Collective Spammer Detection in Evolving Multi-Relational Social Networks

Fakhraei, Shobeir; Foulds, James; Shashanka, Madhusudana; Getoor, Lise

doi:10.1145/2783258.2788606

Cited by 91 publications

(71 citation statements)

References 34 publications

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“…Among them even fewer are practical for large‐scale social media platforms. This is also pointed out in the recent survey by Yu et al As a result of this search, we selected eleven recent papers . The approaches in these papers have been tested or are actively in use in real social network media platforms.…”

Section: Overview Of Literature Review Methodologymentioning

confidence: 99%

“…Besides as abstract semantic features, temporal patterns can also be aggregated as sequence‐based features. The project of Fakhraei et al uses such approach. Specifically, it considers the sequence of each user u ’s action as follows:

S^{u} = r_{p}, \dots, r_{q} .

…”

Section: Learning Methodologiesmentioning

confidence: 99%

“…In this section, we will discuss the different types of such activities. In Table , we list several projects that address this problem in popular social networks like Facebook, Twitter, Renren, LinkedIn, Instagram, http://Tagged.com, Tencent Weibo, and two media platforms, YouTube, and Wikipedia . Generally, deceptive engagement activities can be classified into four main types: spams, such as comment spams for the purposes of unsolicited advertisements, phishing, and virus attacks; disinformation, which refers to actions by criminals, politicians, or terrorists that deliberately tamper community‐contributed content or spread fabricated facts in social media for illicitly shaping and influencing the opinions of social media crowd; artificial traffic, content or ratings, which refers to inflated traffic or ratings of web content not reflecting realistic user's genuine opinions, such as artificial retweeting, fake page ‘likes’, or YouTube video views.…”

Section: Deceptive Engagement In Social Mediamentioning

confidence: 99%

“…For spam detection problem, we use six projects as examples. Some of those projects consider multiple types of user actions to identify spammers.…”

Section: Deceptive Engagement In Social Mediamentioning

confidence: 99%

See 3 more Smart Citations

Detecting deceptive engagement in social media by temporal pattern analysis of user behaviors: a survey

Xiao

Bertino

2017

WIREs Data Min & Knowl

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Deceptive engagement in social media, such as spamming, commenting, or rating with automatic scripts, spreading fabricated facts, seriously affects users’ trust on online services. Given the large volumes of information generated by users, effectively spotting users involved such deceptive engagement has become a challenging problem. Recent research has shown that techniques for analyzing temporal behavioral patterns are critical to address such problem. In this study, we survey recent advances in these techniques. We first summarize three approaches to model temporal information. Then, by using representative application examples, we discuss recent approaches with respect to their applications to real‐world large‐scale social media. With a focus on the temporal perspective, we then discuss advantages and challenges of each approach. WIREs Data Mining Knowl Discov 2017, 7:e1210. doi: 10.1002/widm.1210 This article is categorized under: Fundamental Concepts of Data and Knowledge > Knowledge Representation Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining Technologies > Prediction

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Section: Overview Of Literature Review Methodologymentioning

confidence: 99%

S^{u} = r_{p}, \dots, r_{q} .

…”

Section: Learning Methodologiesmentioning

confidence: 99%

Section: Deceptive Engagement In Social Mediamentioning

confidence: 99%

“…For spam detection problem, we use six projects as examples. Some of those projects consider multiple types of user actions to identify spammers.…”

Section: Deceptive Engagement In Social Mediamentioning

confidence: 99%

See 2 more Smart Citations

Detecting deceptive engagement in social media by temporal pattern analysis of user behaviors: a survey

Xiao

Bertino

2017

WIREs Data Min & Knowl

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“…Similar with review spam detection, various behavior indicators such as review/rating posting time, 17 rating deviation, 17 burst review ratio, 3 reviewer burstiness, 4 and ratio of verified purchase (only in Amazon) 4 are extracted to represent the users as vectors, and the machine learning-based classification models are employed to distinguish spammers from normal users. Fakhraei et al 22 Spammer group detection. Besides, some researchers proposed network-based spammer detection methods, which model the users, reviews, products, and their relations as a review network and then propagate the spam labels along the edges 19 or compute the suspicious score by an iterative algorithm similar to HITS.…”

