Enriching Trust Prediction Model in Social Network with User Rating Similarity

Borzymek, Piotr; Sydow, Marcin; Wierzbicki, Adam

doi:10.1109/cason.2009.30

Cited by 37 publications

(32 citation statements)

References 9 publications

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“…Likewise a large body of research which has been devoted to the study of social media [35,17,1,24,2], increasing attention has been paid to the trust prediction problem [3,5,18,26,32]. The recent availability of trust and distrust networks has motivated increasing research on trust/distrust prediction [6,10,20,28].…”

Section: Related Workmentioning

confidence: 99%

Exploiting Emotional Information for Trust/Distrust Prediction

Beigi¹,

Tang²,

Wang³

et al. 2016

Proceedings of the 2016 SIAM International Conference on Data Mining

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Trust and distrust networks are usually extremely sparse and the vast majority of the existing algorithms for trust/distrust prediction suffer from the data sparsity problem. In this paper, following the research from psychology and sociology, we envision that users' emotions such as happiness and anger are strong indicators of trust/distrust relations. Meanwhile the popularity of social media encourages the increasing number of users to freely express their emotions; hence emotional information is pervasively available and usually denser than the trust and distrust relations. Therefore incorporating emotional information could have the potentials to alleviate the data sparsity in the problem of trust/distrust prediction. In this study, we investigate how to exploit emotional information for trust/distrust prediction. In particular, we provide a principled way to capture emotional information mathematically and propose a novel trust/distrust prediction framework ETD. Experimental results on the real-world social media dataset demonstrate the effectiveness of the proposed framework and the importance of emotional information in trust/distrust prediction.

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Section: Related Workmentioning

confidence: 99%

Exploiting Emotional Information for Trust/Distrust Prediction

Beigi¹,

Tang²,

Wang³

et al. 2016

Proceedings of the 2016 SIAM International Conference on Data Mining

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show abstract

“…Stemming from Internet auction systems [5], a reputation system is a standard countermeasure. Several interesting variations of reputation systems performing such tasks are discussed in [1,7], and [14]. …”

Section: Studymentioning

confidence: 99%

What Affects Web Credibility Perception? An Analysis of Textual Justifications

Kąkol

Nielek

2015

csci

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“…We will refer to this test as identity aggregator test, since the sample of users was obtained by parsing Profilactic 5 , an identity aggregator application which enabled us to know which profiles were owned by the same person.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…Examples are the suggestion of people with similar interests, the recommendation of resources [2] and the support to content annotation by tag suggestion [3]. Personal data are also used for security-related services, such as automatic filtering of unintended friends [4] and trust predictions in social networks [5]. However, technologies for automating the acquisition of user data can be a two-edged sword.…”

Section: Introductionmentioning

confidence: 99%

Escaping the Big Brother: An empirical study on factors influencing identification and information leakage on the Web

Carmagnola

Osborne

Torre

2013

Journal of Information Science

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This paper presents a study on factors that may increase the risks of personal information leakage, due to the possibility of connecting user profiles that are not explicitly linked together. First, we introduce a technique for user identification based on cross-site checking and linking of user attributes. Then, we describe the experimental evaluation of the identification technique both on a real setting and on an online sample, showing its accuracy to discover unknown personal data. Finally, we combine the results on the accuracy of identification with the results of a questionnaire completed by the same subjects who performed the test on the real setting. The aim of the study was to discover possible factors that make users vulnerable to this kind of techniques. We found out that the number of social networks used, their features and especially the amount of profiles abandoned and forgotten by the user are factors that increase the likelihood of identification and the privacy risks.

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Enriching Trust Prediction Model in Social Network with User Rating Similarity

Cited by 37 publications

References 9 publications

Exploiting Emotional Information for Trust/Distrust Prediction

Exploiting Emotional Information for Trust/Distrust Prediction

What Affects Web Credibility Perception? An Analysis of Textual Justifications

Escaping the Big Brother: An empirical study on factors influencing identification and information leakage on the Web

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