=Paper=
{{Paper
|id=Vol-485/paper-14
|storemode=property
|title=A Collaborative System Based on Reputation for Wide-Scale Public Participation
|pdfUrl=https://ceur-ws.org/Vol-485/paper1-S.pdf
|volume=Vol-485
|dblpUrl=https://dblp.org/rec/conf/um/FernandezH09
}}
==A Collaborative System Based on Reputation for Wide-Scale Public Participation==
https://ceur-ws.org/Vol-485/paper1-S.pdf
Workshop on Adaptation and Personalization for Web 2.0, UMAP'09, June 22-26, 2009
A Collaborative System Based on Reputation for
Wide-Scale Public Participation
Ana Fernández1 and Jens Hardings1
1
Departamento de Ciencia de la Computación, Pontificia Universidad Católica de Chile
Vicuña Mackenna 4860 (143), Macul, Santiago, Chile
{asfernan, jhp}@ing.puc.cl
Abstract. The aim of this paper is to use a reputation system to promote trust
among participants of an online social network. By the use of past behaviour
and ratings from other users, this paper presents a novel metric to compute the
reputation of peers. Also a prototype and deployment results are included.
Keywords: Reputation system, public participation, e-democracy.
1 Introduction
This article proposes the use of a reputation system to promote trust among
participants and the system construction over an online social network.
Although reputation systems are being used in several fields, it has not yet been
implemented in politics and citizen participation. We have not found other attempts
of reputation systems for the world of politics in order to promote participation and e-
democracy between peers.
Reference [1] defines e-democracy as the sum of acts realized by individuals or
groups in order to influence the way the political system operates. Due to the Internet,
citizens can easily access political content and such an increased access to political
information should extend governmental transparency and thus democracy.
In reference [2], a proposal over the Internet where players have to cope with
uncertainty from quality of products and trustworthiness of participants is presented.
The method to address this predicament is to use feedback ratings about past
behaviour to make recommendations about who to trust.
In reference [3] a proposal for the use of reputation systems in Communities of
Practice (CoPs) was presented in order to assist users in creating relationships for
honest and useful participation, based on trust, for the benefit of the entire
community. Indeed [3] presents a simple reputation calculation based only in the
median of past reputations.
In [4] we have used reputation systems in a Mobile Ad hoc Network (MANET)
which is a low complexity system. But in this paper we offer a generalisation of the
use of reputation systems to a more complex framework represented by the world of
politics with the aim to promote participation and e-democracy between peers.
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Workshop on Adaptation and Personalization for Web 2.0, UMAP'09, June 22-26, 2009
The paper is organized as follows: section 2 presents reputation systems concepts
and issues; section 3 presents the proposed reputation model with its respective
reputation metric; section 4 presents the deployed system; and finally, section 5
discusses our conclusions.
2 Reputation Systems
Many interactions in real world are based on rumours or on friends’ experiences.
As a result of this, future interactions can be influenced by past interactions. We call
this the reputation of a user. Keeping that in mind we can build a system that collects,
processes and distributes information about the quality of interactions. Referring to
[5], we call such system a “reputation system”. Reputation systems are well suited for
stimulating social control within online communities. The idea is to let parties rate
each other and use those ratings to derive a reputation score, which can assist other
parties in deciding whether or not to transact with that party in the future [6].
Reputation systems need models in order to calculate the reputation of its users,
that is, a way to obtain a qualification for each individual, using information stored in
the system. Many reputation models have been proposed for online environments
systems throughout the past years, but there is not an accepted common model yet.
3 Proposed Reputation Model
In our model we identified several factors that influence on the reputation of a user
in the system which will be described as follows.
