SMART: a Tool for Trust and Reputation Management in Social Media

Database and Expert Systems Applications, 2013

The Web 2.0 is the top manifestation of User-Generated Content systems, such as reviews, tags, comments, tweets etc. Due to their free nature such systems contain information of different quality levels. Consequently, it is difficult for users to determine the quality of the information and the reputation of its providers. The quality evaluation is a matter of great concern, especially in medical and healthcare domain. To help the posts quality assessment this paper presents an alternative to measuring reputation based on social interactions. As a test case, we explore the data structure of the Twitter micro blogging service. Our main contribution is to provide a new methodology to Rank Reputation in a network structure based on weighted social interaction. This approach can guide Internet users to encounter authority and trustworthy sources of online health and medical information in Twittershpere. The results show that the rank methodology and the network structure have succeeded in inferring user reputation.

Lecture Notes in Computer Science, 2012

Trust and Reputation Systems (TRSs) represent a significant trend in decision support for Internet-based interactions. They help users to decide whom to trust and how much to trust a transaction. They are also an effective mechanism to encourage honesty and cooperation among users, resulting in healthy online markets or communities. The basic idea is to let parties rate each other so that new public knowledge can be created from personal experiences. The major difficulty in designing a reputation system is making it robust against malicious attacks. Our contribution in this paper is twofold. Firstly, we combine multiple research agendas into a holistic approach to building a robust TRS. Secondly, we focus on one TRS component which is the reputation computing engine and provide a novel investigation into an implementation of the engine proposed in [7].

Studies in Computational Intelligence, 2011

A key-issue for the effectiveness of collaborative decision support systems is the problem of the trustworthiness of the entities involved in the process. Trust has been always used by humans as a form of collective intelligence to support effective decision making process. Computational trust models are becoming now a popular technique across many applications such as cloud computing, p2p networks, wikis, e-commerce sites, social network. The chapter provides an overview of the current landscape of computational models of trust and reputation, and it presents an experimental study case in the domain of social search, where we show how trust techniques can be applied to enhance the quality of social search engine predictions.

Proceedings of the 14th International Joint Conference on e-Business and Telecommunications, 2017

Trust and reputation management stands as a corner stone within the Online Social Networks (OSNs) since they ensure a healthy collaboration relationship among participants. Currently, most trust and reputation systems focus on evaluating the credibility of the users. The reputation systems in OSNs have as objective to help users to make difference between trustworthy and untrustworthy, and encourage honest users by rewarding them with high trust values. Computing reputation of one user within a network requires knowledge of trust degrees between the users. In this paper, we propose a new Clustering Reputation algorithm, called RepC, based on trusted network. This algorithm classifies the users of OSNs by their trust similarity such that most trustworthy users belong to the same cluster. We conduct extensive experiments on a real online social network dataset from Twitter. Experimental results show that our algorithm generates better results than do the pioneering approaches of the literature.

IEEE Access

Online Social Media (OSM) are dominating the wide range of Internet services. Due to their vast audience, it is crucial to evaluate the interpersonal trust among OSM users that can identify reliable sources of information, the meaningfulness of a relationship, or the trustworthiness of other users. SentiTrust is an innovative trust model for Decentralized Online Social Networks that is based on AI-powered Sentiment Analysis. It enriches the trust definition by exploiting important features that are enabled because of the adoption of Social Media through mobile devices. The model can be easily extended and customized according to the scenario of interest. The sentiment analysis component has been tested by involving 30 participants who completed several guided tasks using a social media application while their electrodermal activity and rate responses were measured. The results suggest that low arousal states are related to receiving happy faces and to sending more messages per minute. Furthermore, positive interactions result in shorter interactions and multimedia exchanges. INDEX TERMS Decentralized online social networks, online social networks, sentiment analysis, statistical learning, trust.

Lecture Notes in Computer Science, 2009

Communication technologies, such as e-mail, instant messaging, discussion forums, blogs, and newsgroups connect people together, forming virtual communities. This concept is not only used for private purposes, but is also attracting attention in professional environments, allowing to consult a large group of experts. Due to the overwhelming size of such communities, various reputation mechanisms have been proposed supporting members with information about people's trustworthiness with respect to their contributions. However, most of today's approaches rely on manual and subjective feedback, suffering from unfair ratings, discrimination, and feedback quality variations over time. To this end, we propose a system which determines trust relationships between community members automatically and objectively by mining communication data. In contrast to other approaches which use these data directly, e.g., by applying natural language processing on log files, we follow a new approach to make contributions visible. We perform structural analysis of discussions, examine interaction patterns between members, and infer social roles expressing motivation, openness to discussions, and willingness to share data, and therefore trust.

Nowadays, we depend on social network sites to get recommendations from other users about things such as health, academic, marriage consultation, places of interest, product ratings by consumers, and more. With the Internet containing a huge repository of social network members, it became very difficult to find trusted users that offer good advice suiting our needs. In this paper, we propose a social network-based recommendation system to offer trustworthy recommendations about establishing connection among social networks users. This system rates users by inferring their degree of trust. We investigated the accuracy of three algorithms in making such inference, namely: trust score, reputation and similarity, and trust score propagation algorithms. The algorithm accuracy is measured by its ability to recover relationships between SN members. Experimental results showed that using reputation and similarity algorithm to infer trust recovered 91.67% of relationships by making accurate recommendations.

As the web is being overtaken by the user generated content and interactions, the question has become increasingly important that what and whom to trust. Fortunately, it has become easier to indicate, for the users to who they want to and who they do not want by the help of online social networks and social media. However, the problem is not solved as every use is only likely to know a very small fraction of other users; researchers should have strategies for restraining trust -and distrust -between users who do not know one another. In this paper, different techniques of trust computation is reviewed and analyzed for negative and positive trust.Several techniques used conjunction with a conclusion algorithm which depends on the possible understanding of a trust based on a irregular graph, which happens with the Spring-embedding algorithm.

Lecture Notes in Social Networks, 2012

Social network analysis has emerged as a key technique in modern sociology, but has recently gained a lot of interest in Web mining research, because of the advent and the increasing popularity of social media, such as blogs, social networks, micro-blogging, customer review sites etc. Such media often serve as platforms for information dissemination and product placement or promotion. One way to improve the quality of recommendations provided to the members of social networks is to use trustworthy resources. In this environment, community-based reputation can help estimating the trustworthiness of individual users. Consequently, influence and trust are becoming essential qualities among user interactions. In this work, we perform an extensive study of various metrics related to the aforementioned elements, and of their effect in the process of information propagation in social networks. In order to better understand the properties of links and the dynamics of social networks, we distinguish between permanent and transient links and in the latter case, we consider the link freshness. Moreover, we distinguish between the propagation of trust in a local level and the effect of global influence and compare suggestions provided by locally trusted or globally influential users.

BIS 2008 Workshops …, 2008