Addressing the class imbalance problem in Twitter spam detection using ensemble learning

Liu, Shigang; Wang, Yu; Zhang, Jun; Chen, Chao; Xiang, Yang

doi:10.1016/j.cose.2016.12.004

Cited by 97 publications

(67 citation statements)

References 8 publications

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“…13 Generally speaking, an ensemble is built in three steps 6,13,39 which are ensemble strategy, classifiers (learners), and final results generation procedure. 13 Generally speaking, an ensemble is built in three steps 6,13,39 which are ensemble strategy, classifiers (learners), and final results generation procedure.…”

Section: Fuzzy-based Ensemble Resamplingmentioning

confidence: 99%

“…40 In Liu et al, 6 a new way of using ensemble approach to empower the fuzzy-based imbalanced data repairing algorithm 31 has been proposed. Just as we mentioned above, we will use the popular one which is majority voting method.…”

Section: Fuzzy-based Ensemble Resamplingmentioning

confidence: 99%

“…to measure the classification performance, 6 which can be calculate as We can see that trend of the F-measure metric drops with the increase of class imbalance rate. For example, C4.5 decision tree, which performs the second best, drops from about 0.83 with IR = 2 to 0.78 with IR = 20.…”

Section: Machine Learning Based Twitter Spam Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Previous study has shown that the detection rate for Twitter spam can be decreased for about 33% in average with the class imbalance rate rises from 2 to 20. 6 Hence, a natural question in data mining research is how to improve the performance of classifiers facing with imbalanced data?Existing techniques for handling the class imbalance problem are mainly from three perspectives, including data sampling, algorithmic and ensemble learning. Specifically, data sampling is a technique that re-balances the data distribution among classes either randomly or intelligently.…”

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A comparative study of the class imbalance problem in Twitter spam detection

Liu

2017

Concurrency and Computation

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Recently, online social network (OSN) such as Twitter has become an important and popular source for real-time information and news dissemination, and Twitter is inevitably a prime target of spammers. It has been showed that the security threats caused by Twitter spam can reach far beyond the social media platform itself. To mitigate the damage caused by Twitter spam, machine learning classification algorithms have been employed by researchers and communities to detect the Twitter spam. However, most of these studies have overlooked the class imbalance problem in Twitter spam detection. In this paper, we have studied the class imbalance problem in Twitter spam detection. Firstly, we have conducted a comparative study regarding some popular methods in handling the class imbalance problem in order to identify the most effective approach for addressing the class imbalance problem. Then, we have conducted another comparative study from Twitter spam detection based on several classic techniques. Experimental results demonstrate that a fuzy-based ensemble learning can significantly improve the classification performance on imbalance ground truth Twitter data. KEYWORDSclassification, class imbalance, online social network, Twitter spam detection INTRODUCTIONTwitter is used to exchange messages among friends. Unfortunately, spammers usually use Twitter as a tool to post unsolicited messages that contain malicious links, and even hijack trending topics. In this respect, the exponential growth of Twitter contributes to the increase of online spamming activities. Study shows that more than 3% messages are most probably abused by spammers. 1 In order to solve the security threats caused by spammers, a lot of researchers have proposed machine learning based algorithm for Twitter spam detection. However, most of these studies have neglected a fundamental issue that is the class imbalance problem, which is widely seen in real-world Twitter data. [2][3][4] The class imbalance problem has been identified as one of the ten challenging problems in data mining research. 5 This issue occurs in two different types of data sets: binary and multiclass. For binary problem, the training data from the minority class or positive class are very small, and the rest which make up the majority class or negative class are very large. While for multiclass problems, each of the class only contains a tiny fraction of samples. These problems are also especially critical in many real-world applications. For example, in Twitter spam detection, we used to have a large amount of normal Twitter data while only small number of spam samples, this gives us imbalanced data. Previous study has shown that the detection rate for Twitter spam can be decreased for about 33% in average with the class imbalance rate rises from 2 to 20. 6 Hence, a natural question in data mining research is how to improve the performance of classifiers facing with imbalanced data?Existing techniques for handling the class imbalance problem are mainly from three perspectives, includi...

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Section: Fuzzy-based Ensemble Resamplingmentioning

confidence: 99%

Section: Fuzzy-based Ensemble Resamplingmentioning

confidence: 99%

Section: Machine Learning Based Twitter Spam Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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A comparative study of the class imbalance problem in Twitter spam detection

Liu

2017

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A hybrid classification method for Twitter spam detection based on differential evolution and random forest

Abkenar

Mahdipour

Jameii

et al. 2021

Concurrency and Computation

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Summary Social networking services are online platforms that are distributed across different computers over long distances. Twitter is the most popular microblogging site that allows users to share their opinions and real‐world events. Due to its popularity and ease of use, Twitter has also attracted spammers. As a result, spam detection is one of the most critical problems. In order to provide a spam‐free environment, it is necessary to identify and filter spam tweets as well as their owners. A hybrid method, which is based on Synthetic Minority Over‐sampling TEchnique (SMOTE) and Differential Evolution (DE) strategies, is presented to enhance the spam detection rate in real Twitter datasets. SMOTE is applied to tackle the imbalanced class distribution of datasets, while DE is used to tune Random Forest (RF) hyperparameters. Compared with related work and based on evaluation results, the presented method significantly enhances the classification performance in imbalanced datasets. The detection rate of optimized RF with excellent F1‐score and Area Under the Receiver Operating Characteristic Curve (AUROC), which are 98.97% and 0.999, respectively, demonstrates the high efficiency of the proposed method.

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Fake User Account Detection in Online Social Media Networks Using Machine Learning and Neural Network Techniques

Hammoodi,

Obaid

2023

Intelligent Systems Reference Library

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Addressing the class imbalance problem in Twitter spam detection using ensemble learning

Cited by 97 publications

References 8 publications

A comparative study of the class imbalance problem in Twitter spam detection

A comparative study of the class imbalance problem in Twitter spam detection

A hybrid classification method for Twitter spam detection based on differential evolution and random forest

Fake User Account Detection in Online Social Media Networks Using Machine Learning and Neural Network Techniques

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