SpaDeS: Detecting spammers at the source network

Las-Casas, Pedro; Guedes, Dorgival; Almeida, Jussara M.; Ziviani, Artur; Marques-Neto, Humberto Torres

doi:10.1016/j.comnet.2012.07.015

Cited by 14 publications

(6 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moh and Murmann [33] established a feature-based matrix to determine the trust degree of users. Las-Casas et al [34] introduced a new detection methodology based on the source network, in which a supervised classification technique and network-level metrics are employed. Santos et al [35] proposed a content-based spam tweets filtering approach.…”

Section: Related Workmentioning

confidence: 99%

An Internet Water Army Detection Supernetwork Model

et al. 2019

View full text Add to dashboard Cite

The emergence of Internet water armies has strongly affected the information quality of online communication platforms, thus disrupting the order of the Internet. Accurate detection of Internet water armies is therefore of great significance. Based on the supernetwork theory, a new Internet water army detection model is proposed in this paper, in which a supernetwork with four layers is established, including social subnetwork, information subnetwork, psychological subnetwork, and negative keyword subnetwork. Then, personal information of users, dissemination process of information, the transformation process of different psychologies, the similarity between different keywords, and the connections between different subnetworks are considered in the model. Thus, nine composite indexes are proposed, the majority of which are used for the first time in detecting Internet water armies. A dataset selected from the largest online communication platform in China, the Weibo website, is used to test the performance of the model. Four existing water army detection models introduced in previous studies are used to provide a comparison analysis. The results show that our proposed model has better performance in terms of accuracy and stability than the other four existing models, which thanks to the employment of the supernetwork theory. We believe that our proposed model could be helpful for information researchers to further understand the complex nature of Internet water armies, as well as for the government to better manage the Internet.

show abstract

Section: Related Workmentioning

confidence: 99%

An Internet Water Army Detection Supernetwork Model

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Malicious information such as rumors and viruses has been observed recently propagating in networks, which incurs privacy and security concerns [9,10] and motivates the research of diffusion provenance detection. To date, existing works on diffusion provenance detection focus on offline detection, where a snapshot of a large network or data harvested from detectors are assumed available in advance.…”

Section: Related Workmentioning

confidence: 99%

Towards large-scale social networks with online diffusion provenance detection

Wang

Pan

et al. 2017

Computer Networks

View full text Add to dashboard Cite

In this paper we study a new problem of online discovering diffusion provenances in large networks. Existing work on network diffusion provenance identification focuses on offline learning where data collected from network detectors are static and a snapshot of network is available before learning. However, an offline learning model does not meet the needs of early warning, real-time awareness, and real-time response of malicious information spreading in networks. To this end, we propose an online regression model for real-time diffusion provenance identification. Specifically, we first use offline collected network cascades to infer the edge transmission weights, and then use an online l 1 non-convex regression model as the identification model. The proposed methods are empirically evaluated on both synthetic and real-world networks. Experimental results demonstrate the effectiveness of the proposed model.

show abstract

“…Benevenuto et al used the classification strategy to identify the garbage users amaong video users in social networks by customizing the attributes and social characteristics of video users [ 42 ]. Las-Casas et al proposed a method called SpaDeS to identify spammers in source network [ 43 ]. This method relies on the supervised classification technique, and only works according to network-level metrics.…”

Section: Introductionmentioning

confidence: 99%

“…The company Facebook developed proposed a EdgeRank algorithm to identify spammers, which scores for this post according to some attributes of each post. The post of low score is more likely to be spammering behaviors [ 36 , 42 , 43 ]. In addition to these works, spamming behaviors are studied from the pespective of semantic analysis.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the robustness of recommender systems against spammers

Chen

Liu

et al. 2018

PLoS ONE

View full text Add to dashboard Cite

The accuracy and diversity of recommendation algorithms have always been the research hotspot of recommender systems. A good recommender system should not only have high accuracy and diversity, but also have adequate robustness against spammer attacks. However, the issue of recommendation robustness has received relatively little attention in the literature. In this paper, we systematically study the influences of different spammer behaviors on the recommendation results in various recommendation algorithms. We further propose an improved algorithm by incorporating the inner-similarity of user’s purchased items in the classic KNN approach. The new algorithm effectively enhances the robustness against spammer attacks and thus outperforms traditional algorithms in recommendation accuracy and diversity when spammers exist in the online commercial systems.

show abstract

SpaDeS: Detecting spammers at the source network

Cited by 14 publications

References 26 publications

An Internet Water Army Detection Supernetwork Model

An Internet Water Army Detection Supernetwork Model

Towards large-scale social networks with online diffusion provenance detection

Enhancing the robustness of recommender systems against spammers

Contact Info

Product

Resources

About