Intelligent Twitter Spam Detection: A Hybrid Approach

Vishwarupe, Varad; Bedekar, Mangesh; Pande, Milind; Hiwale, Anil

doi:10.1007/978-981-10-6916-1_17

Cited by 21 publications

(5 citation statements)

References 9 publications

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“…Vishwarupe et al [38] developed an intelligent system for spam detection: it provided a description about spam profiles after the detection process had completed. This system was tested on twitter OSN.…”

Section: Introductionmentioning

confidence: 99%

Spam profiles detection on social networks using computational intelligence methods: The effect of the lingual context

Al-Zoubi

Alqatawna

Faris

et al. 2019

Journal of Information Science

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In online social networks, spam profiles represent one of the most serious security threats over the Internet; if they do not stop producing bad advertisements, they can be exploited by criminals for various purposes. This article addresses the nature and the characteristics of spam profiles in a social network like Twitter to improve spam detection, based on a number of publicly available language-independent features. In order to investigate the effectiveness of these features in spam detection, four datasets are extracted for four different language contexts (i.e. Arabic, English, Korean and Spanish), and a fifth is formed by combining them all. We conduct our experiments using a set of five well-known classification algorithms in spam detection field, k-Nearest Neighbours ( k-NN), Random Forest (RF), Naive Bayes (NB), Decision Tree (DT) (J48) and Multilayer Perceptron (MLP) classifiers, along with five filter-based feature selection methods, namely, Information Gain, Chi-square, ReliefF, Correlation and Significance. The results show oscillating performance of each classifier across all datasets, but improved classification results with feature selection. In addition, detailed analysis and comparisons are carried out on two different levels: in the first level, we compare the selected features’ importance among the feature selection methods, whereas in the second level, we observe the relations and the importance of the selected features across all datasets. The findings of this article lead to a better understanding of social spam and improving detection methods by considering the various important features resulting from the different lingual contexts.

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Section: Introductionmentioning

confidence: 99%

Spam profiles detection on social networks using computational intelligence methods: The effect of the lingual context

Al-Zoubi

Alqatawna

Faris

et al. 2019

Journal of Information Science

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show abstract

“…Vishwarupe et al 26 introduced an Intelligent Twitter spam detection system that investigated spam accounts precisely by diagnosing Twitter spam. This hybrid method applied a set of unique features, Twitter 4j API, and Google Safe Browsing API.…”

Section: Background and Related Workmentioning

confidence: 99%

“…In literature works, one work, 21 has investigated ROC (AUC) similarly to our paper to identify the models' predictive ability in discriminating between spam and non‐spam tweets. Most papers 1,10,15,17,20,22,23,25–27 have evaluated a few number of parameters, while very few papers have examined imbalanced datasets, or in case of studying imbalanced data, the spam detection rate in their methods is still very low 10,12,17,21 . To best of our knowledge, no paper employs evolutionary algorithms for hyperparameters to enhance the detection rate; one work 30 has combined WOA with SSA to select an optimum subset of features, and one work 17 has applied DT, PSO, and genetic algorithms, no paper has used DE algorithm to improve the spam detection rate in Twitter dataset.…”

Section: Background and Related Workmentioning

confidence: 99%

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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“…Liu et al [31] employed user behavior information, online social network attributes, and text content characteristics as features for spammer detection. Vishwarupe et al [32] used the type of account as a feature, i.e., they checked whether the account was verified.…”

Section: A Spammer Detection Approachesmentioning

confidence: 99%

A Spammer Identification Method for Class Imbalanced Weibo Datasets

2019

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Nowadays, Weibo has become a significant and popular information sharing platform in China. Meanwhile, spammer identification has been a big challenge for it. To mitigate the damage caused by spammers, classification algorithms from machine learning have been applied to distinguish spammers and non-spammers. However, most of the previous studies overlook the class imbalance problem of real-world data. In this paper, by analyzing the characteristics of spammers in Weibo, we select microblog content similarity, the average number of links, and the other 12 features to construct a comprehensive feature vector never seen before. Considering the existence of imbalance problems in spammer identification, an ensemble learning method is used to combine multiple base classifiers for improving the learning performance. During the training stage of base learners, fuzzy-logic-based oversampling and cost-sensitive support vector machine are considered to tackle imbalanced data at both data and algorithmic levels. The experimental results demonstrate that compared with the existing state-of-the-art methods, the recall rate of our proposed approach increases by 6.5% and reaches the precision value of 87.53% when used to deal with real-world Weibo datasets we collected.

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Intelligent Twitter Spam Detection: A Hybrid Approach

Cited by 21 publications

References 9 publications

Spam profiles detection on social networks using computational intelligence methods: The effect of the lingual context

Spam profiles detection on social networks using computational intelligence methods: The effect of the lingual context

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

A Spammer Identification Method for Class Imbalanced Weibo Datasets

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