Financial fraud detection using social media crowdsourcing

Matti, Timothy; Zhu, Yuntao; Xu, Kuai

doi:10.1109/pccc.2014.7017023

Cited by 5 publications

(1 citation statement)

References 3 publications

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“…Another important issue related to the structural analysis of social networks is to investigate the presence of spams, malicious activities, anomalies, and frauds within a network. 46 These events can be in any form such as cyber bullying, 47,48 terrorist attack planning, 49 embezzlement, 50 dissemination of fraud news, 51 fake accounts to create artificial numbers on pages, 52,53 and political manipulation. 54,55 Beutel et al suggested a system in which a bipartite graph is created between users and pages they liked.…”

Section: Applications Of Structural Analysismentioning

confidence: 99%

Semantic analysis on social networks: A survey

Bayrakdar

Yücedağ

Şimşek

et al. 2020

Int J Communication

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Summary As social networks are getting more and more popular day by day, large numbers of users becoming constantly active social network users. In this way, there is a huge amount of data produced by users in social networks. While social networking sites and dynamic applications of these sites are actively used by people, social network analysis is also receiving an increasing interest. Moreover, semantic understanding of text, image, and video shared in a social network has been a significant topic in the network analysis research. To the best of the author's knowledge, there has not been any comprehensive survey of social networks, including semantic analysis. In this survey, we have reviewed over 200 contributions in the field, most of which appeared in recent years. This paper not only aims to provide a comprehensive survey of the research and application of social network analysis based on semantic analysis but also summarizes the state‐of‐the‐art techniques for analyzing social media data. First of all, in this paper, social networks, basic concepts, and components related to social network analysis were examined. Second, semantic analysis methods for text, image, and video in social networks are explained, and various studies about these topics are examined in the literature. Then, the emerging approaches in social network analysis research, especially in semantic social network analysis, are discussed. Finally, the trending topics and applications for future directions of the research are emphasized; the information on what kind of studies may be realized in this area is given.

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