-42

ISBN: 978-989-758-427-5

and Emurian, 2005), security and privacy (Vasude-

van et al., 2012) and many others. In some cases a

trust network is created for each user and it contains

his friends as nodes and an associated trust value for

each of them as edges weights, provided that usually

trust is not for free, rather some effort must be paid

to earn trustworthiness from others (Carchiolo et al.,

2015a).

Goldbeck (Golbeck et al., 2003) proposed a

method for creating a trust network on the seman-

tic web allowing users to express the level of trust

for each person they know about a speciﬁc topic.

This weighted network was used to infer trust values

between individuals not directly connected to each

other. In another work (Golbeck, 2005) authors pro-

posed TidalTrust to derive the trust relationship based

on the premise that neighbors with higher trust ratings

are likely to agree with each other about the trust-

worthiness of a third party, so for a ﬁxed trust rat-

ing shorter paths have a lower average difference and

higher trust ratings have a lower average difference.

This work was extended by Zhang et al. (Zhang et al.,

2006) including pairwise trust ratings and reliability

factors of the entities in the network and using an

edge-weighted network for trust assessment. It cal-

culates the similarity of two raters by comparing their

ratings about the same provider and then it is adopted

to decide which neighbor recommendation should be

followed. Comparing two recommendations, the rec-

ommendation from a rater that is more similar to the

trustor will be chosen. Dubois et al. (DuBois et al.,

2011) presented a method to compute trust and dis-

trust combining an inference algorithm that relies on

a probabilistic interpretation of trust based on random

graphs with a modiﬁed spring-embedding algorithm

in order to classify hidden trust edges as positive or

negative.

Other works that exploit the OSN structure has

been proposed by Hang and Singh (Hang and Singh,

2010) that employed a graph-based approach based

on similarity between each node’s friends trust net-

work for measuring trust with the aim to recommend

a node in a social network using the trust network,

Kuter et al. (Kuter and Golbeck, 2007) that pro-

posed a Bayesian trust inference model for estimat-

ing the conﬁdence on the trust information obtained

from speciﬁc sources, Caverlee et al. (Caverlee et al.,

2008) that proposed a social trust model that exploits

both social relationships and feedback by users after

each interaction to evaluate trust where users’ feed-

back have a different weight based on their link qual-

ity (higher weights belong to users with a lot of links

with user having high trust ratings) and Zuo et al.

(Zuo et al., 2009) that proposed a model that uses a

trust certiﬁcate graph and calculates trust along a trust

chain, then, it exploits the composability of trust in the

form of fusion of relevant trust chains to form a base

trust chain set.

In contrast to most of the previous exposed works

that derive data from users feedback, Kim (Kim et al.,

2008) built a Web of trust without using explicit user

ratings. His approach consists on calculating users

expertise in a certain topic, which involves calcu-

lating the quality of reviews using the reputation of

raters and then the reputation of writers, calculating

the users afﬁnity to the category, where the user afﬁn-

ity to the ratings is derived from the average number

ratings and reviews provided by them in each cate-

gory, ﬁnally deriving degree of trust from the user’s

afﬁnity to the topic and another users expertise on the

topic.

Another set of OSNs trust models that exists in lit-

erature only use interactions among users within the

network to calculate trust. Liu et al. (Liu et al., 2008)

proposed an approach for predicting trust in online

communities using the interaction behaviors of OSNs

users. This model considered the temporal factor, i.e.,

the time difference between two connected users re-

spective actions and described a supervised learning

approach that automatically predicts trust between a

pair of users using the user factors, representing ev-

idence derived from actions of individual users, and

the interaction factors, representing the evidence de-

rived from interactions between pairs of users.

Nepal et al. (Nepal et al., 2010) proposed STrust,

a social trust model based only on interactions within

the social network. The model consists of two types

of trust: the popularity trust refers to the acceptance

and approval of a member in the community and rep-

resenting the trustworthiness of the member from the

perspective of other members in the community, and

the engagement trust refers to the involvement of the

member in the community and representing the trust

the member has towards the community. This model

aims to increase the social capital of the community

by encouraging positive interactions within the com-

munity and, so, increase the social trust in the com-

munity.

Adali et al. (Adali et al., 2010) evaluated trust

based on communication behaviors of OSNs users.

Behavioral trust is calculated based on two types of

trust: conversation trust, that speciﬁes how long and

how frequently two users communicate with each

other, and propagation trust that is obtained from one

user to other users in the network and indicates the de-

gree of trust placed on the information and implicitly

on the user that created the information.

