Tuned bidirectional encoder representations from transformers for fake news detection

Pardamean, Amsal; Pardede, Hilman F.

doi:10.11591/ijeecs.v22.i3.pp1667-1671

Cited by 4 publications

(4 citation statements)

References 21 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%

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%

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

Villela

Corrêa

Ribeiro

et al. 2023

JIS

View full text Add to dashboard Cite

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“…However, fake news detection is a complicated task that ordinary people can hardly detect without any extra information instead of the news contents [7]. Therefore, fake news detection has become a quite active study in natural language processing (NLP), and several studies have explored algorithms for solving inaccurate fake detection [8]. In addition, there are also many various rumour datasets in English that have been provided, including information security and object technology (ISOT) dataset.…”

Section: Introductionmentioning

confidence: 99%

News classification using light gradient boosted machine algorithm

Kholiq

Wiranto

Sihwi

2022

IJEECS

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News classification is a complex issue as people are easily convinced of misleading information and lack control over the spread of fake news. However, we ca n break the problem of spreading fake news with artificial intelligence (AI), which has developed rapidly. This study proposes a news classification model using a light gradient boosted machine (LightGBM) algorithm. The model is analyzed using two feature extraction techniques, count vectorizer and Tfidf vectorize r and compared with a deep learning model using long - short term memory (LSTM). The experimental evaluation showed that all LightGBM models outperform LSTM. The best model is the count vectorizer Li ghtGBM, which achieves an accuracy value of 0.9933 and an area under curve (AUC) score of 0.9999.

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“…Efforts are being made to automate the process of fake news detection [12]- [16]. One such technique is Generating aRticles by Only Viewing mEtadata Records (GROVER) [17] which generates fake news and then uses nucleus sampling at each time step to sample from the most probable words whose cumulative probability comprises the top-p% of the entire vocabulary, to create fake news.…”

Section: Introductionmentioning

confidence: 99%

Predictive linguistic cues for fake news: a societal artificial intelligence problem

Aneja

Kumaraguru

2022

IJ-AI

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<span>Media news are making a large part of public opinion and, therefore, must not be fake. News on web sites, blogs, and social media must be analyzed before being published. In this paper, we present linguistic characteristics of media news items to differentiate between fake news and real news using machine learning algorithms. Neural fake news generation, headlines created by machines, semantic incongruities in text and image captions generated by machine are other types of fake news problems. These problems use neural networks which mainly control distributional features rather than evidence. We propose applying correlation between features set and class, and correlation among the features to compute correlation attribute evaluation metric and covariance metric to compute variance of attributes over the news items. Features unique, negative, positive, and cardinal numbers with high values on the metrics are observed to provide a high area under the curve (AUC) and F1-score.</span>

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Tuned bidirectional encoder representations from transformers for fake news detection

Cited by 4 publications

References 21 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

News classification using light gradient boosted machine algorithm

Predictive linguistic cues for fake news: a societal artificial intelligence problem

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