CHARACTERISTICS OF TRUST IN ONLINE SOCIAL

NETWORKS AND COMMUNITY OF TRUST AS A SPECIAL

CASE OF ONLINE COMMUNITY

David Zejda

Faculty of Informatics and Management, University of Hradec Králové, Hradec Králové, Czech Republic

Keywords: Trust, Distrust, Community of trust, Social networks, Community.

Abstract: With boost of interest in Web 2.0 technologies, appropriate trust models are increasingly more important.

First section the paper contains state of the art about trust characteristics, in particular multidimensionality,

contextuality, scope of relevance, transitivity and asymmetry. Transitivity as a key aspect utilized in most

models is described in a slightly greater detail. Discussion on scope of relevance allowed us to introduce

taxonomy of trust from the scope point of view. Based on the general foundation, in the second section we

introduce community of trust as a niche type of online community where users trust each other as default

and where the trust loses most of its subjective flavour.

1 INTRODUCTION

Both individual social interactions and a whole

dynamics of personal social network are highly

influenced by trust. Trust may be defined as “the

willingness of a party to be vulnerable to the actions

of another party based on the expectation that the

other will perform a particular action important to

the trustor, irrespective of the ability to monitor or

control that other party.” (Mayer et al., 1995) For

our work we adopted rather the definition: “Trust in

a person is a commitment to an action based on a

belief that the future actions of that person will lead

to a good outcome.” (Golbeck & Hendler, 2006) The

level of trust which we feel toward someone helps us

to decide whether to rely on his promises or whether

to entrust him an information or a task.

Trust emerges primarily from our experiences

with others, their acts, words, their willingness to

help us in difficulties, promises which have been

kept. Another source of trust is recommendation or

guarantee from those, who we trust already. In

general, trust grows slowly, but falls sharply.

(Walter et al., 2008) It may take months or years

before we credit someone, whereas a single act of

betrayal destroys the trust from the roots.

We all belong to a global-world village. As

expressed in the small world phenomenon, everyone

is connected with anyone else through only several

steps of relations. (Pavlovic, 2009) Current

technology emphasizes the connectedness. Besides

milieu for implicit socialization (Wennerberg &

Oellinger, 2006), web provides variety of explicitly

social spaces, including dating sites, community

portals and social networking sites. If we add pace

of life nowadays, new social strategies are needed to

cope with the social and information overload.

(Walter et al., 2008) Reliable, efficient, and

appropriate trust solutions for social software should

reflect the needs. In the paper we present state of the

art about trust characteristics and define community

of trust as a niche kind of community where trust

among users is a default state.

2 TRUST CHARACTERISTICS

Online interactions may be viewed as a technical

extension of interactions in real world. (Dwyer et al.,

2007) So, trust in online networking systems keeps

most of its general characteristics. Meo et al. (Meo

et al., 2009) define three aspects of trust,

multidimensionality, contextuality and scope of

relevance. Goldbeck et al. identify transitivity,

asymmetry and personalization (Golbeck & Hendler,

2006). Personalization may be viewed as a special

case of scope of relevance. We decided to add

disproportion of impacts and dynamics.

531

Zejda D..

CHARACTERISTICS OF TRUST IN ONLINE SOCIAL NETWORKS AND COMMUNITY OF TRUST AS A SPECIAL CASE OF ONLINE COMMUNITY.

DOI: 10.5220/0003332905310534

In Proceedings of the 7th International Conference on Web Information Systems and Technologies (WEBIST-2011), pages 531-534

ISBN: 978-989-8425-51-5

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

Eigenvector-type algorithm is PageRank by Google.

Multidimensionality. There is no single source of

trust, on the contrary various factors may be

considered to evaluate trust, such as honesty,

experience, precision, efficiency, or cooperativeness

of the party. We may mix the indices to get more

complex view. Dimensions grow with breadth of

a social network. In a virtual space on one hand we

miss non-verbal indices. We do not see others in

real, sometimes even not at all. It is also likely that

there are not many trustful people around who could

share their real world experiences. On the other hand

we may take the whole community into account and

use plenty of algorithms to overcome the drawback.

Contextuality. Social context and purpose of

trust evaluation affect our requirements on trust and

the process of trust formation - trust is contextually-

dependant. E.g. when we search for advices on

particular topic, we prefer experts on the domain.

