Reputation‐based credibility analysis of Twitter social network users

Alrubaian, Majed; Al‐Qurishi, Muhammad; Al-Rakhami, Mabrook; Hassan, Mohammad Mehedi; Alamri, Atif

doi:10.1002/cpe.3873

Cited by 55 publications

(64 citation statements)

References 29 publications

(35 reference statements)

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“…In (Alrubaian et al, 2017), authors presented an approach for twitter user credibility evaluation. It explores features that characterize user expertise and reputation to obtain an evaluation of user credibility.…”

Section: Related Workmentioning

confidence: 99%

Twitter Users Crediblity Evaluation Based on Social Graph Impression

Slimi¹,

Bounhas²,

Slimani³

2019

Proceedings of the 16th International Conference on Applied Computing 2019

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In the era of social media, information became abundant, versatile and easily accessible. Such characteristics enable almost anyone to publish and share their opinion about a variety of subjects. Such freedom comes at a cost since information and source credibility are in jeopardy. Therefore, numerous solutions were proposed to evaluate source credibility. In this paper, we review previous efforts on twitter credibility evaluation. We also propose an approach that relies on the impression of user social network on his tweets to determine his credibility. Furthermore, we incorporate a feature that determines the credibility of the URLs shared by the user through his tweets. The evaluation of our approach has shown it outperforms the baseline in both precision and recall.

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Section: Related Workmentioning

confidence: 99%

Twitter Users Crediblity Evaluation Based on Social Graph Impression

Slimi¹,

Bounhas²,

Slimani³

2019

Proceedings of the 16th International Conference on Applied Computing 2019

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“…Machine learning-based methods can be supervised or unsupervised, and they are based on building classifiers that determine credibility scores for blogs as a measure of their factuality. Examples of this type are presented in [5,11,12]. These methods require recursive processing of data to train and test the algorithms to achieve the desired accuracy.…”

Section: Introductionmentioning

confidence: 99%

Credibility Ranking Methods Analysis of Users in Social Network Based on Relation Graph

Cheng¹,

Han²,

Zhou³

et al. 2017

Proceedings of the 2017 5th International Conference on Machinery, Materials and Computing Technology (ICMMCT 2017)

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“…24,25 In addition, obtaining reliable and sufficiently labeled data tends to be expensive and time consuming. 24,25 In addition, obtaining reliable and sufficiently labeled data tends to be expensive and time consuming.…”

mentioning

confidence: 99%

“…complex and grow rapidly in terms of user numbers and content volume. 24,25 In addition, obtaining reliable and sufficiently labeled data tends to be expensive and time consuming.…”

mentioning

confidence: 99%

SybilTrap: A graph‐based semi‐supervised Sybil defense scheme for online social networks

Al‐Qurishi¹,

Rahman²,

Alamri³

et al. 2017

Concurrency and Computation

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Sybil attacks are increasingly prevalent in online social networks. A malicious user can generate a huge number of fake accounts to produce spam, impersonate other users, commit fraud, and reach many legitimate users. For security reasons, such fake accounts have to be detected and deactivated immediately. Various defense schemes have been proposed to deal with fake accounts. However, most identify fake accounts using only the structure of social graphs, leading to poor performance. In this paper, we propose a new and scalable defense scheme, SybilTrap. SybilTrap uses a semi-supervised technique that automatically integrates the underlying features of user activities with the social structure into one system. Unlike other machine learning-based approaches, the proposed defense scheme works on unlabeled data, and it is effective in detecting targeted attacks, because it manipulates different levels of features of user profiles. We evaluate SybilTrap on a dataset collected from Twitter. We show that our proposed scheme is able to accurately detect Sybil nodes as well as huge conspiracies among them. KEYWORDS social network, Sybil attack, Sybil defense, targeted attack 1 | INTRODUCTION Online social networking services such as Facebook, Twitter, and YouTube have attracted millions of users in recent years, with individuals and companies scrambling to be part of the paradigm shift changing the world of communication. 1 The ability to address millions of users worldwideis desirable for many businesses; it is undeniable that social networks have had a massive impact in the realm of business. These platforms are so widely used that some users are now using them for malicious purposes. One of the most popular techniques is the Sybil attack. 2 During a Sybil attack, a malevolent user creates multiple fake accounts that can be used to increase the user 0 s influence within the target community. 2,3 In sufficient numbers, such fake accounts can affect public opinion on an individual or the popularity of an organization. 4 They can also change the characteristics of an audience or even create a legitimacy problem for individuals or organizations. At present, it is difficult to detect fake accounts because they do not make up a substantial proportion of users of social networks.Previous studies have proposed several defense schemes and techniques against Sybil attacks. 5-15 These approaches can be divided into graph-based detection and machine learning techniques. 2,16 Graph-based techniques assume that it is difficult for the fake accounts to quickly build relations with legitimate users, so the number of attack edges is limited for networks with a high level of trust. 17 In addition, they assume that the mixing time of benign users to be connected to each other is fast and thus detection schemes are built based on this assumption. Some researchers have questioned the effectiveness of graph-based systems in various social networks. 2,16-20 Other graph-based techniques use credit networks as a basis for Sybil tolerance. 21 Ho...

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Reputation‐based credibility analysis of Twitter social network users

Cited by 55 publications

References 29 publications

Twitter Users Crediblity Evaluation Based on Social Graph Impression

Twitter Users Crediblity Evaluation Based on Social Graph Impression

Credibility Ranking Methods Analysis of Users in Social Network Based on Relation Graph

SybilTrap: A graph‐based semi‐supervised Sybil defense scheme for online social networks

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