Term Frequency Tokenization for Fake News Detection

Suresh, Pallavi; Shettigar, Abhishek; Karunavathi, M.; Ajith,; Kidane, Leul

doi:10.1007/978-3-031-18497-0_1

Engineering Cyber-Physical Systems and Critical Infrastructures

2023

DOI: 10.1007/978-3-031-18497-0_1

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Term Frequency Tokenization for Fake News Detection

Pallavi Suresh¹,

Abhishek Shettigar²,

M. Karunavathi³

et al.

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2024

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“…;Briscoe et al 2014;Giasemidis et al 2016;Elyassami et al 2022;Kini 2022), random forest (RF)(Briscoe et al 2014;Giasemidis et al 2016;Elyassami et al 2022;Sharma et al 2023), decision tree (DT)(Castillo et al 2011;Giasemidis et al 2016;Kishwar and Zafar 2023) and logistic regression (LR)(Giasemidis et al 2016;Elyassami et al 2022;Kishwar and Zafar 2023). 2.…”

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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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confidence: 99%

Disinformation detection using graph neural networks: a survey

Lakzaei,

Haghir Chehreghani,

Bagheri

2024

Artif Intell Rev

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Term Frequency Tokenization for Fake News Detection

Cited by 1 publication

References 14 publications

Disinformation detection using graph neural networks: a survey

Disinformation detection using graph neural networks: a survey

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