Detection of Fake News Using Transformer Model

Qazi, Momina; Khan, Muhammad Usman Shahid; Ali, Mazhar

doi:10.1109/icomet48670.2020.9074071

Cited by 14 publications

(6 citation statements)

References 15 publications

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“…The majority of recent work in disinformation analysis has been conducted with Computational Linguistic methods (Ruffo, Semeraro, Giachanou, & Rosso, 2023). Some recent (and straightforward) approaches put together an NLP pipeline (preprocessing, feature extraction, model building) to exploit traditional text analysis techniques (Asaad & Erascu, 2018;Koloski, Pollak, & Škrlj, 2020) or to train neural networks (Reddy, Suman, Saha, & Bhattacharyya, 2020;Umer et al, 2020;Eldesoky & Moussa, 2021;Qazi, Khan, & Ali, 2020;Tida, Hsu, & Hei, 2022;Dun, Tu, Chen, Hou, & Yuan, 2021) for fake news classification.…”

Section: Natural Language Processing For Stylistic Characterizationmentioning

confidence: 99%

IBERIFIER Reports: Is the ‘AI toolbox for disinformation’ ready?

Montoro Montarroso,

Camacho,

Martín

et al. 2023

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This project has received funding from the European Commission under the call CEF-TC-2020-2 (European Digital Media Observatory)Reference: 2020-EU-IA-0252 Consortium DisclaimerAll information provided reflects the status of the IBERIFIER project at the time of writing and may be subject to change.Neither the IBERIFIER Consortium as a whole, nor any single party within the IBERIFIER Consortium warrant that the information contained in this document is capable of use, nor that the use of such information is free from risk. Neither the IBERIFIER Consortium as a whole, nor any single party within the IBERIFIER Consortium accepts any liability for loss or damage suffered by any person using the information.This document does not represent the opinion of the European Community, and the European Community is not responsible for any use that might be made of its content.

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Section: Natural Language Processing For Stylistic Characterizationmentioning

confidence: 99%

IBERIFIER Reports: Is the ‘AI toolbox for disinformation’ ready?

Montoro Montarroso,

Camacho,

Martín

et al. 2023

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“…Research on automatic fake news detection using the Transformer model has been carried out by several researchers. For example, research conducted by [12] and [13] used the BERT transformer model to detect hoaxes on English news datasets, namely LIAR and the FNC-1 datasets sequentially. The research shows that the pretrained transformer model gets 15-20% better accuracy than the traditional CNN and LSTM models in classifying hoax news.…”

Section: Related Workmentioning

confidence: 99%

“…Transformer models such as BERT, ALBERT, and XLNet have been widely used in making automatic hoax detectors and have been shown to have better performance than traditional approaches (e.g. [12], [13], [14]). The transformer also has pre-trained multilingual models, such as mBERT, XLM, and XLM-R, which can be used for many languages.…”

Section: Introductionmentioning

confidence: 99%

Indonesian Hoax News Classification with Multilingual Transformer Model and BERTopic

Hutama¹,

Suhartono

2022

IJCAI

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Technology and information growth enable internet users to play a role in disseminating information, including hoax news. One way that to avoid hoax news is to look for sources of information, but valid news is not always perceived as 'true' by individuals because human judgments can lead to bias. Several studies on automatic hoax news classification have been carried out using various deep learning approaches such as the pre-trained multilingual transformer model. This study focuses on classifying Indonesian hoax news using the pre-trained transformer multilingual model (XLM-R and mBERT) combined with a BERTopic model as a topic distribution model. The result shows that the proposed method outperforms the baseline model in classifying fake news in the low-resource language (Indonesian) with accuracy, precision, recall, and F1 results of 0.9051, 0.9515, 0.8233, and 0.8828 respectively. Povzetek: Raziskava se ukvarja z identifikacijo lažnih novic v Indoneziji s pomočjo modelov XLM-R and mBERT.

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“…Convolution neural networks (CNN)(Chen et al 2017; Kadek et al 2022;Vishwakarma et al 2023), recurrent neural networks (RNN)(Kadek et al 2022;Ma et al 2016;Kishore and Kumar 2023) and transformers models(Slovikovskaya 2019;Qazi et al 2020;Hande et al 2021;Schütz et al 2021;Rai et al 2022;Praseed et al 2023) are among the most commonly used deep learning models for detecting disinformation. (b) The second category comprises DL models that utilize GNNs.…”

mentioning

confidence: 99%

Disinformation detection using graph neural networks: a survey

Lakzaei,

Haghir Chehreghani,

Bagheri

2024

Artif Intell Rev

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The creation and propagation of disinformation on social media is a growing concern. The widespread dissemination of disinformation can have destructive effects on people’s attitudes and behavior. So, it is essential to detect disinformation as soon as possible. Therefore, the interest in effective detection techniques has grown rapidly in recent years. Major social media and social networking sites are trying to develop robust strategies to detect disinformation and prevent its spread. Machine learning techniques and especially neural networks, have an essential role in this task. In this paper, we review different approaches for automatic disinformation detection, with a focus on methods that leverage graph neural networks (GNNs). GNNs are very suitable tools for detecting disinformation in social networks. Because on the one hand, graphs are the most comprehensive way to model social networks and on the other hand, GNNs are the best tool for processing graph data. We define different forms of disinformation, and examine the features used and the methods presented from different perspectives. We also discuss relevant research areas, open problems, and future research directions for disinformation detection in social media.

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Detection of Fake News Using Transformer Model

Cited by 14 publications

References 15 publications

IBERIFIER Reports: Is the ‘AI toolbox for disinformation’ ready?

IBERIFIER Reports: Is the ‘AI toolbox for disinformation’ ready?

Indonesian Hoax News Classification with Multilingual Transformer Model and BERTopic

Disinformation detection using graph neural networks: a survey

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