Predict Pairwise Trust Based on Machine Learning in Online Social Networks: A Survey

Liu, Shushu; Zhang, Lifang; Yan, Zheng

doi:10.1109/access.2018.2869699

Cited by 40 publications

(27 citation statements)

References 46 publications

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“…A basic algorithm with lots of data shows the performance edge over the modern algorithms. Liu et al [58] mentioned the datasets which are in tens of thousands in records i.e., Bitcoin and Ciao datasets. In addition to these datasets, the Epinions dataset with regards to review rating has been also used.…”

Section: The Impact Of the Data-set Size On The Trust Prediction Pmentioning

confidence: 99%

Evaluating Trust Prediction and Confusion Matrix Measures for Web Services Ranking

et al. 2020

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To accurately rank various web services can be a very challenging task depending on the evaluation criteria used, however, it can play an important role in performing a better selection of web services afterward. This paper proposes an approach to evaluate trust prediction and confusion matrix to rank web services from throughput and response time. AdaBoostM1 and J48 classifiers are used as binary classifiers on a benchmark web services dataset. The trust score (TS) measuring method is proposed by using the confusion matrix to determine trust scores of all web services. Trust prediction is calculated using 5-Fold, 10-Fold, and 15-Fold cross-validation methods. The reported results showed that the web service 1 (WS1) was most trusted with (48.5294%) TS value, and web service 2 (WS2) was least trusted with (24.0196%) TS value by users. Correct prediction of trusted and untrusted users in web services invocation has improved the overall selection process in a pool of similar web services. Kappa statistics values are used for the evaluation of the proposed approach and for performance comparison of the two above-mentioned classifiers.

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Section: The Impact Of the Data-set Size On The Trust Prediction Pmentioning

confidence: 99%

Evaluating Trust Prediction and Confusion Matrix Measures for Web Services Ranking

et al. 2020

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“…Many studies (Pereira et al 2017;Wang et al 2018;Zhao and Fan 2018) have explored how the quality of OGD can help developers reduce the time and cost of information system development. Machine learning technology is widely used in various information systems to enhance intelligence (Demarie and Sabia 2019;Kumar et al 2019;Lee and Park 2019;Liu et al 2018;Park et al 2020;Sharp et al 2018;Xiao et al 2018). Wireless sensor network (WSN) is the foundation of the Internet of Things (IoT).…”

Section: Related Workmentioning

confidence: 99%

Integrating machine learning and open data into social Chatbot for filtering information rumor

Hsu

Chang

2020

J Ambient Intell Human Comput

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Social networks have become a major platform for people to disseminate information, which can include negative rumors. In recent years, rumors on social networks has caused grave problems and considerable damages. We attempted to create a method to verify information from numerous social media messages. We propose a general architecture that integrates machine learning and open data with a Chatbot and is based cloud computing (MLODCCC), which can assist users in evaluating information authenticity on social platforms. The proposed MLODCCC architecture consists of six integrated modules: cloud computing, machine learning, data preparation, open data, chatbot, and intelligent social application modules. Food safety has garnered worldwide attention. Consequently, we used the proposed MLODCCC architecture to develop a Food Safety Information Platform (FSIP) that provides a friendly hyperlink and chatbot interface on Facebook to identify credible food safety information. The performance and accuracy of three binary classification algorithms, namely the decision tree, logistic regression, and support vector machine algorithms, operating in different cloud computing environments were compared. The binary classification accuracy was 0.769, which indicates that the proposed approach accurately classifies using the developed FSIP.

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“…Accordingly, visualization models show the trust network that helps to understand trust relationships between the members of the social network. The reader can refer to the detailed survey by Liu et al [57] for a comprehensive overview of the Machine Learning (ML) based methods for pairwise trust prediction.…”

Section: Trust and Reputation Notions And Computational Modelsmentioning

confidence: 99%

Predicting Reputation in the Sharing Economy with Twitter Social Data

Prada

Iglesias

2020

Applied Sciences

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In recent years, the sharing economy has become popular, with outstanding examples such as Airbnb, Uber, or BlaBlaCar, to name a few. In the sharing economy, users provide goods and services in a peer-to-peer scheme and expose themselves to material and personal risks. Thus, an essential component of its success is its capability to build trust among strangers. This goal is achieved usually by creating reputation systems where users rate each other after each transaction. Nevertheless, these systems present challenges such as the lack of information about new users or the reliability of peer ratings. However, users leave their digital footprints on many social networks. These social footprints are used for inferring personal information (e.g., personality and consumer habits) and social behaviors (e.g., flu propagation). This article proposes to advance the state of the art on reputation systems by researching how digital footprints coming from social networks can be used to predict future behaviors on sharing economy platforms. In particular, we have focused on predicting the reputation of users in the second-hand market Wallapop based solely on their users’ Twitter profiles. The main contributions of this research are twofold: (a) a reputation prediction model based on social data; and (b) an anonymized dataset of paired users in the sharing economy site Wallapop and Twitter, which has been collected using the user self-mentioning strategy.

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Predict Pairwise Trust Based on Machine Learning in Online Social Networks: A Survey

Cited by 40 publications

References 46 publications

Evaluating Trust Prediction and Confusion Matrix Measures for Web Services Ranking

Evaluating Trust Prediction and Confusion Matrix Measures for Web Services Ranking

Integrating machine learning and open data into social Chatbot for filtering information rumor

Predicting Reputation in the Sharing Economy with Twitter Social Data

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