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

机译:基于域的在线社交网络用户可信度评估的初步方法

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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 fine-grained trustworthiness analysis of users and their domains of interest in the OSNs.
机译:在线社交网络(OSN)是一种肥沃的媒体,用户可以通过它释放自己的观点,并分享他们对各种主题和领域的想法,活动和知识。这种介质允许合法用户以及垃圾邮件发送者利用开放环境以及较少的与OSN相关的限制来发布其内容。因此,至关重要的是评估用户在各个领域中的信誉,并据此做出对特定领域中潜在影响用户的判断。用户及其在OSN中的职位的大多数现有可信度评估方法都是基于通用的方法。缺乏基于域的可信度评估机制。在OSN中,发现用户在特定领域中的影响力是由于其在广泛的应用程序(例如个性化推荐系统和专业知识检索)中的重要性而引起的。本文的目的是提出一种评估OSN中基于域的用户可信度的初步方法。我们为一组知识领域的用户提供了一种新颖的区分性度量。使用语义分析从用户的内容中提取域。为了获得可信度,考虑到时间因素,合并并制定了一个包含从内容分析和用户分析中提取的许多属性的度量。本文提供的方法很有希望,因为它提供了OSN中用户及其关注域的细粒度可信赖性分析。

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