A survey on fake news and rumour detection techniques

Bondielli, Alessandro; Marcelloni, Francesco

doi:10.1016/j.ins.2019.05.035

Cited by 446 publications

(354 citation statements)

References 82 publications

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“…These days, the internet have become a vital part of our daily lives [2]. Traditional methods of acquiring information have nearly vanished to pave the way for social media platforms [2].…”

Section: Fake News and Social Mediamentioning

confidence: 99%

“…These days, the internet have become a vital part of our daily lives [2]. Traditional methods of acquiring information have nearly vanished to pave the way for social media platforms [2]. It was reported in 2017 that Facebook was the largest social media platform, hosting more 1.9 million users world-wide [18].…”

Section: Fake News and Social Mediamentioning

confidence: 99%

“…Satire and Humorous Fakes. Some of the content on social media are designed to amuse users and are made to deceive people into thinking that it is real news [2]. Satire is referred to as criticising or mocking ideas or opinions of people in an entertaining or comical way [7,29].…”

Section: Social Factorsmentioning

confidence: 99%

“…Most satire articles are designed to mislead and instruct certain individuals [13]. Some social media users will be convinced that it is true information and will thus share the information [2].…”

Section: Social Factorsmentioning

confidence: 99%

“…It is the publication of known false information and sharing it amongst individuals [7]. It is the intentional publishing of misleading information and can be verified as false through fact-checking [2]. Social media platforms allow individuals to fast share information with only a click of a single share button [4].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Systematic Review on Fake News Themes Reported in Literature

Celliers

Hattingh

2020

Lecture Notes in Computer Science

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In this systematic literature review, a study of the factors involved in the spreading of fake news, have been provided. In this review, the root causes of the spreading of fake news are identified to reduce the encouraging of such false information. To combat the spreading of fake news on social media, the reasons behind the spreading of fake news must first be identified. Therefore, this literature review takes an early initiative to identify the possible reasons behind the spreading of fake news. The purpose of this literature review is to identify why individuals tend to share false information and to possibly help in detecting fake news before it spreads.

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“…These days, the internet have become a vital part of our daily lives [2]. Traditional methods of acquiring information have nearly vanished to pave the way for social media platforms [2].…”

Section: Fake News and Social Mediamentioning

confidence: 99%

Section: Fake News and Social Mediamentioning

confidence: 99%

Section: Social Factorsmentioning

confidence: 99%

Section: Social Factorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Systematic Review on Fake News Themes Reported in Literature

Celliers

Hattingh

2020

Lecture Notes in Computer Science

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Comprehending Covidiocy Communication

Spitzberg¹

2021

Communicating Science in Times of Crisis

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Social rumor detection based on multilayer transformer encoding blocks

Lin

Chen

2020

Concurrency and Computation

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The propagation of rumors on social media has been identified as a critical problem in recent years that causes social panic or economic turmoil (to some extent), thereby giving rise to the need for faster identification. With the advancements in deep learning, researches based on neural networks become popular. Most of the existing methods extensively adopt recurrent neural networks (RNNs), such as gated recurrent unit and long short-term memory. This results in a significant degradation in the concurrency performance of the models, implying an increased consumption of time and resources. This study proposes a model with multilayer transformer encoding blocks for detecting rumors. The self-attention mechanism in the transformer encoding blocks provides better concurrency to the proposed model and improves its performance. The proposed model performs faster executions using less or no recurrences in comparison to other models based on RNNs. Experiments on two real social datasets verified that our model could achieve significantly better results than baseline methods. The accuracy rate increased by 1.1% and 2.2% on the Weibo and PHEME datasets, respectively. In comparison to the methods that use RNNs as a feature extractor, the training duration in the proposed model was reduced by 16% and 70% in Weibo and PHEME, respectively.

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A survey on fake news and rumour detection techniques

Cited by 446 publications

References 82 publications

A Systematic Review on Fake News Themes Reported in Literature

A Systematic Review on Fake News Themes Reported in Literature

Comprehending Covidiocy Communication

Social rumor detection based on multilayer transformer encoding blocks

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