In the last decade some hybrid trust models that

Credibility-based Model for News Spreading on Online Social Networks

use both interactions among OSNs users and OSNs

structure has been created, even if the literature on

these promising models is limited. Trifunovic et al.

(Trifunovic et al., 2010) proposed a social trust model

for opportunistic networks. This model uses two com-

plementary approaches for social trust establishment:

explicit social trust that is based on consciously es-

tablished social ties and produces a general decrease

of trust with the growth of the number of links be-

tween pairs of users, and the implicit social trust that

is based on frequency and duration of contact between

two users, but take into account not only the length

of interaction, but also the similarity in order to avoid

that a set of negative long interactions produces a high

trust between pairs of users.

Zinoviev et al. (Zinoviev et al., 2010) proposed

a game theoretical model of the information forward-

ing and feedback mechanisms in a social network that

take into account the personalities of the sender and

the receiver, including their perceived knowledge-

ability, reputation, and desire for popularity, and the

global characteristics of the network.

Wu et al. (Wu et al., 2017) investigated the dy-

namics of competitive information diffusion over a

connected social network, proposing a modiﬁed SIR

model for two competitive information, where each

individual may turn to either of the two information

after interacting with a spreader, while the spreader

associated with one information may change into the

other information. The population is divided into

three subgroups: innovators, ordinary and laggard

subgroups, and they observed that innovators and

larger network degree can help to increase the cover-

age of the information among the population but they

cannot help one information to compete with the other

one. Moreover, innovators cannot always accelerate

the convergence speed, which depends more on the

network topology.

3 THE CREDIBILITY-BASED

MODEL

As discussed previously, the simple acquaintance net-

work naturally presents in each OSNs is not sufﬁcient

to model the propagation of news because such net-

works do not take into account several factors that

come into play when someone decides to propagate

the news. To this purpose we introduce a duplex net-

work composed by a directed acquaintance network

A = hN, Ei and a directed weighted credibility net-

work C = hN, E

, ci with edges in opposite direction

respect to those in the acquaintance network because

if a node spreads a piece of news the receiving node

forms an opinion about the spreader modeled through

the weight c ∈ [−1, 1] (-1 indicates the highest credi-

bility whereas 1 indicates the lowest).

In directed OSNs the basic assumption is that be-

tween pairs of individuals there is not necessarily a

mutual interest in published posts, so incoming and

outgoing edges of acquaintance network hold differ-

ent roles: outgoing edges represent the link with peo-

ple interested in what we publish, while the incoming

ones are a source of inspiration for arguments of our

interest and that in some cases we want to repost. Ac-

cording to this reason we introduce two amounts in

the credibility network structure: trust and reliability.

The trust (T) in an individual indicates how much

he/she is considered trustworthy by its neighbors;

high trust values indicate that who is in contact with

him/her appreciates the contents he/she posted and

considers him/her a person who veriﬁes the news be-

fore reposting it (it is related to incoming edges of

credibility network);

The reliability (R) of an individual shows its abil-

ity to select which neighbors he/she will accept news

from to repost, hence this parameter indirectly inﬂu-

ences his/her ability to post true news, i.e. it is related

to outgoing edges of credibility network.

In OSNs, many users tend to link with others who

share the same news, while malicious users create

multiple accounts to repost the news. The members

of these two groups of accounts increase each other’s

trust so the network remainder that is in contact with

them can assign a low level of reliability to offset the

high level of trust; this is performed in order to re-size

their weight in the credibility network and therefore to

attenuate the echo chambers phenomenon (Baumann

et al., 2019).

3.1 Trust and Reliability Metrics

The trust and reliability parameters described above

are closely linked each other and inﬂuence the credi-

bility of an individual in his neighborhood. In partic-

ular, trust of v ∈ N is deﬁned as:

t+1

(v) =

|v|

∑

u∈U

(v)

(u)c(u, v) (1)

(v) is the set of neighbors pointing the node v

and R

(u) is the reliability at time t. We deﬁne relia-

bility of v ∈ N:

t+1

(v) = 1 −

|v|

out

∑

u∈U

out

(v)

t+1

(u) − c(v, u)|

(2)

out

(v) is the set of neighbors who are pointed

by node v and T

t+1

(u) is the trust calculated with the

COMPLEXIS 2020 - 5th International Conference on Complexity, Future Information Systems and Risk

eq. 1.

Since credibility c ∈ [−1,1], T ∈ [−1, 1] as well,

where -1 indicates full distrust and 1 indicates full

trust; each neighbor has actually a different weight

in determining trust depending on his reliability.

