Abstract
Due to the openness of online social networks (OSNs), they have become the most popular platforms for people to communicate with others in the expectation of sharing their opinions in a trustworthy environment. However, individuals are often exposed to a wide range of risks posed by malicious users who spread various fake information to achieve their vicious goals, which makes the concept of trust a vital issue. Most of the existing research attempts to construct a trust network among users, whereas only a few studies pay attention to analyzing their features. In this paper, we propose a trust evaluation framework based on machine learning to facilitate human decision making by extensively considering multiple trust-related user features and criteria. We first divide user features into four groups according to the empirical analysis, including profile-based features, behavior-based features, feedback-based features, and link-based features. Then, we design a lightweight feature selection approach to evaluate the effectiveness of every single feature and find out the optimal combination of features from users' online records. We formalize trust analysis as a classification problem to simplify the verification process. We compare the performance of our features with four other feature sets proposed in the existing research. Moreover, four traditional trust evaluation methods are employed to compare with our machine learning based methods. Experiments conducted on a real-world dataset show that the overall performance of our features and methods is superior to the other existing features and traditional approaches.
Highlights
With the fast development of the Internet in the last decade, online social networks (OSNs) have become the most prevailing platforms with significant growth of users joining and an enormous amount of data spreading through them daily [1]
In this paper, we propose a novel trust evaluation framework based on machine learning methods by conducting an in-depth analysis of user features and considering multidimensional factors
In this paper, we have proposed a multi-feature involved framework based on machine learning methods for trust evaluation in OSNs, which categorizes trust features into four groups to reveal the essence of trust from different aspects, including profile-based trust, behavior-based trust, feedbackbased trust, and link-based trust
Summary
With the fast development of the Internet in the last decade, online social networks (OSNs) have become the most prevailing platforms with significant growth of users joining and an enormous amount of data spreading through them daily [1]. There is still no systematic framework proposed to assist feature selection or form the trust-involved feature system To address this problem, in this paper, we propose a novel trust evaluation framework based on machine learning methods by conducting an in-depth analysis of user features and considering multidimensional factors. The main thrust of this framework is to find out the optimal collection of user features from the online records and quantify them into a computable form for further trust analysis using machine learning approaches. The primary contributions of this paper are outlined as follows: 1) We propose a novel framework to implement trust evaluation based on machine learning methods for facilitating human decision making in OSNs by considering multiple trust-related features.
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