Whenever a user participates in the system he should be rewarded. A good way to
measure the participation is by the relative contribution factor which will be the
amount of actions executed by a user over the amount of total actions. We will denote
C iP as the relative contribution factor for participation which has been divided in m
areas, where m represents the amount of participation dimensions measured by the
system, and its values will satisfy 0 CiP 1 for i 1,..., m . Each contribution
should have different importance in the system, for such reason we will identify i as
the importance weight of CiP which values will go between 0 and 1. We then define
the participating reputation RP of user a as:
RP a i CiP a
m
(1)
i 1
Certain users have the ability to generate participation in others and such ability
should be rewarded by the system. In a similar way to participating reputation, C iL
represents the contribution factor for leadership which will be sorted out in n
different areas, where n represents the amount of leadership dimensions and its
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Workshop on Adaptation and Personalization for Web 2.0, UMAP'09, June 22-26, 2009
values will satisfy 0 CiL 1 for i 1,..., n . We will define i as the weight of
CiL in the system which values will go between 0 and 1. The leadership reputation
RL of user a will then be defined as:
RL a i CiL a .
n
(2)
i 1
Users in the system can be qualified by others for a performed activity. Agent a
will be rated and given a qualification q Q where Q 1,0 which represent a
positive or negative qualification respectively. Qa represents the time-sorted list of
qualifications of user a assigned by other users where Qa 1 is the oldest rate and
Qa h is the most recent. Each user in the system will have an ordered list used to
store his last h qualifications. When a new qualification h 1 arrives, the oldest one
comes out of the list like a FIFO array.
Agents will behave more probably like they did in their most recent transactions.
Therefore we chose a metric called BlurredSquared [7] which computes a weighted
sum of all ratings. The older a rating is, the less it influences the current reputation. In
our particular case the reputation will only be calculated with the last h qualifications.
The peer reputation RQ of user a will then be defined as:
Qa j
RQ a
h
. (3)
j 1 h j 1 2
The chosen model is based in the one described in [8]. The essential distinction
between that metric and ours is that this novel metric considers qualifications from
other nodes assigning more importance to the most recent ones.
We will define F as a function that determines the level of recent activity of a
certain node. Let T a be the residential time of user a in the system and let k be a
discount factor between 0 and 1 that will be chosen in order to decrease the level of
participation when the time spent in the system is higher and increase it when it is
shorter. The level of recent activity for user a will be:
F a RP a R L a k T a . (4)
Our model computes the global reputation or trust of a user based on two factors:
past qualifications and level of recent activity. Trust for user a will be calculated as:
RQ a 1 F a 1
Trust a . (5)
RQ a 1
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Workshop on Adaptation and Personalization for Web 2.0, UMAP'09, June 22-26, 2009
4 Deployed System
The proposed system was implemented in the Alumni Center of the Faculty of
Engineering of Universidad Católica de Chile using the well-known social network
Facebook. Such implementation offers a participation platform for students as it
permits them to express their concerns and ideas and allows others to vote or
comment about them. The previously described model was applied in order to
determine the improvement of trust among peers.
Figure 1 shows the evolution of trust for several users. Initially all users begin with
the same trust value. Their behaviour in the system and the qualifications assigned by
others determines the progress of their trust. User 1 has an increasing participation
and leadership reputation as well as a good reputation among other peers; therefore
his level of trust increases significantly over time. User 2 presents a decreasing
participating reputation but an incremental leadership and a high reputation. Finally
User 3 has a poor participation in the system and is not well qualified by others for
that reason it presents a decreasing trust value as time passes by.
1,6
1,5
1,4
User 1
Trust
1,3 User 2
User 3
1,2
1,1
1
Time
Fig. 1. Evolution of trust for different users.
5 Conclusions
This paper reflects the behaviour of a user in the system and the quality of his
contribution in his trust value. As a direct consequence arises the fact that users with
high level of trust are comparatively better users and therefore will eventually come
with good ideas that could be used as part of future policies in the world of politics.
From simulation we can conclude that good behaviour in the past and the use of
ratings from other participants is a high-quality metric in a social network.
A trust-based system built over a well-known social network brings a great
opportunity to participate for all interested users as well as an opportunity to identify
high-quality users whom may become in the leaders for tomorrow.
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Workshop on Adaptation and Personalization for Web 2.0, UMAP'09, June 22-26, 2009
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