To Trust or Not to Trust? Predicting Online Trusts Using Trust Antecedent Framework

Nguyen, Viet-An; Lim, Ee-Peng; Jiang, Jing; Sun, Aixin

doi:10.1109/icdm.2009.115

Cited by 44 publications

(29 citation statements)

References 8 publications

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“…Algorithms for all variations of trust prediction are either unsupervised methods [23,77] or supervised methods [43,59]. In the following two subsections, we will investigate distrust prediction in both unsupervised [80] and supervised scenarios [73].…”

Section: Discussionmentioning

confidence: 99%

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Computing Distrust in Social Media

Tang¹

2015

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A myriad of social media services are emerging in recent years that allow people to communicate and express themselves conveniently and easily. The pervasive use of social media generates massive data at an unprecedented rate. It becomes increasingly difficult for online users to find relevant information or, in other words, exacerbates the information overload problem. Meanwhile, users in social media can be both passive content consumers and active content producers, causing the quality of user-generated content can vary dramatically from excellence to abuse or spam, which results in a problem of information credibility. Trust, providing evidence about with whom users can trust to share information and from whom users can accept information without additional verification, plays a crucial role in helping online users collect relevant and reliable information. It has been proven to be an effective way to mitigate information overload and credibility problems and has attracted increasing attention.As the conceptual counterpart of trust, distrust could be as important as trust and its value has been widely recognized by social sciences in the physical world. However, little attention is paid on distrust in social media. Social media differs from the physical world -(1) its data is passively observed, large-scale, incomplete, noisy and embedded with rich heterogeneous sources; and (2) distrust is generally unavailable in social media. These unique properties of social media present novel challenges for computing distrust in social media: (1) passively observed social media data does not provide necessary information social scientists use to understand distrust, how can I understand distrust in social media? (2) distrust is usually invisible in social media, how can I make invisible distrust visible by leveraging unique properties of social media data? and (3) little is known about distrust and its role in social media applications, how can distrust help make difference in social media applications?The chief objective of this dissertation is to figure out solutions to these challenges i via innovative research and novel methods. In particular, computational tasks are designed to understand distrust, a innovative task, i.e., predicting distrust is proposed with novel frameworks to make invisible distrust visible, and principled approaches are develop to apply distrust in social media applications. Since distrust is a special type of negative links, I demonstrate the generalization of properties and algorithms of distrust to negative links, i.e., generalizing findings of distrust, which greatly expands the boundaries of research of distrust and largely broadens its applications in social media. He is the mentor because I always seek his suggestions and help for many aspects in my life; and he is also my friend because I feel free to share my personal happiness and sadness with him. Dr.Liu, I cannot thank you enough.

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Section: Discussionmentioning

confidence: 99%

“…From a personalization perspective, trust metrics can be classified as global [30] and local trust metrics [21]. From a methodology perspective, trust metrics can be supervised [59] or unsupervised [108]. From a network perspective, trust metrics can be binary or continuous [87].…”

Section: Computing Trust In Social Mediamentioning

confidence: 99%

Computing Distrust in Social Media

Tang¹

2015

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“…450 We do not compare our proposed frame work with the methods proposed in [18,44] because these methods use additional data sources to work such as user interaction activities. Another reason is that these methods are supervised, whereas our method is unsupervised.…”

Section: Rsmentioning

confidence: 99%

Cluster-level trust prediction based on multi-modal social networks

Zhang

Liu

2016

Neurocomputing

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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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To Trust or Not to Trust? Predicting Online Trusts Using Trust Antecedent Framework

Cited by 44 publications

References 8 publications

Computing Distrust in Social Media

Computing Distrust in Social Media

Cluster-level trust prediction based on multi-modal social networks

Exploiting Emotional Information for Trust/Distrust Prediction

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