To establish the reliability of a node, the closer

this value to the values of other users that contributed

to generate credibility (i.e. the smaller the difference

t+1

(u)−c(v, u)|), the higher its reliability. The value

2 in the denominator is used as normalization factor to

balance the discrepancy between c(v, u) and T(u) that

can be at most 2. From equation 2 R ∈ [0, 1], where 0

indicates that he/she emphasizes news of little interest

to his neighborhood and conversely 1 indicates that

he/she agrees with his neighbors opinion.

In OSNs the state when observation starts can-

not be usually considered as neutral in which nobody

knows others neither opinions exist, so it is likely to

set initial values of c(u, v) ∀u, v ∈ N. To set in par-

ticular values for T and R we use the equations (1)

and (2) by setting R = 1 and iterating the two equa-

tions recursively until convergence occurs, i.e. un-

til |T

t+1

(v) − T

(v)|< ε and |R

t+1

(v) − R

(v)|< ε with

proper values for ε.

If a node does not have incoming or outgoing

edges, it is not possible to calculate the trust or re-

liability values respectively using previous equations

hence we suppose that nodes without incoming edges

are hypothetically pointed by all the other N −1 nodes

of the network and similarly nodes without outgo-

ing edges are supposed to point at each other node

in the network. For all these edges we set weight to

1, meaning that maximum credibility is assigned; the

approach resembles that used in deﬁning the telepor-

tation in PageRank algorithm (Gleich, 2014).

Equations 1 and 2 in this scenario become:

t+1

(v) =

N − 1

∑

u∈N

u6=v

(u)c(u, v) =

N − 1

∑

u∈N

u6=v

(u)

(3)

(4)

t+1

(v) = 1 −

N − 1

∑

u∈N

u6=v

t+1

(u) − c(v, u)|

= 1 −

2(N − 1)

∑

u∈N

u6=v

t+1

(u) − 1|

3.2 News Spreading Models

The process of news diffusion can occur in different

ways: sometimes the news propagates over the net-

work without opposition, while in other cases con-

ﬂicting opinions arise. To address this, we consider

three kinds of news spreading models:

• No-competitive news spreading model when

news can propagate over the network without op-

position

• Competitive news spreading model in which

there are two different thought factions about the

same topic that propagate the news at the same

time and a group of target OSN users reached by

both factions is called to decide which side they

are on

• Competitive news spreading model with delay

same as previous but one line of thought is dis-

seminated after the other and a group of targert

OSN users is called to decide if publish the re-

traction after the propagation of ﬁrst news

In the following we describe these models in a fur-

ther reﬁnement.

3.2.1 No-competitive Model

After the setting of the initial values on credibility

network, to consider OSN users previous activities

we suppose that a set of nodes S becomes spreader,

activating themselves to propagate a piece of news.

The ignorant neighbors I exposed to that piece of

news must decide whether to repost it or not therefore

the sum of the credibility of the edges linking with

spreader nodes must exceed the activation threshold

of the inactive nodes.

The activation threshold g

(v) of a node v must

take into account the contribution of incoming and

outgoing edges that in the previous interactions con-

tributed to help that node to create an opinion about

its neighborhood. This two contributions are embed-

ded in trust and reliability values calculated before the

node is requested to evaluate a new piece of news

posted by a spreader, hence the threshold value will

be:

(v) = R

t−1

(v)T

t−1

(v) (5)

Therefore, an inactive node v will become active

if:

out

(v)|

∑

u∈U

out

(v)

c(v, u) > g

(v) (6)

out

(v) = U

out

(v) ∩ S is the set of neighbors

spreading the news. If there are new spreaders at time

t, the procedure is repeated for their inactive neigh-

bors otherwise the propagation ends.

3.2.2 Competitive Model

As described above in this scenario two conﬂicting

piece of news are spread simultaneously, hence at a

given instant t

two groups of spreader S

and S

are activated. For each subsequent instant for both

Credibility-based Model for News Spreading on Online Social Networks

pieces of news the activation of the neighboring in-

active nodes is attempted according to the following

equations:

out

(v)|

∑

u∈U

out

(v)

c(v, u) > g

(v) (7)

out

(v)|

∑

u∈U

out

(v)

c(v, u) > g

(v) (8)

If a node is exposed to both ideas at the same time

and both sums of credibilities exceed the threshold

(v) a further comparison is necessary to evaluate

which group of nodes mostly inﬂuences the inactive

node:

out

(v)|

∑

u∈U

out

(v)

c(v, u) >

out

(v)|

∑

u∈U

out

(v)

c(v, u)

(9)

If the ﬁrst term is higher than the second the dis-

puted node is activated for the ﬁrst piece of news, for

the second one elsewhere. At the end of each step if

there are new spreaders the attempts of propagation

are carried out on the nodes directly reached by the

new spreader, otherwise they end.

