State of the Art on Twitter Spam Detection

Borse, Dipalee; Borse, Swati

doi:10.1007/978-981-19-2719-5_46

Cited by 2 publications

(3 citation statements)

References 38 publications

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“…Most recent advancements in natural language processing research introduced pre-trained word embedding models that are coupled with Deep Learning (DL) algorithms [14], [23], [82]. BERT word embedding is used with DL to build spam classification models.…”

Section: ) Comparison With Bert and Deep Learningmentioning

confidence: 99%

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Modified Genetic Algorithm for Feature Selection and Hyper Parameter Optimization: Case of XGBoost in Spam Prediction

2022

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Recently, spam on online social networks has attracted attention in the research and business world. Twitter has become the preferred medium to spread spam content. Many research efforts attempted to encounter social networks spam. Twitter brought extra challenges represented by the feature space size, and imbalanced data distributions. Usually, the related research works focus on part of these main challenges or produce black-box models. In this paper, we propose a modified genetic algorithm for simultaneous dimensionality reduction and hyper parameter optimization over imbalanced datasets. The algorithm initialized an eXtreme Gradient Boosting classifier and reduced the features space of tweets dataset; to generate a spam prediction model. The model is validated using a 50 times repeated 10-fold stratified cross-validation, and analyzed using nonparametric statistical tests. The resulted prediction model attains on average 82.32% and 92.67% in terms of geometric mean and accuracy respectively, utilizing less than 10% of the total feature space. The empirical results show that the modified genetic algorithm outperforms Chi 2 and P CA feature selection methods. In addition, eXtreme Gradient Boosting outperforms many machine learning algorithms, including BERT-based deep learning model, in spam prediction. Furthermore, the proposed approach is applied to SMS spam modeling and compared to related works.

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Section: ) Comparison With Bert and Deep Learningmentioning

confidence: 99%

“…One of the major issues in spam text research is the limited availability of labeled text datasets with high quality [13], [14]. For example the well known benchmark datasets are few, and many researchers use tools to collect domain specific datasets.…”

Section: Introductionmentioning

confidence: 99%

Modified Genetic Algorithm for Feature Selection and Hyper Parameter Optimization: Case of XGBoost in Spam Prediction

2022

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“…Our DSpamOnto, which includes concepts, relationships, attributes, and examples, was designed using the steps outlined above. A comprehensive investigation of various academic papers and corporate reports was conducted in order to extract the technical terminology required to build the ontology [1,4,[27][28][29][30]. Protégé is used to develop DSpa-mOnto.…”

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confidence: 99%

DSpamOnto: An Ontology Modelling for Domain-Specific Social Spammers in Microblogging

Al-Hassan,

Abu-Salih,

Al Hwaitat

2023

BDCC

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The lack of regulations and oversight on Online Social Networks (OSNs) has resulted in the rise of social spam, which is the dissemination of unsolicited and low-quality content that aims to deceive and manipulate users. Social spam can cause a range of negative consequences for individuals and businesses, such as the spread of malware, phishing scams, and reputational damage. While machine learning techniques can be used to detect social spammers by analysing patterns in data, they have limitations such as the potential for false positives and false negatives. In contrast, ontologies allow for the explicit modelling and representation of domain knowledge, which can be used to create a set of rules for identifying social spammers. However, the literature exposes a deficiency of ontologies that conceptualize domain-based social spam. This paper aims to address this gap by designing a domain-specific ontology called DSpamOnto to detect social spammers in microblogging that targes a specific domain. DSpamOnto can identify social spammers based on their domain-specific behaviour, such as posting repetitive or irrelevant content and using misleading information. The proposed model is compared and benchmarked against well-proven ML models using various evaluation metrics to verify and validate its utility in capturing social spammers.

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State of the Art on Twitter Spam Detection

Cited by 2 publications

References 38 publications

Modified Genetic Algorithm for Feature Selection and Hyper Parameter Optimization: Case of XGBoost in Spam Prediction

Modified Genetic Algorithm for Feature Selection and Hyper Parameter Optimization: Case of XGBoost in Spam Prediction

DSpamOnto: An Ontology Modelling for Domain-Specific Social Spammers in Microblogging

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