A robust technique of fake news detection using Ensemble Voting Classifier and comparison with other classifiers

Mahabub, Atik

doi:10.1007/s42452-020-2326-y

Cited by 57 publications

(27 citation statements)

References 27 publications

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“…Mahabub [31] used a distinct method for detecting fake news in developing an ensemble voting classifier that incorporates many familiar machine learning algorithms. Kaur et al [32] designed a voting model with multiple levels in automating the detection of fake news by experimenting with several models.…”

Section: Related Workmentioning

confidence: 99%

Detection of Online Fake News Using Blending Ensemble Learning

Hansrajh

Adeliyi

Wing

2021

Scientific Programming

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The exponential growth in fake news and its inherent threat to democracy, public trust, and justice has escalated the necessity for fake news detection and mitigation. Detecting fake news is a complex challenge as it is intentionally written to mislead and hoodwink. Humans are not good at identifying fake news. The detection of fake news by humans is reported to be at a rate of 54% and an additional 4% is reported in the literature as being speculative. The significance of fighting fake news is exemplified during the present pandemic. Consequently, social networks are ramping up the usage of detection tools and educating the public in recognising fake news. In the literature, it was observed that several machine learning algorithms have been applied to the detection of fake news with limited and mixed success. However, several advanced machine learning models are not being applied, although recent studies are demonstrating the efﬁcacy of the ensemble machine learning approach; hence, the purpose of this study is to assist in the automated detection of fake news. An ensemble approach is adopted to help resolve the identified gap. This study proposed a blended machine learning ensemble model developed from logistic regression, support vector machine, linear discriminant analysis, stochastic gradient descent, and ridge regression, which is then used on a publicly available dataset to predict if a news report is true or not. The proposed model will be appraised with the popular classical machine learning models, while performance metrics such as AUC, ROC, recall, accuracy, precision, and f1-score will be used to measure the performance of the proposed model. Results presented showed that the proposed model outperformed other popular classical machine learning models.

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Section: Related Workmentioning

confidence: 99%

Detection of Online Fake News Using Blending Ensemble Learning

Hansrajh

Adeliyi

Wing

2021

Scientific Programming

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“…Later, an ensemble voting classifier-based approach was proposed in [139], where the authors developed an intelligent system that classifies the news as fake or real. They have compared eleven Novel ML algorithms like NB, KNN, SVM, RF, ANN, LR, Ada Boosting, and some others to detect fake news (incorporated best three based on results).…”

Section: E Ensemble Learningmentioning

confidence: 99%

A Taxonomy of Fake News Classification Techniques: Survey and Implementation Aspects

et al. 2022

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In the present era, social media platforms such as Facebook, WhatsApp, Twitter, and Telegram are significant sources of information distribution, and people believe it without knowing their origin and genuineness. Social media has fascinated people worldwide in spreading fake news due to its easy availability, cost-effectiveness, and ease of information sharing. Fake news can be generated to mislead the community for personal or commercial gains. It can also be used for other personal benefits such as defaming eminent personalities, amendment of government policies, etc. Thus, to mitigate the awful consequences of fake news, several research types have been conducted for its detection with high accuracy to prevent its fatal outcome. Motivated by the aforementioned concerns, we present a comprehensive survey of the existing fake news identification techniques in this paper. Then, we select Machine Learning (ML) models such as Long-Short Term Memory (LSTM), Passive Aggressive Algorithm, Random Forest (RF), and Naive Bayes (NB) and train them to detect fake news articles on the self-aggregated dataset. Later, we implemented these models by hyper tuning various parameters such as smoothing, drop out factor, and batch size, which has shown promising results in accuracy and other evaluation metrics such as F1-score, recall, precision, and AUC score. The model is trained on 6335 news articles, with LSTM showing the highest accuracy of 92.34% in predicting fake news and NB were showing the highest recall. Based on these results, we propose a hybrid fake news detection technique using NB and LSTM. At last, challenges and open issues along with future research directions are discussed to facilitate the research in this domain further.

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“…The authors have put forward an ensemble soft voting classifier that gives binary classification and uses a combination of three machine learning algorithms, i.e., Logistic Regression, Random Forest, and Gaussian Naive Bayes for the classification. Mahabub (2020) has proposed an ensemble voting classifier based on a sharp detection system to categorise real and fake news. The authors have tried and categorically analyzed at least eleven notable ML classification algorithms.…”

Section: Related Workmentioning

confidence: 99%

Performance evaluation of machine learning based voting classifier system for human activity recognition

Jindal

Sachdeva

Kushwaha

2022

KJS publishes peer-review articles in Mathematics, Computer Sci

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In the last few decades, Human Activity Recognition (HAR) has been a centre of attraction in many research domains, and it is referred to as the potential of interpreting human body gestures through sensors and ascertaining the activity of a human being. The present work has proposed the voting classifier system for human activity recognition. For the voting classifier system, five machine learning classifiers are considered: Logistic Regression (LR), K-Nearest Neighbour (KNN), Random Forest (RF), Naive Bayes (NB), and Support Vector Machine (SVM). These machine learning classifiers are ensembled by analyzing the best performers among them. The ensemble voting classifiers are proposed under two variations, i.e., hard voting and soft voting. The various combinations of voting classifiers are compared and evaluated. For experiments, the benchmark dataset of the UCI-HAR dataset is considered, and all the data files are combined into a single file to avoid bias. The dimensionality of the dataset is reduced by using Principal Component Analysis (PCA) from 561 features to 200 components. The results reveal that Voting Classifier-II (a combination of SVM, KNN, and LR) using soft voting outperformed other machine learning classifiers.

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A robust technique of fake news detection using Ensemble Voting Classifier and comparison with other classifiers

Cited by 57 publications

References 27 publications

Detection of Online Fake News Using Blending Ensemble Learning

Detection of Online Fake News Using Blending Ensemble Learning

A Taxonomy of Fake News Classification Techniques: Survey and Implementation Aspects

Performance evaluation of machine learning based voting classifier system for human activity recognition

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