Bengali Fake News Detection

Islam, Farzana; Alam, Mohammad Minhazul; Hossain, Shahadat; Motaleb, Abdul; Yeasmin, Sabrina; Hasan, Mehedi; Rahman, Rashedur M.

doi:10.1109/is48319.2020.9199931

Cited by 20 publications

(5 citation statements)

References 3 publications

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“…The other works that were conducted on Bangla fake news detection like [5] had achieved a 96.64% of accuracy for SVM on their own dataset consisted of around 2541 instances with real and fake being 60.92% and 39.08% respectively. In other work, [6] had achieved an accuracy of 85% for Random Forest Classifier working on their own dataset consisting of 726 news articles. A common phenomena is seen in these studies that their dataset is comparatively much smaller.…”

Section: Overall Performance Of All Methodsmentioning

confidence: 99%

Approaches for Improving the Performance of Fake News Detection in Bangla: Imbalance Handling and Model Stacking

Hossain¹,

Awosaf²,

Hossan³

et al. 2022

Preprint

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Imbalanced datasets can lead to biasness into the detection of fake news. In this work, we present several strategies for resolving the imbalance issue for fake news detection in Bangla with a comparative assessment of proposed methodologies. Additionally, we propose a technique for improving performance even when the dataset is imbalanced. We applied our proposed approaches to BanFakeNews [4], a dataset developed for the purpose of detecting fake news in Bangla comprising of 50K instances but is significantly skewed, with 97% of majority instances. We obtained a 93.1% F1-score using data manipulation manipulation techniques such as SMOTE, and a 79.1% F1-score using without data manipulation approaches such as Stacked Generalization. Without implementing these techniques, the F1-score would have been 67.6% for baseline models. We see this work as an important step towards paving the way of fake news detection in Bangla. By implementing these strategies the obstacles of imbalanced dataset can be removed and improvement in the performance can be achieved.

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Section: Overall Performance Of All Methodsmentioning

confidence: 99%

Approaches for Improving the Performance of Fake News Detection in Bangla: Imbalance Handling and Model Stacking

Hossain¹,

Awosaf²,

Hossan³

et al. 2022

Preprint

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show abstract

“…To combine the aforementioned data, a Bi-directional Long Short-Term Memory Condition Random Fields (Bi-LSTM-CRF) network based on attention is implemented as described in [13]. The random forest classifier has been used to identify whether the Bengali news is fake or real which is implemented in [14]. Transformer-based structures produce multimodal representations of an utterance.…”

Section: Related Workmentioning

confidence: 99%

Bengali Query Processing System for Disease Detection using LSTM and GRU

Mandal¹,

Mukherjee²,

Ganguly³

et al. 2023

IJCDS

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This paper proposes a disease detection system where it receives the query in form of symptoms of the disease in the Bengali language. This system is able to handle natural language queries in Bengali. The proposed system assists a layman to detect a probable disorder or disease in their body using disease symptoms. The proposed research work is challenging due to insufficient resources in vernacular languages like Bengali. This system receives a description of the patient's symptoms in the Bengali language and after processing the natural language text, it detects any potential disorders or diseases that may have occurred. This research work has been implemented separately by using the two most popular sequential prediction models. One is Bi-directional Long Short-Term Memory (Bi-LSTM) and the other is Bi-directional Gated Recurrent Unit (Bi-GRU). Both Bi-GRU and Bi-LSTM have provided significant results on a dataset of 3714 samples. The raw clinical text categorization data has been gathered from the Kaggle to build the detection model. The performances of disease detectability of both models have been measured using precision, recall and f1-score. The accuracy of the proposed system using the Bi-LSTM and the Bi-GRU models are 97.85% and 99.73%, respectively.

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“…They discussed models like MOSES and hybrid CNN-RNN models, both showcasing promising accuracies. Islam et al [24] focused on Bengali fake news categorization, understanding its significance in South Asia. Their web interface, backed by a classifier, could verify Bengali news articles.…”

Section: Classificationmentioning

confidence: 99%

Machine Learning Techniques for Identifying Fake News: An Overview

Saini,

Khatarkar

2023

IJOSCIENCE

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Fake news, which is defined as material that has been shared with the intention of defrauding people, has been growing quickly and widely recently. This kind of misinformation is dangerous to social cohesion and wellbeing because it exacerbates political polarisation and public mistrust of authority figures. As a result, false news is an issue that has a big impact on our social lives, especially in politics. To combat this issue, this study proposes innovative approaches grounded in machine learning (ML) and deep learning (DL) for enhancing fake news detection systems. This investigation encompasses a thorough examination of established machine learning algorithms such as Naïve Bayes, Convolutional Neural Networks, Long Short-Term Memory networks, Neural Networks, and Support Vector Machines. These algorithms are explored in the context of identifying and mitigating fake news across various social media platforms, including Facebook, WhatsApp, Twitter, and more. This review offers a comprehensive overview that includes perspectives from data mining, evaluation metrics, and representative datasets, contributing to a deeper understanding of the strategies employed to combat the proliferation of fake news

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Bengali Fake News Detection

Cited by 20 publications

References 3 publications

Approaches for Improving the Performance of Fake News Detection in Bangla: Imbalance Handling and Model Stacking

Approaches for Improving the Performance of Fake News Detection in Bangla: Imbalance Handling and Model Stacking

Bengali Query Processing System for Disease Detection using LSTM and GRU

Machine Learning Techniques for Identifying Fake News: An Overview

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