Fraud-Agents Detection in Online Microfinance: A Large-Scale Empirical Study

Wu, Yiming; Zhiyuan, Xie; Ji, Shouling; Liu, Zhenguang; Zhang, Xuhong; Lin, Changting; Zhou, Jun; Wang, Ting; Beyah, Raheem

doi:10.1109/tdsc.2022.3151132

Cited by 6 publications

(1 citation statement)

References 40 publications

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“…Therefore, more and more studies have tried to verify the authenticity of news by features beyond its contents. Research shows that the diffusion structure between real and fake news is different in nature; thus, many studies have been conducted to identify fake news by mining the structural features of information in the process of diffusion [13][14][15]. A natural thought would be utilizing graph convolutional networks (GCN) to extract the topological features in the diffusion process [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Research on Fake News Detection Based on Diffusion Growth Rate

Chen

Jia

et al. 2022

Wireless Communications and Mobile Computing

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With the rapid development of the Internet, social media has become a convenient online platform for users to obtain information, express opinions, and communicate with each other. Users are keen to participate in discussions on hot topics and exchange opinions on social media. A lot of fake news has also arisen at this moment. However, existing fake news detection methods have the problem of relying too much on textual features. Textual features are easy to be tampered with and deceive the detector; thus, it is difficult to distinguish fake news only by relying on textual features. To address the challenge, we propose a fake news detection method based on the diffusion growth rate (Delta-G). To identify the real and fake news, Delta-G uses graph convolutional networks to extract the diffusion structure features and then adopts the long-short-term memory networks to extract the growth rate features on time series. In the experiments, Delta-G is verified on two news datasets, Twitter and Weibo. Compared with the three detection methods of decision tree classifier, support vector machines with a propagation tree kernel, and RvNN, the accuracy of the Delta-G on the two datasets is improved by an average of 5% or more, which is better than all the baselines.

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Section: Introductionmentioning

confidence: 99%

Research on Fake News Detection Based on Diffusion Growth Rate

Chen

Jia

et al. 2022

Wireless Communications and Mobile Computing

Self Cite

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Behavioral authentication for security and safety

Wang,

Tang,

Zhu

et al. 2024

Security and Safety

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The issues of both system security and safety can be dissected integrally from the perspective of behavioral \emph{appropriateness}. That is, a system is secure or safe can be judged by whether the behavior of certain agent(s) is \emph{appropriate} or not. Specifically, a so-called \emph{appropriate behavior} involves the right agent performing the right actions at the right time under certain conditions. Then, according to different levels of appropriateness and degrees of custodies, behavioral authentication can be graded into three levels, i.e., the authentication of behavioral \emph{Identity}, \emph{Conformity}, and \emph{Benignity}. In a broad sense, for the security and safety issue, behavioral authentication is not only an innovative and promising method due to its inherent advantages but also a critical and fundamental problem due to the ubiquity of behavior generation and the necessity of behavior regulation in any system. By this classification, this review provides a comprehensive examination of the background and preliminaries of behavioral authentication. It further summarizes existing research based on their respective focus areas and characteristics. The challenges confronted by current behavioral authentication methods are analyzed, and potential research directions are discussed to promote the diversified and integrated development of behavioral authentication.

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