Section: Related Workmentioning

confidence: 99%

Spotting review spammer groups: A cosine pattern and network based method

Zhang

Cao

et al. 2018

Concurrency and Computation

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Nowadays, online product reviews strongly influence the purchase decision of consumers in e-commerce platforms. Driven by the immense financial profits, review spammers deliberately post fake reviews to promote or demote their target products. Some spammers are even organized as groups to work together and try to take total control of the sentiment on their target products. To detect such spammer groups, most previous works exploit frequent itemset mining (FIM) to find spammer group candidates and then use unsupervised spamicity ranking methods to identify real spammer groups. However, these methods usually suffer from the problem of threshold setting, ie, high support value finding fewer groups while low support value leading to more coincidentally generated groups and computational inefficiency. Moreover, the unsupervised methods are not able to make good use of labeled instances which are actually obtainable in practice. In this paper, we propose CONSGD, a cosine pattern and heterogeneous information network-based spammer group detecting method. Specifically, the CONSGD uses cosine pattern mining (CPM) to discover tight spammer group candidates with a respective low support value, where the cosine threshold is utilized to avoid coincidentally generated groups. Moreover, CONSGD employs heterogeneous information network classification to identify the real spammer groups, which could utilize the labeled instances and do not rely to the assumption of independent instances. Experiments on real-life dataset show that our proposed CONSGD is effective and outperforms the state-of-the-art spammer group detection methods. KEYWORDScosine pattern, heterogeneous information network, review spammer group detecting, tight spammer group INTRODUCTIONNowadays, the consumers often read product reviews before they buy the product in e-commerce platforms. When the reviews of one product are positive, the consumer will be very likely to buy it. On the contrary, if most of the reviews are negative, the consumer will discard to buy the product and choose another one. Driven by the immense financial profits, some review spammers (also called opinion spammer) post fake reviews to promote their products or to demote their competitors' products. As the manipulated reviews are misleading more and more consumers in their decision making of purchase, detecting such fake reviews or their posters, ie, spammers, becomes a pressing issue.Ever since the problem of review spam detecting was proposed by Jindal and Liu, 1 many efforts have been done for fake review or individual review spammer detecting. 2-6 Recently, however, many spammers are organized to post fake reviews together to promote or demote some target products. These collusive spammers, also called spammer group, can be highly damaging as they have many accounts to review one product and therefore could take total control of the sentiment on this product. Meanwhile, the spammer group has more manpower to imperceptibly manipulate reviews so that each group member may no longer appear ...

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Deep Dual Support Vector Data description for anomaly detection on attributed networks

Zhang

Fan

Wang

et al. 2021

Int J of Intelligent Sys

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Networks are ubiquitous in the real world such as social networks and communication networks, and anomaly detection on networks aims at finding nodes whose structural or attributed patterns deviate significantly from the majority of reference nodes. However, most of the traditional anomaly detection methods neglect the relation structure information among data points and therefore cannot effectively generalize to the graph structure data. In this paper, we propose an end-to-end model of Deep Dual Support Vector Data description based Autoencoder (Dual-SVDAE) for anomaly detection on attributed networks, which considers both the structure and attribute for attributed networks. Specifically, Dual-SVDAE consists of a structure autoencoder and an attribute autoencoder to learn the latent representation of the node in the structure space and attribute space, respectively.Then, a dual-hypersphere learning mechanism is imposed on them to learn two hyperspheres of normal nodes from the structure and attribute perspectives, respectively. Moreover, to achieve joint learning between the structure and attribute of the network, we fuse the structure embedding and attribute embedding as the final input of the feature decoder to generate the node attribute. Finally, abnormal nodes can be

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Collective Spammer Detection in Evolving Multi-Relational Social Networks

Cited by 91 publications

References 34 publications

Detecting deceptive engagement in social media by temporal pattern analysis of user behaviors: a survey

Detecting deceptive engagement in social media by temporal pattern analysis of user behaviors: a survey

Spotting review spammer groups: A cosine pattern and network based method

Deep Dual Support Vector Data description for anomaly detection on attributed networks

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