Attribute selection for improving spam classification in online social networks: a rough set theory-based approach

Dutta, Soumi; Ghatak, Sujata; Dey, R.; Das, Asit Kumar; Ghosh, Saptarshi

doi:10.1007/s13278-017-0484-8

Cited by 31 publications

(10 citation statements)

References 28 publications

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“…In this study, two spam datasets from Costa, et al [7] and Dutta, et al [36] are used for training and testing the proposed models. These datasets (herein referred to as Dataset 1 and Dataset 2) are about "Tip Spam" in location-based social networks.…”

Section: Spam Datasetmentioning

confidence: 99%

Heterogeneous Ensemble with Combined Dimensionality Reduction for Social Spam Detection

Oladepo

Bajeh

Balogun

et al. 2021

Int. J. Interact. Mob. Technol.

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This study presents a novel framework based on a heterogeneous ensemble method and a hybrid dimensionality reduction technique for spam detection in micro-blogging social networks. A hybrid of Information Gain (IG) and Principal Component Analysis (PCA) (dimensionality reduction) was implemented for the selection of important features and a heterogeneous ensemble consisting of Naïve Bayes (NB), K Nearest Neighbor (KNN), Logistic Regression (LR) and Repeated Incremental Pruning to Produce Error Reduction (RIPPER) classifiers based on Average of Probabilities (AOP) was used for spam detection. The proposed framework was applied on MPI_SWS and SAC’13 Tip spam datasets and the developed models were evaluated based on accuracy, precision, recall, f-measure, and area under the curve (AUC). From the experimental results, the proposed framework (that is, Ensemble + IG + PCA) outperformed other experimented methods on studied spam datasets. Specifically, the proposed method had an average accuracy value of 87.5%, an average precision score of 0.877, an average recall value of 0.845, an average F-measure value of 0.872 and an average AUC value of 0.943. Also, the proposed method had better performance than some existing methods. Consequently, this study has shown that addressing high dimensionality in spam datasets, in this case, a hybrid of IG and PCA with a heterogeneous ensemble method can produce a more effective method for detecting spam contents.

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Section: Spam Datasetmentioning

confidence: 99%

Heterogeneous Ensemble with Combined Dimensionality Reduction for Social Spam Detection

Oladepo

Bajeh

Balogun

et al. 2021

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

show abstract

“…If they are infected, the sensors monitor them frequently and alert message pass to doctors and hospitals. In the cloud layer, social network analysis [58][59][60] (SNA) graph is generated for the infected users and mosquito breeding sites. The system uses information fragmentation and key sharing mechanism to avoid unlicensed access to data and provide security.…”

Section: Enhancement Of Healthcare Applications Using Internet Of Thingsmentioning

confidence: 99%

A study on medical Internet of Things and Big Data in personalized healthcare system

Jagadeeswari

Subramaniyaswamy

Ravi

et al. 2018

Health Inf Sci Syst

265

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Personalized healthcare systems deliver e-health services to fulfill the medical and assistive needs of the aging population. Internet of Things (IoT) is a significant advancement in the Big Data era, which supports many real-time engineering applications through enhanced services. Analytics over data streams from IoT has become a source of user data for the healthcare systems to discover new information, predict early detection, and makes decision over the critical situation for the improvement of the quality of life. In this paper, we have made a detailed study on the recent emerging technologies in the personalized healthcare systems with the focus towards cloud computing, fog computing, Big Data analytics, IoT and mobile based applications. We have analyzed the challenges in designing a better healthcare system to make early detection and diagnosis of diseases and discussed the possible solutions while providing e-health services in secure manner. This paper poses a light on the rapidly growing needs of the better healthcare systems in real-time and provides possible future work guidelines.

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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%

“…Several researchers employed feature selection and extraction methodologies to identify the most important features for social network spam filtering. The concept of rough set theory was applied by [22], concluding that the used methodology selected a smaller subset of features than those of the baseline methodologies. By considering important features of the posts and their corresponding comments, and finally applying the feature selection techniques, the method proposed in [23] selected the most effective features to detect spam using machine learning techniques.…”

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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Attribute selection for improving spam classification in online social networks: a rough set theory-based approach

Cited by 31 publications

References 28 publications

Heterogeneous Ensemble with Combined Dimensionality Reduction for Social Spam Detection

Heterogeneous Ensemble with Combined Dimensionality Reduction for Social Spam Detection

A study on medical Internet of Things and Big Data in personalized healthcare system

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

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