3.2.3 Competitive Model with Delay

In this type of propagation a piece of news coming

from untrustworthy nodes is spread and after a certain

number of iterations the retraction comes by trustwor-

thy nodes; the viceversa can also occur, i.e. after the

spread of a true piece of news a group of malicious

spreaders propagate a piece of news with conﬂicting

content.

In this case the diffusion of the malicious piece of

news and the further diffusion of the retraction at the

end of the propagation must be measured, also allow-

ing nodes that posted the malicious news to publish

the retraction if the inﬂuence of benevolent nodes is

greater than the one of malicious nodes. Therefore,

the equations are the same of previous case but the

execution times are different.

4 EXPERIMENTS

4.1 Dataset

In this work we used the Wikipedia edit war net-

work topology to carry out our simulations (e.g.

(Sumi et al., 2011)). It is composed by 116,836 nodes

and 2,027,871 directed edges related to the changes

performed by users on pages previously modiﬁed by

others. Each edge weight falls into the range [-9,12]

that depends on the number of words modiﬁed in fa-

vor (if it is an attempt to expand the information) or

against another user (if it is an attempt to reverse pre-

vious changes).

We considered only the ﬁrst contact between each

pair of nodes, ﬁltering the edge list and normalizing

the values so that they fall within the range [-1,1] in

order to use them as c(u, v). This is important be-

cause it avoids that simulations begin from random

trust and reliability values that would not be repre-

sentative of the previous interaction history between

pairs of nodes.

4.2 Simulator Workﬂow

To evaluate the model we implemented a simulator

that carries out the following steps in order to emulate

the spreading processes on OSN:

• It reads the edgelist and generates the correspond-

ing weighted directed network

• exploiting weights, it calculates trust, reliability

and the threshold value (respectively with eqs. 1,

2 and 5) for each node

• It creates the trust ranking and selects the percent-

age of seeds, i.e. the initial spreaders of a piece

news (0.1%, 0.2%, 0.5%, 1%, 2% and 5% of to-

tal network nodes) from the top or the bottom of

ranking and actives them depending on which cat-

egory of nodes it wants to use as seeds of news

propagation, high trust seeds (HTS) or low trust

seeds (LTS))

• For each inactive node that has at least a spreader

as neighbors it checks the eqs. 6 or 7 and 8 (in

competitive cases also 9 if the threshold is over-

come by both factions) and activates the inactive

nodes whose threshold are exceeded

• At the end of each loop, checks the list of new

spreaders and if it is not empty restarts the loop;

• When the list of new spreaders does not contain

new nodes, it saves relevant information about

propagation.

4.3 Simulations

In the case of a no-competitive model we have two

cases of initial spreader nodes: trustworthy (HTS) and

not trustworthy (LTS) and simulator calculates how

many nodes decide to propagate the piece of news

from each group of initial spreader nodes.

In the case of a competitive model it calculates

how many nodes decide to propagate the piece of

COMPLEXIS 2020 - 5th International Conference on Complexity, Future Information Systems and Risk

news from two conﬂicting factions, high and low trust

nodes. In the case of disputed nodes, i.e. nodes that

come into contact with both groups of spreaders, it is

interesting to see how many people follow each of the

two sides.

In the case of a competitive model with delay in

addition to the number of nodes that decide to prop-

agate the piece of news of each faction, it calculates

the number of nodes changing their opinion after the

propagation of retraction.

Therefore, the following ﬁve variants of the afore-

mentioned evolution of the model will be examined:

• High trust seeds in no-competitive model

• Low trust seeds in no-competitive model

• High trust seeds vs low trust seeds in competitive

model

• High trust seeds vs low trust seeds in delayed

competitive model

• Low trust seeds vs high trust seeds in delayed

competitive model

4.4 Results

The distribution of c, T , R and g calculated in the sec-

ond step of simulator workﬂow on the Wikipedia edit

war network are shown in ﬁgs. 1, 2, 3 and 4 respec-

tively.

Figure 1: Distributions of credibility initial values for

Wikipedia edit war dataset.

Most interactions in this network are to reverse

text changes hence most c have a small negative value.

Accordingly to assign negative credibility values to

their neighbors, the reliability values are very high in

the initial phase while the g threshold histogram have

the same form of the trust histogram, it just appears

more contracted along the X axis due to the reliability

resizing. The simulator outputs are shown in tables 1,

2 and 3.

