Fake News Detection using Deep Recurrent Neural Networks

Jiang, Tao; Li, Jian Ping; Haq, Amin Ul; Saboor, Abdus

doi:10.1109/iccwamtip51612.2020.9317325

Cited by 17 publications

(9 citation statements)

References 13 publications

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“…Social media platforms have a plethora of misinformation, which has caught the attention of researchers in developing mechanisms to detect them (Jiang et al, 2021(Jiang et al, , 2020Birunda & Devi, 2021;Sahoo & Gupta, 2021;Goel et al, 2021;Pardamean & Pardede, 2021). The European Commission has established a group of experts to advise and discuss policy initiatives to combat fake news and the spread of disinformation online (Assad & Erascu, 2018).…”

Section: Fake News Detectionmentioning

confidence: 99%

“…The combination of models generating hybrid models is a recommendation highlighted by Jiang et al (2020), Pardameanm, and Pardede (2021), and Kaliyar, Goswamim and Narang (2021. Another recommendation was to use algorithms based on deep learning in future research and understand how this technique can help identify fake news (Bahad, Saxena & Kamal, 2020).…”

Section: Algorithm -Authormentioning

confidence: 99%

See 1 more Smart Citation

Fake news detection: a systematic literature review of machine learning algorithms and datasets

Villela

Corrêa

Ribeiro

et al. 2023

JIS

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Fake news (i.e., false news created to have a high capacity for dissemination and malicious intentions) is a problem of great interest to society today since it has achieved unprecedented political, economic, and social impacts. Taking advantage of modern digital communication and information technologies, they are widely propagated through social media, being their use intentional and challenging to identify. In order to mitigate the damage caused by fake news, researchers have been seeking the development of automated mechanisms to detect them, such as algorithms based on machine learning as well as the datasets employed in this development. This research aims to analyze the machine learning algorithms and datasets used in training to identify fake news published in the literature. It is exploratory research with a qualitative approach, which uses a research protocol to identify studies with the intention of analyzing them. As a result, we have the algorithms Stacking Method, Bidirectional Recurrent Neural Network (BiRNN), and Convolutional Neural Network (CNN), with 99.9%, 99.8%, and 99.8% accuracy, respectively. Although this accuracy is expressive, most of the research employed datasets in controlled environments (e.g., Kaggle) or without information updated in real-time (from social networks). Still, only a few studies have been applied in social network environments, where the most significant dissemination of disinformation occurs nowadays. Kaggle was the platform identified with the most frequently used datasets, being succeeded by Weibo, FNC-1, COVID-19 Fake News, and Twitter. For future research, studies should be carried out in addition to news about politics, the area that was the primary motivator for the growth of research from 2017, and the use of hybrid methods for identifying fake news.

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Section: Fake News Detectionmentioning

confidence: 99%

Section: Algorithm -Authormentioning

confidence: 99%

Fake news detection: a systematic literature review of machine learning algorithms and datasets

Villela

Corrêa

Ribeiro

et al. 2023

JIS

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“…Many research works are available regarding fake news. Many researchers use machine learning algorithms to detect fake news; some use deep learning algorithms that also notice fake news [7]. Some researchers focus on general unnatural news detection methods, and others on social media counterfeit news detection.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Researchers have attempted to address the problem of fake news using various techniques. Some research uses traditional machine learning models like Na¨ıve Bayes, Support Vector Machines [13], while others use deep learning models like CNN and BiLSTM [7]. The datasets used in most experiments are the standard fake news datasets like LIAR, BuzzFeed, ISOT, etc.…”

Section: Literature Reviewmentioning

confidence: 99%

Fake News Detection in the Medical Field Using Machine Learning Techniques

Murugesan¹,

Pachamuthu²

2022

IJSSE

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In today's world, fake news is the biggest and most challenging problem in the natural world environment. This type of fake news will create many problems in society, especially in the medical field. This research aims to detect automatic phoney news in the news. The following machine learning algorithm will help us to see phoney medical information based on dataset 1. KNN, 2. Naive Bayes, 3. Support Vector Machine, 4. BERT and 5. Decision tree, but the Decision tree will provide better accuracy. Findings: Performance measures such as accuracy, precision, recall, and f1-score showed 98.5% accuracy of our proposed Adaboost & Decision Tree algorithm. In this research work, we introduced and implemented the Proposed Ensembling (Adaboost & Decision tree) and achieved better accuracy. We collected and trained the dataset to identify misinformation in the medical area.

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“…The widespread use of fake news has significantly impacted our lives in politics and economics. Supervised computing algorithms are used for extracting fake news; they can transform the info set into a structured format with text mining methods [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Based Fake News Detection Techniques

Desamsetti

Juttuka

Posina

et al. 2023

Advances in Transdisciplinary Engineering

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Fake news on social media platforms is increasing rapidly, so many people are becoming victims of this news without their interference. It is a big challenge for us to detect who is spreading fake news. Fake news spreads faster nowadays than in the past due to the widespread use of the internet. This research paper is a study of techniques based on artificial intelligence, such as neural networks, natural language processing, and machine learning algorithms that work together. The learning models surveyed are Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bidirectional Recurrent Neural Network (RNN) methods. Natural language processing methods contain the tokenization model, and machine learning includes Term Frequency-Inverse Document Frequency (TFIDF) and unsupervised algorithms. The algorithms are compared and their effectiveness in detecting fake news is investigated, along with the advantages and disadvantages of the respective techniques.

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Fake News Detection using Deep Recurrent Neural Networks

Cited by 17 publications

References 13 publications

Fake news detection: a systematic literature review of machine learning algorithms and datasets

Fake news detection: a systematic literature review of machine learning algorithms and datasets

Fake News Detection in the Medical Field Using Machine Learning Techniques

Artificial Intelligence Based Fake News Detection Techniques

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