ELM-based spammer detection in social networks

Zheng, Xianghan; Zhang, Xueying; Yu, Yuanlong; Kechadi, Tahar; Rong, Chunming

doi:10.1007/s11227-015-1437-5

Cited by 40 publications

(27 citation statements)

References 22 publications

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“…Uma alternativa desenvolvida com o objetivo de detectar variações inorgânicas de valores desses atributos é a geração de modelos de comportamento temporal, explorada por [Benevenuto et al 2010;Gao et al 2011;Jin et al 2011;Lee et al 2011;Yang et al 2011;Tan et al 2012;Yang et al 2013;Zheng et al 2015]. Considerando então dados históricos do usuário, os modelos detectam anomalias e então sinalizam a conta como spammer.…”

Section: •unclassified

A Survey and a Preliminary Evaluation of Low-quality Content Detection Strategies: Which Attributes Are Still Relevant, Which Are Not?

Resende¹,

Moraes²,

Silva³

et al. 2019

Anais Do VII Symposium on Knowledge Discovery, Mining and Learning (KDMiLe 2019)

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Online social networks have gone mainstream: millions of users have come to rely on the wide range of services provided by social networks. However, the ease use of social networks for communicating information also makes them particularly vulnerable to social spammers, i.e., ill-intentioned users whose main purpose is to degrade the information quality of social networks through the proliferation of different types of malicious data (e.g., social spam, malware downloads, and phishing) that are collectively called low-quality content or spams. Since Twitter is also rife with low-quality content, several researchers have devised various low-quality detection strategies that inspect tweets for the existence of spam contents. We carried out a literature survey of these low-quality detection strategies, examining which strategies are still applicable in the current scenario – taken into account that Twitter has undergone a lot of changes in the last few years. To gather some evidence of the usefulness of the attributes used by the low-quality detection strategies, we carried out a preliminary evaluation of these attributes.

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Section: •unclassified

A Survey and a Preliminary Evaluation of Low-quality Content Detection Strategies: Which Attributes Are Still Relevant, Which Are Not?

Resende¹,

Moraes²,

Silva³

et al. 2019

Anais Do VII Symposium on Knowledge Discovery, Mining and Learning (KDMiLe 2019)

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“…It is important to note that spammers may use anonymisers, making it difficult to trace them. In order to overcome this problem, several social network spam filters have recently been developed [10][11][12][13][14][15][16][17][18][19][20][21][22].…”

Section: Social Network Spam Filtering -A Literature Reviewmentioning

confidence: 99%

“…The most important features were then used in the SVM classifier for spam detection. Extreme learning machines were used by [18] on a similar dataset. A semi-supervised social media spammer filtering approach was developed in [19].…”

Section: Social Network Spam Filtering -A Literature Reviewmentioning

confidence: 99%

Spam Filtering in Social Networks Using Regularized Deep Neural Networks with Ensemble Learning

Barushka

Hájek

2018

IFIP Advances in Information and Communication Technology

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Spam filtering in social networks is increasingly important owing to the rapid growth of social network user base. Sophisticated spam filters must be developed to deal with this complex problem. Traditional machine learning approaches such as neural networks, support vector machine and Naïve Bayes classifiers are not effective enough to process and utilize complex features present in high-dimensional data on social network spam. To overcome this problem, here we propose a novel approach to social network spam filtering. The approach uses ensemble learning techniques with regularized deep neural networks as base learners. We demonstrate that this approach is effective for social network spam filtering on a benchmark dataset in terms of accuracy and area under ROC. In addition, solid performance is achieved in terms of false negative and false positive rates. We also show that the proposed approach outperforms other popular algorithms used in spam filtering, such as decision trees, Naïve Bayes, artificial immune systems, support vector machines, etc.

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“…Based on the idea of classification, the researchers have designed numerical form characteristics to identify spam users. The supervised machine learning algorithm can be split into a single classification algorithm and an integrated classification algorithm (e.g., Support Vector Machine (SVM) [3] [8][9][10][11] [13][14], meta-classifiers (Decorate, Logit Boost) [4], Naive Bayesian (NB) [6][9] [11], Back Propagation Neural Network (BP) [16], Radial Basis Function (RBF) [18], Extreme Learning Machine (ELM) [8] [22], K-nearest Neighbor (KNN) [9] [19], Decision Tree (DT) [9] [20], Random Forest (RF) [5] [7][8][9][ [23][24][25][26] and eXtreme Gradient Boosting (XGBoost) [31,32]).…”

Section: Introductionmentioning

confidence: 99%

Detection of Social Network Spam Based on Improved Extreme Learning Machine

2020

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With the rapid advancement of the online social network, social media like Twitter has been increasingly critical to real life and become the prime objective of spammers. Twitter spam detection refers to a complex task for the involvement of a range of characteristics, and spam and non-spam have caused unbalanced data distribution in Twitter. To solve the mentioned problems, Twitter spam characteristics are analyzed as the user attribute, content, activity and relationship in this study, and a novel spam detection algorithm is designed based on regularized extreme learning machine, called the Improved Incremental Fuzzy-kernel-regularized Extreme Learning Machine (I2FELM), which is used to detect the Twitter spam accurately. As revealed from the experience validation results, the proposed I2FELM can efficiently identify the balanced and unbalanced dataset. Moreover, with few characteristics taken, the I2FELM can more effectively detect spam, which proves the effectiveness of the algorithm.

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ELM-based spammer detection in social networks

Cited by 40 publications

References 22 publications

A Survey and a Preliminary Evaluation of Low-quality Content Detection Strategies: Which Attributes Are Still Relevant, Which Are Not?

A Survey and a Preliminary Evaluation of Low-quality Content Detection Strategies: Which Attributes Are Still Relevant, Which Are Not?

Spam Filtering in Social Networks Using Regularized Deep Neural Networks with Ensemble Learning

Detection of Social Network Spam Based on Improved Extreme Learning Machine

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