State of the Art Models for Fake News Detection Tasks

Antoun, Wissam; Baly, Fady; Achour, Rim; Hussein, Amir; Hajj, Hazem

doi:10.1109/iciot48696.2020.9089487

Cited by 45 publications

(33 citation statements)

References 28 publications

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“…Teenagers and aged people with limited knowledge of possibilities of fake news on social media are an easy target [72]. Similarly, people with low qualifications and coming from rural areas are more prone to be the victims of fake news [95]. Following papers have discussed the features of a target-based account.…”

Section: Target-based Accounts Detectionmentioning

confidence: 99%

Are You a Cyborg, Bot or Human?—A Survey on Detecting Fake News Spreaders

Shahid

Staples

et al. 2022

IEEE Access

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One of the major components of Societal Digitalization is Online social networks (OSNs). OSNs can expose people to different popular trends in various aspects of life and alter people's beliefs, behaviors, and decisions and communication. Social bots and malicious users are the significant sources for spreading misinformation on social media and can pose serious cyber threats in society. The degree of similarity of user profiles of a cyber bot and a malicious user spreading fake news is so great that it is very difficult to differentiate both based on their attributes. Over the years, researchers have attempted to find a way to mitigate this problem. However, the detection of fake news spreaders across OSNs remains a challenge. In this paper, we have provided a comprehensive survey of the state of art methods for detecting malicious users and bots based on different features proposed in our novel taxonomy. We have also aimed to avert the crucial problem of fake news detection by discussing several key challenges and potential future research areas to help researchers who are new to this field.

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Section: Target-based Accounts Detectionmentioning

confidence: 99%

Are You a Cyborg, Bot or Human?—A Survey on Detecting Fake News Spreaders

Shahid

Staples

et al. 2022

IEEE Access

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“…The authors in [35] proposed a model detecting fake information automatically, and obtained the accuracy of 74%. In [36], the authors proposed a fake news automatic detection model, and showed that pre-trained deep learning models such as BERT, XLNet, and RoBERTa performed better than machine learning models such as SVM, RF, and XGBoost, and the accuracy of fake news detection was up to 98%.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Fake News Detection Based on Autoencoder

Guo

Wang

et al. 2021

IEEE Access

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With the development of social networks, the spread of fake news brings great negative effects to people's daily life, and even causes social panic. Fake news can be regarded as an anomaly on social networks, and autoencoder can be used as the basic unsupervised learning method. So, an unsupervised fake news detection method based on autoencoder (UFNDA) is proposed. This paper firstly considers some forms of news in social networks, integrates the text content, images, propagation, and user information of publishing news to improve the performance of fake news detection. Next, to obtain the hidden information and internal relationship between features, Bidirectional GRU(Bi-GRU) layer and Self-Attention layer are added into the autoencoder, and then reconstruct residual to detect fake news. The experimental results compared with the existence of other four methods, on two real-world datasets, show that UFNDA obtains the more positive results.

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“…Aggarwal et al investigate the performance of pre-trained BERT models on fake news detection and find that the "BERT model considerably outperforms other approaches even with minimal to no engineering of features", concluding that transfer learning "can yield good results in the case of detection of fake news" [1]. Radford et al demonstrate that pre-training contributes to the strong performance of transformers on a wide variety of natural language processing fields, showing in ablation studies that its removal "hurts performance across all the tasks" [46], and reinforcing BERT as the state-of-the-art in this context due to its "deep understanding of the language", which is considered "necessary to detect the subtle stylistics differences in the writing of the fake articles" [6].…”

Section: B Fake News Detectionmentioning

confidence: 99%

Transforming Fake News: Robust Generalisable News Classification Using Transformers

Ciara

Atapour-Abarghouei

2021

2021 IEEE International Conference on Big Data (Big Data)

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As online news has become increasingly popular and fake news increasingly prevalent, the ability to audit the veracity of online news content has become more important than ever. Such a task represents a binary classification challenge, for which transformers have achieved state-of-the-art results. Using the publicly available ISOT and Combined Corpus datasets, this study explores transformers' abilities to identify fake news, with particular attention given to investigating generalisation to unseen datasets with varying styles, topics and class distributions. Moreover, we explore the idea that opinion-based news articles cannot be classified as real or fake due to their subjective nature and often sensationalised language, and propose a novel two-step classification pipeline to remove such articles from both model training and the final deployed inference system. Experiments over the ISOT and Combined Corpus datasets show that transformers achieve an increase in F1 scores of up to 4.9% for out of distribution generalisation compared to baseline approaches, with a further increase of 10.1% following the implementation of our two-step classification pipeline. To the best of our knowledge, this study is the first to investigate generalisation of transformers in this context.

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State of the Art Models for Fake News Detection Tasks

Cited by 45 publications

References 28 publications

Are You a Cyborg, Bot or Human?—A Survey on Detecting Fake News Spreaders

Are You a Cyborg, Bot or Human?—A Survey on Detecting Fake News Spreaders

Unsupervised Fake News Detection Based on Autoencoder

Transforming Fake News: Robust Generalisable News Classification Using Transformers

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