Social Network Mining for Predicting Users’ Credibility with Optimal Feature Selection

Jayashree, P.; Laila, K.; Kumar, K. Santhosh; Udayavannan, A.

doi:10.1007/978-981-16-2422-3_29

Cited by 6 publications

(3 citation statements)

References 19 publications

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“…Working with this multitude of features in data analysis can be a complex task, but it can be streamlined via reducing the dataset's dimensionality and pinpointing the most relevant features for accurate classification [41]. Several studies [7,10,13,[41][42][43]50,51,[55][56][57][58][59][60]] have adopted various feature selection methods to concentrate on the most pertinent and significant features for prediction, concurrently minimizing computational complexity. Among the methods employed in these studies are:…”

Section: Feature Selectionmentioning

confidence: 99%

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The Effect of Feature Selection on the Accuracy of X-Platform User Credibility Detection with Supervised Machine Learning

Abid-Althaqafi,

Alsalamah

2024

Electronics

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In the era of digital information, online platforms play a crucial role in shaping public opinion. However, the extensive spread of misinformation and fake news poses a significant challenge, largely fueled by non-credible users. Detecting user credibility is vital for ensuring the reliability of information on these platforms. This study employs supervised machine learning algorithms, leveraging key user features to enhance credibility detection. Feature selection methods, specifically SelectKBest and correlation-based algorithms, are explored for their impact on X-Platform user credibility detection. Utilizing various classifiers, including support vector machine, logistic regression, and XGBoost, experiments are conducted on the ArPFN dataset, which is a labeled, balanced, publicly available dataset. The evaluation includes measures like accuracy, precision, recall, and F1-score to assess efficiency. This research considers feature categories and selection methods with SML to detect their impact on the accuracy of X-Platform user credibility detection, making this research a reference for researchers and practitioners working in the field of SML, feature engineering, and social media analysis. We aim to advance the field’s understanding of effective strategies for mitigating the spread of fake news. The novelty of this study lies in the comprehensive exploration of feature selection methods and their influence on credibility detection, contributing valuable insights for future research in this domain.

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Section: Feature Selectionmentioning

confidence: 99%

“…Recursive feature elimination (RFE): The study in [59] employed RFE to evaluate the optimal features that improve classification accuracy in detecting spammers on X-Platform. The top 10 features were selected from a larger set including 31 features, resulting in improved accuracy.…”

mentioning

confidence: 99%

The Effect of Feature Selection on the Accuracy of X-Platform User Credibility Detection with Supervised Machine Learning

Abid-Althaqafi,

Alsalamah

2024

Electronics

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“…Authors of [53] addressed spammer detection with a hybrid approach combining logistic regression and principal component analysis (LR-PCA) for dimensional reduction, claiming increased classification accuracy. On the other hand, [73] used recursive feature elimination (RFE) to evaluate optimal features for improved spam detection accuracy, selecting the top 10 features from 31. Whilst [54] examined the best features identified by the random forest algorithm, achieving over 90% accuracy in detecting online bots on Twitter.…”

Section: Literature Reviewmentioning

confidence: 99%

Detecting User Credibility on Twitter using a Hybrid Machine Learning Model of Features’ Selection and Weighting

Abid-Althaqafi,

Alsalamah

2024

ijacsa

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With the pervasive and rapidly growing presence of the internet and social media, creating untrustworthy accounts has become effortless, allowing fake news to be spread for personal or private interests. As a result, it is crucial in this era to investigate the credibility of users on social networking platforms such as Twitter. In this research, we aim to integrate existing solutions from previous research to create a hybrid model. Our approach is based on selecting and weighting features using supervised machine learning methods such as ExtraTressClarifier, correlation-based algorithm methods, and SelectKBest to extract new ranked and weighted features in the dataset and then use them to train our model to discover their impact on the accuracy of user credibility detection issues. The research objective is to combine feature selection and weighting methods with Supervised Machine Learning to evaluate their impact on the accuracy of user credibility detection on Twitter. In addition, we measure the effectiveness of different feature categories on this detection. Experiments are conducted on one of the online available datasets. We seek to employ extracted features from a user's profile and statistical and emotional information. Then, the experimental results are compared to discover the effectiveness of the proposed solution. This study focuses on revealing the credibility of Twitter (or X-platform as recently renamed) accounts, which may result in the need for some adjustments to the generalization of Twitter's outputs to other social media accounts such as LinkedIn, Facebook, and others.

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Bi-Modal Meta-Classification of Tweet Spamicity Using Machine Learning Approach

Jayashree¹,

Laila²,

Vinuvarsidh³

et al. 2022

Futuristic Trends in Networks and Computing Technologies

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Social Network Mining for Predicting Users’ Credibility with Optimal Feature Selection

Cited by 6 publications

References 19 publications

The Effect of Feature Selection on the Accuracy of X-Platform User Credibility Detection with Supervised Machine Learning

The Effect of Feature Selection on the Accuracy of X-Platform User Credibility Detection with Supervised Machine Learning

Detecting User Credibility on Twitter using a Hybrid Machine Learning Model of Features’ Selection and Weighting

Bi-Modal Meta-Classification of Tweet Spamicity Using Machine Learning Approach

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