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  • Broschiertes Buch

Online communities have something in common: their success rise and fall with the participation rate of active users. This book focuses on social rewarding mechanisms that generate benefits for users in order to achieve a higher contribution rate in a wiki system (e.g., like Wikipedia). In an online community, social rewarding is in the majority of cases based on accentuation of the most active members. As money cannot be used as a motivating factor others, such as status, power, acceptance, and glory have to be employed. Different social rewarding mechanisms are explained which aim to meet…mehr

Produktbeschreibung
Online communities have something in common: their
success rise and fall with the participation rate of
active users. This book focuses on social rewarding
mechanisms that generate benefits for users in order
to achieve a higher contribution rate in a wiki
system (e.g., like Wikipedia). In an online
community, social rewarding is in the majority of
cases based on accentuation of the most active
members. As money cannot be used as a motivating
factor others, such as status, power, acceptance, and
glory have to be employed. Different social rewarding
mechanisms are explained which aim to meet these
needs of users. Furthermore, four methods were
implemented within the MediaWiki system, where social
rewarding criteria are satisfied by generating a
ranking of the most active members. These techniques
refer to used references in an article, user votes,
article visits, and user recommendations. In
addition, this book also focuses on the calculation
algorithm the ranking of authors is based on, the
implementation process of the development, and the
use of adequate information visualization techniques
to present results.
Autorenporträt
Bernhard Hoisl received a BSc and a MSc in Information Systems
from the Vienna University of Economics and Business. Since the
end of 2008 he is a PhD student at the Institute for Information
Systems and New Media involved in an EU funded project
researching in the area of Natural Language Processing and
Technology Enhanced Learning.