Collaborative Communities for Social Сomputing

Research Article

Why Rating is not Enough: A Study on Online Reputation Systems

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  • @INPROCEEDINGS{10.4108/icst.collaboratecom.2011.247145,
        author={Rehab Alnemr and Christoph Meinel},
        title={Why Rating is not Enough: A Study on Online Reputation Systems},
        proceedings={Collaborative Communities for Social Сomputing},
        publisher={IEEE},
        proceedings_a={CCSOCIALCOMP},
        year={2012},
        month={4},
        keywords={reputation rating online reviews collaboration},
        doi={10.4108/icst.collaboratecom.2011.247145}
    }
    
  • Rehab Alnemr
    Christoph Meinel
    Year: 2012
    Why Rating is not Enough: A Study on Online Reputation Systems
    CCSOCIALCOMP
    ICST
    DOI: 10.4108/icst.collaboratecom.2011.247145
Rehab Alnemr1,*, Christoph Meinel2
  • 1: Research Assistant at Hasso Plattner Institute
  • 2: Full Professor at Hasso Plattner Institute
*Contact email: rehab.alnemr@hpi.uni-potsdam.de

Abstract

For several years online reputation systems have been evolving in an attempt to mimic their real-world counterparts. Rating in online communities comes in many forms such as numbers, stars, scales, etc.. The underlying aggregation or computation methods varies according to the community's information sources and perception of reputation. However, the notion of reputation is a complex one that represented as a simple form of property-rating or a vector of ratings, strips it from its original concept and postulation. It does not also facilitate the derivation of meaningful conclusions from it. In this paper, we present studies we designed to examine how users perceive reputation as well as what reputation information being used and whether rating is enough to convey an entity's reputation. These studies are analyzed and discussed based on the results. Moreover, we show how our reputation ontology - that is designed to facilitate reputation knowledge transfer on the communication level- is used in a simple interface of multiple ratings. The model is based on semantic web technologies.