Asymmetry. Trust of one to another does not

imply trust in reverse direction. Graph of trust is

directed, matrix of trust is not necessarily

symmetrical.

Transitivity. Admitting transitivity of trust, we

may follow trust relations to infer trust between

those who do not trust each other yet or who even do

not know each other. Multiplication along the path

performed by most algorithms effectively discounts

the resulting value (Huang & Fox 2006), thus those

whom the user trusts already are being taken more

seriously as a source of recommendations whom else

to trust. The algorithms differ in their focus. E.g.

some of them do not reduce cycles in a graph before

computation (Walter et al. 2009) or may be applied

in an environment with no central authority, e.g. to

find cooperative routes among selfish agents acting

as players in prisoner's dilemma. (Hales & Arteconi,

2005) Work on trust inference comprises e.g.:

(Ziegler & Lausen, 2004) (Kamvar et al., 2003)

(Guha et al., 2004) (Richardson et al. 2003).

Scope of relevance. It is necessary to distinguish

subjective trust to objective trust. Many models treat

trust as inherently subjective. (Golbeck & Hendler,

2006) Meo et al. classify subjective trust,

community-wide reputation, and general reliability.

(Meo et al., 2009) We further split reliability into

system-wide trustfulness and world-wide trust

identity exceeding borders of systems, as described

in Figure 1. Trustworthy user is usually being trusted

subjectively more quickly, reversely trustworthiness

may be inferred from a set of subjective trust

expressions. The inference my be performed with an

eigenvector

algorithm, weighing subjective trust

according to trustor's own trust. (Yan & Holtmanns,

2007). In result, trustworthiness of certain user stems

from trustworthiness of his neighbours in the graph

of trust (Walter et al., 2009). Explicit negative

experiences (signs of subjective distrust) may help to

reveal objectively malicious users. We lack

applicable solutions for world-wide trust identity.

Figure 1: Taxonomy of trust.

Disproportion of impacts. We may identify two

complementing types of errors in the process of trust

emergence. The first is 'excessive prudence' when an

user is excessively suspicious. The error inhibits

formation of vital trust and lead to certain losses. On

the contrary 'undue confidence' occurs if an user is

either intentionally careless or if he is prone to fraud

attempts. The second error may lead to more severe

impacts, which should be reflected in trust models.

Dynamics. Caverlee at al. recommend to fold

two main sources of information in a well-designed

trust metric – network topology and record of

behaviour. (Caverlee et al., 2008) Moghaddam et al.

provide model for rapidly evolving networks, with

puts emphasis on feedback as a source of trust.

(Moghaddam et al., 2009) Driven by the dynamics,

trust undergoes transitions between various states, it

may be gained, lowered, or even lost. Conceptual

representations of failures of trust, such as distrust,

mistrust, untrust and ignorance are available. Trust

may be recovered again, when regret followed by

forgiveness takes place. (Golbeck, 2008)

The characteristics mentioned are mutually

interrelated. E.g. contextuality brings further

dynamics to the model, severity of impacts is further

influenced by the context and scope of relevance,

etc. Yan et al. reflect most of the characteristics in

their conditional definition of trust: “Trustor A trusts

trustee B for purpose P under condition C based on

root trust R”. (Yan & Cofta, 2004) Trustor should be

informed about any distrustful behaviour of the

trustee according to the conditions and trust itself is

considered as dependant on the conditions.

3 COMMUNITY OF TRUST

What's the source of trust in social networking

systems? Trusted friendships may arise out of vital

interactions within a site, usually during a sufficient

WEBIST 2011 - 7th International Conference on Web Information Systems and Technologies

532

period of time and based on a sufficient level of

harmless activity. The model is meaningful for most

cases, however, perception of virtue of trust is not

unique among all communities. So, various models

of trust are needed to reflect the needs.

