Contrast Pattern-Based Classification for Bot Detection on Twitter

Loyola‐González, Octavio; Monroy, Raúl; Rodríguez, Jorge; López-Cuevas, Armando; Mata-Sánchez, Javier Israel

doi:10.1109/access.2019.2904220

Cited by 67 publications

(58 citation statements)

References 64 publications

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“…Moreover, Van Der Walt and Eloff [20] linked engineered features such as the ''friendto-followers ratio'' to a set of fake human accounts in the hope of advancing the successful detection of fake identities created by humans on social media platforms. Loyola-González et al [21] used a pattern-based classification mechanism for social bot detection specifically for Twitter and introduced a new feature model for social bot detection that partially extended an existing model with features based on Twitter account usage and a tweet content sentiment analysis.…”

Section: Related Work a Social Bot Detection Based On Feature Exmentioning

confidence: 99%

Using Improved Conditional Generative Adversarial Networks to Detect Social Bots on Twitter

Liu

Yang

et al. 2020

IEEE Access

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The detection and removal of malicious social bots in social networks has become an area of interest in industry and academia. The widely used bot detection method based on machine learning leads to an imbalance in the number of samples in different categories. Classifier bias leads to a low detection rate of minority samples. Therefore, we propose an improved conditional generative adversarial network (improved CGAN) to extend imbalanced data sets before applying training classifiers to improve the detection accuracy of social bots. To generate an auxiliary condition, we propose a modified clustering algorithm, namely, the Gaussian kernel density peak clustering algorithm (GKDPCA), which avoids the generation of data-augmentation noise and eliminates imbalances between and within social bot class distributions. Furthermore, we improve the CGAN convergence judgment condition by introducing the Wasserstein distance with a gradient penalty, which addresses the model collapse and gradient disappearance in the traditional CGAN. Three common oversampling algorithms are compared in experiments. The effects of the imbalance degree and the expansion ratio of the original data on oversampling are studied, and the improved CGAN performs better than the others. Experimental results comparing with three common oversampling algorithms show that the improved CGAN achieves the higher evaluation scores in terms of F1-score, G-mean and AUC. INDEX TERMS Social bot detection, conditional generative adversarial networks, data augmentation, supervised classification, imbalanced data.

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Section: Related Work a Social Bot Detection Based On Feature Exmentioning

confidence: 99%

Using Improved Conditional Generative Adversarial Networks to Detect Social Bots on Twitter

Liu

Yang

et al. 2020

IEEE Access

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“…Later, the authors of [129] extract CPs from tweets text to describe the behavior of legitimate and automated bot accounts. The authors used three different algorithms for mining contrast patterns by using the option of filtering.…”

Section: Classificationmentioning

confidence: 99%

“…The authors showed that their proposal of using the Random Forest miner jointly with the classifiers PBC4cip [127] obtains significantly better classification results than ten popular state-of-the-art classifiers. The main difference of this work with the proposed by [122] is that [129] proposed a new feature representation based on the frequency of the issued tweets.…”

Section: Classificationmentioning

confidence: 99%

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A Review of Supervised Classification based on Contrast Patterns: Applications, Trends, and Challenges

2020

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Supervised classification based on Contrast Patterns (CP) is a trending topic in the pattern recognition literature, partly because it contains an important family of both understandable and accurate classifiers. In this paper, we survey 105 articles and provide an in-depth review of CP-based supervised classification and its applications. Based on our review, we present a taxonomy of the existing application domains of CP-based supervised classification, and a scientometric study. We also discuss potential future research opportunities.

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“…In recent years, online social networks (OSNs) have been seriously impacted by social bots [14]. A social botnet is a group of social bots created and controlled by a botmaster (acting as a leader among social bots) and performs malicious activities, such as creating multiple fake accounts, spreading spam, manipulating online ratings, and so on [13], [17], [28].…”

Section: Introductionmentioning

confidence: 99%

Social Botnet Community Detection: A Novel Approach based on Behavioral Similarity in Twitter Network using Deep Learning

Lingam

Rout

Somayajulu

et al. 2020

Proceedings of the 15th ACM Asia Conference on Computer and Communications Security

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Detecting social bots and identifying social botnet communities are extremely important in online social networks (OSNs). In this paper, we first construct a weighted signed Twitter network graph based on the behavioral similarity and trust values between the participants (i.e., OSN accounts) as weighted edges. The behavioral similarity is analyzed from the viewpoints of tweet-content similarity, shared URL similarity, interest similarity, and social interaction similarity for identifying similar types of behavior (malicious or not) among the participants in the Twitter network; whereas the participant's trust value is determined by a random walk model. Next, we design two algorithms-Social Botnet Community Detection (SBCD) and Deep Autoencoder based SBCD (called DA-SBCD)-where the former detects social botnet communities of social bots with malicious behavioral similarity, while the latter reconstructs and detects social botnet communities more accurately in presence of different types of malicious activities. Finally, we evaluate the performance of proposed algorithms with the help of two Twitter datasets. Experimental results demonstrate the efficacy of our algorithms with better performance than existing schemes in terms of normalized mutual information (NMI), precision, recall and Fmeasure. More precisely, the DA-SBCD algorithm achieves about 90% precision and exhibits up to 8% improvement on NMI.

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Contrast Pattern-Based Classification for Bot Detection on Twitter

Cited by 67 publications

References 64 publications

Using Improved Conditional Generative Adversarial Networks to Detect Social Bots on Twitter

Using Improved Conditional Generative Adversarial Networks to Detect Social Bots on Twitter

A Review of Supervised Classification based on Contrast Patterns: Applications, Trends, and Challenges

Social Botnet Community Detection: A Novel Approach based on Behavioral Similarity in Twitter Network using Deep Learning

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