In no-competitive model (table 1) we note that us-

ing the same number of seeds HTS can always propa-

gate a piece of news more effectively than LTS. When

Figure 2: Distributions of trust initial values for Wikipedia

edit war dataset.

Figure 3: Distributions of reliability initial values for

Wikipedia edit war dataset.

Figure 4: Distributions of g threshold initial values for

Wikipedia edit war dataset.

the seeds are few (0.1%) compared to the total size

of the network, LTS cannot convince anyone to re-

post the piece of news while in the last case (5%) the

propagation of the LTS is equal to 1/4 of HTS one,

probably thanks to the large number of promoters.

In competitive model (table 2) it can be observed

that for the same number of seeds the propagations of

both factions are smaller than in no-competitive case.

Particularly interesting is the cases of the propaga-

tion of LTS with few seeds (0.1%, 0.2% and 0.5%)

in which only nodes with few or no contacts with the

opposite faction are convinced to propagate the piece

of news while almost all nodes reached by HTS has

Credibility-based Model for News Spreading on Online Social Networks

Table 1: No-Competitive model. Number of nodes that decide to propagate the piece of news deriving from LTS and HTS

respectively.

% of seeds LTS HTS

0.1 116 13681

0.2 234 14904

0.5 591 16551

1 7657 17763

2 4696 20655

5 6253 24830

Table 2: Competitive model. Number of nodes that decide to propagate a piece of news and, in this set, number nodes in

touch with opposite faction for news deriving from LTS and HTS respectively.

% of seeds LTS LTS (HTS) HTS HTS (LTS)

0.1 116 0 13662 10905

0.2 234 1 14863 11989

0.5 590 6 16443 13218

1 1215 47 17512 15042

2 2564 128 20019 15042

5 6153 312 23245 14763

Table 3: Competitive model with delay. Number of nodes that decide to propagate the piece of news at the end of each

propagation and number of reversed nodes that change their mind after the second propagation for news deriving from LTS

and HTS respectively and vice versa.

% of seeds LTS HTS Reversed HTS LTS Reversed

0.1 116 13681 19 13681 116 0

0.2 234 14903 40 14904 234 1

0.5 591 16551 110 16551 588 0

1 7657 17681 5421 17763 5738 3721

2 4696 20582 2413 20655 3458 852

5 6253 24778 1720 24830 6117 83

contacts with the opposite faction and decided any-

way to propagate the HTS piece of news. This means

that when there are many seeds with low trust (i.e. the

piece of news is perceived as false) it is not the content

of the news that plays a fundamental role in its propa-

gation rather the inﬂuence of the numerous neighbors

that propagate it.

In the competitive model with delay (table 3) the

HTS propagate the news more effectively than the

LTS as in the previous case but when the denial comes

from the HTS for all the percentage of seeds there

are some nodes that change ideafor a small number

of seeds (0.1%, 0.2% and 0.5%) there are no publi-

cations of the retraction. In each case the number of

retractions is higher for HTS respect to LTS.

5 CONCLUSIONS AND FUTURE

WORK

In this work, we proposed a model that exploits trust

and reliability with 3 variations in order to describe

the different way a piece of news can be propagated

through OSNs: in the no-competitive model each

piece of news is free to propagate over the network

without opposition, in competitive model two differ-

ent factions propagate two pieces of news with op-

posite contents at the same time in order to convince

the undecided social network users to align with their

own thought group and ﬁnally in competitive model

with delay the second piece of news propagation hap-

pens after the propagation of the ﬁrst one in order to

convince other user to change their minds and publish

a retraction.

We implemented a simulator and used a real

weighted directed network as starting point for the

simulation. This simulator exploits different number

COMPLEXIS 2020 - 5th International Conference on Complexity, Future Information Systems and Risk

of seeds belonging to two different groups of users,

high trust seeds and low trust seeds. The purpose is

twofold, we evaluated the inﬂuence in news propaga-

tion of large groups of seeds respect to smaller ones

and the importance of a high trust respect to a low

one.

We discovered that as in real OSNs occurs, if a

piece of news is propagated by a small group of user

with low trust it does not propagate, indeed in such

cases only the initial seeds remain involved in news

spreading while if the group of seeds has a high num-

ber of members it propagates news over the network

but with a minor impact respect to the case of the

same number of seeds with high trust. This means

that trust is an important metric in this scenarios but

also the inﬂuence of neighborhood plays a key role in

the decision to further spread or not.

Currently we are working to an improved model

where dynamics is introduced by inserting a credibil-

ity update mechanism to check whether and to what

extent the changing of user behaviors (e.g. a HTS

suddenly start to spread fake news or viceversa) af-

fects the credibility and the related spread.

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