Survey of fake news detection using machine intelligence approach

Pal, Aishika; Pranav,; Pradhan, Moumita

doi:10.1016/j.datak.2022.102118

Cited by 13 publications

(3 citation statements)

References 11 publications

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“…The authors confirm that the data supporting the findings of this study are available within the article [6][7][8][9][10][11][12][13][14][15][16][17][18][19], and its supplementary materials.…”

Section: Authors Contributionssupporting

confidence: 84%

“…The study will evaluate the algorithms using a new dataset. An article discusses the impact of globalization and online platforms on information exchange, including the propagation of false news on social media [8]. The article proposes a machine learning-based approach to identify both fake and genuine news using the sklearn module, TF-IDF vectorizer, and different ML models such as Passive Aggressive Classifier, Naïve Bayes algorithm, Logistic Regression, Decision Tree, Long Short Term Memory (LSTM), and Bidirectional Encoder Representations from Transformers (BERT).…”

Section: Introductionmentioning

confidence: 99%

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An Innovative Approach for Fake News Detection using Machine Learning

Hisham

Hasan

Hussain

2023

SSURJET

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This research aims to increase people's awareness of fake news on online social networks and help them determine the reliability of information they consume. It investigates methods for detecting fake news sources, authors, and subjects on online social networks. The project uses an open-source online dataset of fake and real news to determine the credibility of news. Various text feature extraction techniques and classification algorithms are reviewed, with the Support Vector Machine (SVM) linear classification algorithm using TF-IDF feature extraction achieving the highest accuracy of 99.36%. Random Forest (RF) and Naive Bayes (NB) had accuracy scores of 98.25% and 94.74%, respectively.

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Section: Authors Contributionssupporting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

An Innovative Approach for Fake News Detection using Machine Learning

Hisham

Hasan

Hussain

2023

SSURJET

View full text Add to dashboard Cite

show abstract

“…For example, reducing misinformation using a computational social science approach can help establish bidirectional links between the information 'producers' and 'consumers' at a system level. Existing CSS applications show that misinformation and polarization can be detected using deep learning models 6 . Newer applications like OpenAI's ChatGPT, which aims to mimic human conversation built…”

Section: Figmentioning

confidence: 99%