Analyzing user demographics and user behavior for trust assessment

TY - JOUR

T1 - Analyzing user demographics and user behavior for trust assessment

AU - Ceolin, D.

AU - Groth, P.T.

AU - Nottamkandath, A.

AU - Fokkink, W.J.

AU - van Hage, W.R.

N1 - Proceedings title: Uncertainty Reasoning for the Semantic Web III - Revised Selected Papers of URSW 2011-2013 Publisher: Springer

PY - 2014

Y1 - 2014

N2 - In many systems, the determination of trust is reduced to reputation estimation. However, reputation is just one way of determining trust. The estimation of trust can be tackled from a variety of other perspectives. In this chapter, we model trust relying on user reputation, user demographics and from provenance. We then explore the effects of combining trust computed through these different methods. Concretely, the first contribution of this chapter is a study of the correlations of demographics with trust. This study helps us to understand which categories of users are better candidates for annotation tasks in the cultural heritage domain. Secondly, we detail a procedure for computing reputation-based trust assessments. The user reputation is modeled in subjective logic based on the user's performance in the system evaluated (Waisda? in the case of the work presented here). The third contribution is a procedure for computing trust values based on provenance information, represented using the W3C PROV model. We show how merging the results of these procedures can be beneficial for the reliability of the estimated trust value. We evaluate the proposed procedures and their merger by estimating and verifying the trustworthiness of the tags created within the Waisda? video tagging game from the Netherlands Institute for Sound and Vision. Through a quantitative analysis of the results, we demonstrate that using provenance and demographic information is beneficial for the accuracy of trust assessments.

AB - In many systems, the determination of trust is reduced to reputation estimation. However, reputation is just one way of determining trust. The estimation of trust can be tackled from a variety of other perspectives. In this chapter, we model trust relying on user reputation, user demographics and from provenance. We then explore the effects of combining trust computed through these different methods. Concretely, the first contribution of this chapter is a study of the correlations of demographics with trust. This study helps us to understand which categories of users are better candidates for annotation tasks in the cultural heritage domain. Secondly, we detail a procedure for computing reputation-based trust assessments. The user reputation is modeled in subjective logic based on the user's performance in the system evaluated (Waisda? in the case of the work presented here). The third contribution is a procedure for computing trust values based on provenance information, represented using the W3C PROV model. We show how merging the results of these procedures can be beneficial for the reliability of the estimated trust value. We evaluate the proposed procedures and their merger by estimating and verifying the trustworthiness of the tags created within the Waisda? video tagging game from the Netherlands Institute for Sound and Vision. Through a quantitative analysis of the results, we demonstrate that using provenance and demographic information is beneficial for the accuracy of trust assessments.

U2 - 10.1007/978-3-319-13413-0_12

DO - 10.1007/978-3-319-13413-0_12

M3 - Article

VL - 8816

SP - 219

EP - 241

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

SN - 0302-9743

ER -