Trusting the Social Web: issues and challenges

Nepal, Surya; Paris, Cécile; Bouguettaya, Athman

doi:10.1007/s11280-013-0252-2

Cited by 22 publications

(10 citation statements)

References 16 publications

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“…The Information has been recorded in the Web with various types of documents, coming from a wide variety of different sources, involves a discussion topics that wide, and the style that's not the same [11]. Specially, Web directly has affected the management of knowledge: a variety of document formats, and news presentation also involves a lot of media [12].…”

Section: A Review and Motivationmentioning

confidence: 99%

A Method to Extract the Forensic about Negative Issues from Web

Nasution

Hardi

Sitepu

et al. 2017

IOP Conf. Ser.: Mater. Sci. Eng.

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Abstract. In the social world there are many issues: positive or negative. The negative issues affect the level of social comfort. On social media such as Web, every issue positioned based on the document, which has its own attributes, such as the URL address and date of creation. Not easy to extract information from the Web, as well as to determine the origin of an issue that is flowing in the web. This paper is to derive a method for revealing the origin of an issue based on the characteristics of each webpage.

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Section: A Review and Motivationmentioning

confidence: 99%

A Method to Extract the Forensic about Negative Issues from Web

Nasution

Hardi

Sitepu

et al. 2017

IOP Conf. Ser.: Mater. Sci. Eng.

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“…Service providers or third party agents may fail partially or fully in delivering the promised QoS at runtime [6]. On the other hand, it is not easy for users to identify the untrustworthy Web services [7].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent QoS Identification for Untrustworthy Web Services via Two-Phase Neural Networks

Wang

Lü

2016

2016 IEEE International Conference on Web Services (ICWS)

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Abstract-QoS identification for untrustworthy Web services is critical in QoS management in the service computing since the performance of untrustworthy Web services may result in QoS downgrade. The key issue is to intelligently learn the characteristics of trustworthy Web services from different QoS levels, then to identify the untrustworthy ones according to the characteristics of QoS metrics. As one of the intelligent identification approaches, deep neural network has emerged as a powerful technique in recent years. In this paper, we propose a novel two-phase neural network model to identify the untrustworthy Web services. In the first phase, Web services are collected from the published QoS dataset. Then, we design a feedforward neural network model to build the classifier for Web services with different QoS levels. In the second phase, we employ a probabilistic neural network (PNN) model to identify the untrustworthy Web services from each classification. The experimental results show the proposed approach has 90.5% identification ratio far higher than other competing approaches.

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“…Online Social Networks (OSNs) have been defined by Nepal [1] as the system compounds of certain tools, applications and platforms that sustain the online social interactions of people and communities. Examples of such web-based social media include, but are not limited to, Facebook®, LinkedIn® and Twitter®.…”

Section: Introduction (Heading 1)mentioning

confidence: 99%

A Preliminary Approach to Domain-Based Evaluation of Users' Trustworthiness in Online Social Networks

Abu-Salih

Wongthongtham

Beheshti

et al. 2015

2015 IEEE International Congress on Big Data

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Online Social Networks (OSNs) are a fertile medium through which users can unleash their opinions and share their thoughts, activities and knowledge of various topics and domains. This medium allows legitimate users as well as spammers to publish their content, leveraging the open environment and fewer restrictions associated with OSNs. Hence, it is essential to evaluate users' credibility in various domains and accordingly make judgements about potentially influential users in a particular domain(s). Most of the existing trustworthiness evaluation approaches of users and their posts in OSNs are generic-based approaches. There is a lack of domain-based trustworthiness evaluation mechanisms. In OSNs, discovering users' influence in a specific domain has been motivated by its significance in a broad range of applications such as personalized recommendation systems and expertise retrieval. The aim of this paper is to present a preliminary approach to evaluating domain-based user's trustworthiness in OSNs. We provide a novel distinguishing measurement for users in a set of knowledge domains. Domains are extracted from the user's content using semantic analysis. In order to obtain the level of trustworthiness, a metric incorporating a number of attributes extracted from content analysis and user analysis is consolidated and formulated considering temporal factor. The approach presented in this paper is promising since it provides a finegrained trustworthiness analysis of users and their domains of interest in the OSNs.

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Trusting the Social Web: issues and challenges

Cited by 22 publications

References 16 publications

A Method to Extract the Forensic about Negative Issues from Web

A Method to Extract the Forensic about Negative Issues from Web

An Intelligent QoS Identification for Untrustworthy Web Services via Two-Phase Neural Networks

A Preliminary Approach to Domain-Based Evaluation of Users' Trustworthiness in Online Social Networks

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