A cooperative deep learning model for fake news detection in online social networks

Mallick, Chandrakant; Mishra, Sarojananda; Senapati, Manas Ranjan

doi:10.1007/s12652-023-04562-4

Cited by 8 publications

(4 citation statements)

References 33 publications

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“…Mallick et al [24] introduced a cooperative deep learning-based fake news detection model which evaluates the trust level of the news and ranks the news based on its values. CNN was utilized in ranking news by using deep learning layers.…”

Section: Related Workmentioning

confidence: 99%

An Improved Fake News Detection Model Using Hybrid Time Frequency-Inverse Document Frequency for Feature Extraction and AdaBoost Ensemble Model as a Classifier

Holla,

Kavitha

2024

JAIT

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Fake news information on the internet has recently emerged as one of the challenging concerns that can have an impact on society and individuals. Furthermore, the propagation of fake news on social media increases the risk of loss of trustworthiness and disseminates fake information across multiple online platforms. As a result, recognizing fake news on the internet plays an important role in society and among individuals. Knowing this, this study proposed an improved fake news detection model that uses the proposed Hybrid Time-Frequency-Inverse Document Frequency (TF-IDF) to extract features and the Adaptive boosting ensemble classifier, which is a combination of Iterative Dichotomiser 3 (ID-3), Random Forest (RF), and Nave Bayes (NB) classifiers. The characteristics are chosen using the Least Absolute Shrinkage and Selection Operator (LASSO), and the news is classified as fake or true using the Adaptive Boosting (AdaBoost) ensemble classifier. The obtained results show that TF-IDF with AdaBoost ensemble classifier has a higher classification accuracy of 98.98% than the existing N-Gram with TF-IDF and Bidirectional Encoder Representation from Transformer (BERT) and Word2Vec with Convolutional Neural Network-Bidirectional Long Short Term Memory (CNN-Bi LSTM) with 96.81% and 97.74%, respectively.

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Section: Related Workmentioning

confidence: 99%

An Improved Fake News Detection Model Using Hybrid Time Frequency-Inverse Document Frequency for Feature Extraction and AdaBoost Ensemble Model as a Classifier

Holla,

Kavitha

2024

JAIT

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“…Al Obaid et al [24] employed a group of deep learners to identify fake news. Mallick et al [25] introduced a collaborative deep learning model for FND that estimates the level of trust in news based on user feedback and ranks them accordingly. Chenguang et al [26] used CNN for textual features and the Cross-modal Attention Residual Network (CARN) for visual features.…”

Section: Related Workmentioning

confidence: 99%

Combating Fake News on Social Media: A Fusion Approach for Improved Detection and Interpretability

Zamil,

Charkari

2024

IEEE Access

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The proliferation of fake news on social media prompted research groups to develop statistical and learning methods to combat this menace. Deep learning techniques did not model and improve in terms of adopting multi-transformer topologies, enhancing interpretability, and coping with uncertainty. This article suggests a fusion strategy to create a more reliable fake news detection (FND) model by fusing text and image features. The different combinations of information in single and multi-modalities have been investigated to find optimal conditions. In this paper, we have employed pre-trained models of Electra and XLnet for text feature learning. Furthermore, ELA has been used to highlight the modified image features and EfficientNetB0 for image learning. To enhance the interpretability of the proposed model, the superpixels contributing to its interpretability were identified using the Local Interpretable Model-agnostic Explanations (LIME). Three well-known datasets (Weibo, MediaEval, and CASIA) have been used in this study. The results show that employing ELA and LIME in conjunction with the fusion of text and image features provides a solid and understandable solution to the FND issue in social media compared to other techniques.INDEX TERMS Social media news, fake news detection, Error Level Analysis, efficientNetB0, LIME.

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“…They analyzed the methods, datasets, and evaluation metrics used in these studies and identified key research gaps and future directions [8]. Kumar and Pandey (2021) conducted a comparative study of machine learning algorithms for fake news detection, including decision tree, random forest, k-nearest neighbors, and support vector machines. They evaluated the models on a dataset of news articles and found that the random forest algorithm performed the best in terms of accuracy and F1 score [3].…”

Section: IImentioning

confidence: 99%

“…Kumar and Pandey (2021) conducted a comparative study of machine learning algorithms for fake news detection, including decision tree, random forest, k-nearest neighbors, and support vector machines. They evaluated the models on a dataset of news articles and found that the random forest algorithm performed the best in terms of accuracy and F1 score [3]. Anand and Taneja (2021) conducted a systematic literature review of studies on fake news detection using machine learning.…”

Section: IImentioning

confidence: 99%

Evaluation of Deep Learning Models for Fake News Detection

Anil¹

2023

IJCIR

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The widespread dissemination of misinformation, commonly known as fake news, has been facilitated by the rapid expansion of social networks as platforms for news distribution. This stands in contrast to traditional mass media channels such as newspapers, magazines, radio, and television. The challenges arise from human limitations in discerning between true and false information, which poses a significant threat to logical coherence, democratic processes, journalistic integrity, and the credibility of government institutions. The lack of reliable and trustworthy information on social media further compounds the issues associated with this phenomenon. To address this pressing problem, we have proposed an integrated system that incorporates various pre-processing techniques and classification models. This system aims to detect and combat fake news by evaluating their efficacy on a specific dataset of labeled news statements. By utilizing metrics such as precision, F1 scores, and recall, we can determine the most effective model. The primary objective of this system is to develop an efficient and accurate model capable of predicting and identifying instances of fake news within social media networks.

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A cooperative deep learning model for fake news detection in online social networks

Cited by 8 publications

References 33 publications

An Improved Fake News Detection Model Using Hybrid Time Frequency-Inverse Document Frequency for Feature Extraction and AdaBoost Ensemble Model as a Classifier

An Improved Fake News Detection Model Using Hybrid Time Frequency-Inverse Document Frequency for Feature Extraction and AdaBoost Ensemble Model as a Classifier

Combating Fake News on Social Media: A Fusion Approach for Improved Detection and Interpretability

Evaluation of Deep Learning Models for Fake News Detection

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