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- 20 creator adam-wierzbicki.
- 20 creator karl-aberer.
- 20 creator katarzyna-abramczuk.
- 20 creator paulina-adamska.
- 20 creator thanasis-papaioannou.
- 20 type InProceedings.
- 20 label "Game-theoretic Models of Web Credibility".
- 20 sameAs 20.
- 20 abstract "Research on Web credibility assessment can significantly benefit from new models that are better suited for evaluation and study of adversary strategies. Currently employed models lack several important aspects, such as the explicit modeling of Web content properties (e.g. presentation quality), the user economic incentives and assessment capabilities. In this paper, we introduce a new, game-theoretic model of credibility, referred to as the Credibility Game. We perform equilibrium and stability analysis of a simple variant of the game and then study it as a signaling game against naive and expert information consumers. By a generic economic model of the player payoffs, we study, via simulation experiments, more complex variants of the Credibility Game and demonstrate the effect of consumer expertise and of the signal for credibility evaluation on the evolutionary stable strategies of the information producers and consumers.".
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- 20 keyword "equilibrium".
- 20 keyword "evolution".
- 20 keyword "incentives".
- 20 keyword "rationality".
- 20 keyword "signaling game".
- 20 title "Game-theoretic Models of Web Credibility".
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- 7 creator andras-a-benczur.
- 7 creator balint-daroczy.
- 7 creator david-siklosi.
- 7 type InProceedings.
- 7 label "Content-Based Trust and Bias Classification via Biclustering".
- 7 sameAs 7.
- 7 abstract "In this paper we give probably the first practically useful result for trust, bias and factuality classification over Web data on the domain level. Unlike the majority of literature in this area that aims at extracting opinion and handling short text on the micro level, we aim to aid a researcher or an archivist in obtaining a large collection that, on the high level, originates from unbiased and trustworthy sources. Our method generates features as Jensen-Shannon distances from centers in a host-term biclustering. On top of the distance features, we apply kernel fusion methods and also combine with baseline text classifiers. We test our method on the ECML/PKDD Discovery Challenge data set DC2010. Our method improves over the best achieved text classification AUC results by over 0.02 for both neutrality and trustworhiness. The fact that the ECML/PKDD Discovery Challenge 2010 participants reached an AUC around 0.5 indicates the hardness of the task.".
- 7 hasAuthorList authorList.
- 7 isPartOf proceedings.
- 7 keyword "Bias".
- 7 keyword "Document Classification".
- 7 keyword "Machine Learning".
- 7 keyword "Trust".
- 7 keyword "Web Quality".
- 7 title "Content-Based Trust and Bias Classification via Biclustering".
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- 10 creator david-camacho.
- 10 creator david-f-barrero.
- 10 creator gema-bello-orgaz.
- 10 creator maria-d-r-moreno.
- 10 type InProceedings.
- 10 label "Clustering avatars behaviours from Virtual Worlds interactions".
- 10 sameAs 10.
- 10 abstract "Virtual Worlds (VWs) platforms and applications provide a practical implementation of the Metaverse concept. These applications, as highly inmersive and interactive 3D environments, have become very popular in social networks and games domains. The existence of a set of open platforms like OpenSim or OpenCobalt have played a major role in the popularization of this technology and they open new exciting research areas. One of these areas is behaviour analysis. In virtual worlds, the user (or avatar) can move and interact within an artificial world with a high degree of freedom. The movements and iterations of the avatar can be monitorized, and hence this information can be analysed to obtain interesting behavioural patterns. Usually, only the information related to the avatars conversations (textual chat logs) are directly available for processing. However, these open platforms allows to capture other kind of information like their exact position of an avatar in the VW, what they are looking at, or which actions they perform inside these worlds. This paper studies how this information, can be extracted, processed and later used by clustering methods to detect behaviour or group formations in the world. To detect the behavioural patterns of the avatars considered, clustering techniques have been used. These techniques, using the correct data preprocessing and modelling, can be used to automatically detect hidden patterns from data.".
- 10 hasAuthorList authorList.
- 10 isPartOf proceedings.
- 10 keyword "Behavioral patterns".
- 10 keyword "Clustering Techniques".
- 10 keyword "Data processing".
- 10 keyword "Virtual Worlds".
- 10 title "Clustering avatars behaviours from Virtual Worlds interactions".
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