Besides the trust which evolves with online

interactions, also trust existing in a real social

background may be mapped into an online system

(Walter et al., 2008). For example, if you personally

invite someone to join a networking site, you

probably know him already and trust him, at least at

certain degree. The trust has been established in

advance already, based on your real world personal

experiences. You do not ask the system to show you

trustworthiness of the user. Rather reversely, you

may provide trust indices to the system. If we follow

the idea further, 'community of trust' is the scenario

where users of certain online social system trust

each other as default. Distrustful behaviour is rare

there and if occurs, it leads to immediate expulsion

from the community. Community of trust may exist

among relatives, among close friends who know

each other for a long time, among volunteers

working jointly on an issue, among members of a

church with strong influence on adherent's life or

within another group of people bonded with strong

shared principles. Table 1 outlines characteristics of

community of trust, discussed in more detail below.

Table 1: Community of trust vs. a common community.

common community community of trust

model of trust model of distrust

distrustful behaviour

relatively common

distrustful behaviour rare,

propagate distrust quickly

users are notably cautious users are careless

pre-validation of users

not necessary / possible

users have to prove their

membership first

users may express trust or

both trust and distrust

users may express distrust or

confirm trust

trust is important trust is pivotal

trust is to be gained trust is default state

trust is subjective trust is objective

trust is dynamic trust is not too dynamic

trust is transitive distrust is totally transitive

While in online social networking systems

supporting a common community we talk about

a model of trust, in community of trust more

appropriate name is model of distrust, because it

fulfils different purposes. Primarily it helps to reveal

intruders, impostors or those who turned bad.

Besides the main purpose, the model of distrust

indirectly fosters fair interactions within the

community, bringing deeper feeling of reliance and

connectedness. Healthful fear of possible

consequences motivates users to adhere to the

principles which keep the community together and

to avoid any bad behaviour.

According to (Golbeck & Hendler, 2006) trust is

a personal opinion, which means that each node has

different levels of trust for each other node (Meo et

al., 2009), but they admit, that systems based on

objective trust may exist. Community of trust is the

case. It is so tightly coupled that trust loses most of

its subjective flavour and turns objective. As long as

someone belongs to the community, others trust him.

If he behaves badly to one, nobody will trust him

more. Transitivity of distrust in a pure community of

trust is total. So, while in subjective models of trust

it gives sense to infer trust and distrust from a graph

of trust relations following paths of transitivity, in

community of trust it gives sense no more, because

trust is default and transitivity of distrust tends to

infinity. In most models, e.g. (Caverlee et al., 2008),

trust is dynamic, reflecting changes in both network

topology and activities of users. Model for

community of trust is not too dynamic, but distrust

has to be propagated as quickly as possible to the

whole community.

Generally, people are willing to make only the

effort, which brings obvious reward to them. Talking

about trust or distrust models, users should be

allowed to express their (dis)trust in situations and in

a way which reflects their pattern of thinking or their

habitual approach. The approach differs per context

or per community. In community of trust users do

not like to be annoyed with requests to evaluate trust

with every transaction or to express trust of each

other because trust is natural, implicit there. They

only wish to have something at hand to defend

themselves and the whole community if matters go

wrong. Eventually they would also like to confirm

the trust within the community to contribute to its

virtue.

Any model of trust itself should be trusted by

users, which implies that it should be also

understandable. Because trust is so vital within

a community of trust, it further underlines the

requirement to bring appropriate model, and to keep

it understandable. Users have to be authenticated

first before entering a community of trust. Details of

the validation process depend on a particular

community and are out of scope of this paper.

4 CONCLUSIONS

In the paper we outlined state of the art trust

CHARACTERISTICS OF TRUST IN ONLINE SOCIAL NETWORKS AND COMMUNITY OF TRUST AS A SPECIAL

CASE OF ONLINE COMMUNITY

533

solutions for online social networks. Besides

multidimensionality, contextuality, asymmetry,

transitivity, scope of relevance, two more

characteristics of trust have been identified -

disproportion of impacts and trust dynamics.

Subsequently we described basic ideas of trust

processing and inference in models with transitive

trust. Idea of scope of relevance has been extended

into simple taxonomy of trust. As a main

contribution, we introduced 'community of trust' to

describe niche tightly coupled communities where

trust among users is default state. Trust has objective

character there, so tracking paths of trust among

users has no sense. Model of distrust for community

of trust has to propagate every distrust quickly to the

whole community.

ACKNOWLEDGEMENTS

The research was supported by grant UHK FIM

specific research 2110/2010.

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