Evaluating Machine Learning Algorithms For Bengali Fake News Detection

Mugdha, Shafayat Bin Shabbir; Ferdous, Sayeda Muntaha; Fahmin, Ahmed

doi:10.1109/iccit51783.2020.9392662

Cited by 24 publications

(6 citation statements)

References 11 publications

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“…Two feature extraction methods and six supervised ML classification methods were also compared in this study. The combination of the TF-IDF feature extraction method and the Linear Support Vector Machine (LSVM) classifier achieves the highest accuracy of 92%.The authors of the study [31] identify bogus news using a Gaussian Naïve Bayes classifier. They collected data from Facebook and various other sources to implement their methodology and achieved 87% accuracy.…”

Section: Role Of Datasetsmentioning

confidence: 99%

Fake News Detection Datasets: A Review and Research Opportunities

Dhiman,

Kaur,

Hamid

et al. 2024

IJCDS

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The impact of fake news is far-reaching, affecting journalism, the economy, and democracy. In response, there has been a surge in research focused on detecting and combating fake news, resulting in the development of datasets, techniques, and fact-verification methods. One crucial aspect of this effort is the creation of diverse and representative datasets for training and evaluating machine learning models for fake news detection. This review paper examines the available datasets relevant to detecting fake news, with a particular emphasis on those available in the Indian context, where few resources exist. By identifying research opportunities and highlighting existing corpora, this paper aims to assist researchers in improving their fake news detection studies and contributing to more comprehensive research on the topic. To the best of our knowledge, no survey has specifically focused on accessible corpora in the Indian context, making this review a valuable resource for researchers in the field.

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Section: Role Of Datasetsmentioning

confidence: 99%

Fake News Detection Datasets: A Review and Research Opportunities

Dhiman,

Kaur,

Hamid

et al. 2024

IJCDS

View full text Add to dashboard Cite

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“…IDF has a process that gives less weight to words that appear more often in a group of text and more weight to words that appear less often. As in the equation (2).…”

Section: Feature Extractionmentioning

confidence: 99%

“…Fake news hurt people's minds, which makes them read less trustworthy news. This kind of false news is spread by people who want to make money from the number of views on their page or by giving a biased opinion to trick people into making a different choice, like during an election [2]. Fake news spreads faster than real news because it is more interesting to the viewer.…”

Section: Introductionmentioning

confidence: 99%

The Detection of Fake Text News using a Dense-based 1D-CNN Deep Learning Algorithm

2024

Acad. Sci. J.

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There are a lot of problems with fake news, which can make people think of things that ‎aren't true. Social media is one of the fastest ways to get information out there because it has ‎a big impact and can manipulate information in both good and bad ways. The goal of this ‎paper is the optimal use of deep learning algorithms to solve the problem of the paper. The research problem is how ‎accurately and to what extent can an individual distinguish between fake news articles using ‎natural language processing and classification algorithms. What are the steps that can be ‎taken to provide a solution?‎‎compared to the previous different methods to solve this problem, including some common ‎deep-learning methods. In this paper, we can find fake news can be found by using the term ‎inverse frequency document (TF-IDF) for feature extraction and a hybrid algorithm of One ‎Dimensional-Convolutional Neural Network (1D-CNN) and Dense as the classifier. The ‎experiments that the proposed dense-based 1D-CNN algorithm substantially outperforms ‎other up-to-date related algorithms with an accuracy of 100%.

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“…Other techniques using machine learning are widely implemented to detect, classify, and predict misinformation. A model created by [86] proposed a stochastic gradient descent technique with 87% accuracy. Moreover, another paper by [87] used 23 machine learning techniques such as BayesNet, JRip, OneR, decision stump, ZeroR, stochastic gradient descent (SGD), CV parameter selection (CVPS), randomizable filtered classifier (RFC), logistic model tree (LMT), locally weighted learning (LWL), classification via clustering (CvC), weighted instances handler wrapper (WIHW), ridor, multi-layer perceptron (MLP), ordinal learning model (OLM), simple cart, attribute selected classifier (ASC), J48, sequential minimal optimization (SMO), bagging, decision tree, IBk, and kernel logistic regression (KLR) to detect fake news in social media.…”

Section: Machine Learningmentioning

confidence: 99%

Classification of Covid-19 misinformation on social media based on neuro-fuzzy and neural network: A systematic review

Ravichandran

Keikhosrokiani

2022

Neural Comput & Applic

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The spread of Covid-19 misinformation on social media had significant real-world consequences, and it raised fears among internet users since the pandemic has begun. Researchers from all over the world have shown an interest in developing deception classification methods to reduce the issue. Despite numerous obstacles that can thwart the efforts, the researchers aim to create an automated, stable, accurate, and effective mechanism for misinformation classification. In this paper, a systematic literature review is conducted to analyse the state-of-the-art related to the classification of misinformation on social media. IEEE Xplore, SpringerLink, ScienceDirect, Scopus, Taylor & Francis, Wiley, Google Scholar are used as databases to find relevant papers since 2018–2021. Firstly, the study begins by reviewing the history of the issues surrounding Covid-19 misinformation and its effects on social media users. Secondly, various neuro-fuzzy and neural network classification methods are identified. Thirdly, the strength, limitations, and challenges of neuro-fuzzy and neural network approaches are verified for the classification misinformation specially in case of Covid-19. Finally, the most efficient hybrid method of neuro-fuzzy and neural networks in terms of performance accuracy is discovered. This study is wrapped up by suggesting a hybrid ANFIS-DNN model for improving Covid-19 misinformation classification. The results of this study can be served as a roadmap for future research on misinformation classification.

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Evaluating Machine Learning Algorithms For Bengali Fake News Detection

Cited by 24 publications

References 11 publications

Fake News Detection Datasets: A Review and Research Opportunities

Fake News Detection Datasets: A Review and Research Opportunities

The Detection of Fake Text News using a Dense-based 1D-CNN Deep Learning Algorithm

Classification of Covid-19 misinformation on social media based on neuro-fuzzy and neural network: A systematic review

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