Fighting an Infodemic: COVID-19 Fake News Dataset

Patwa, Parth; Sharma, Shivam; Pykl, Srinivas; Guptha, Vineeth; Kumari, Gitanjali; Akhtar, Md. Shad; Ekbal, Asif; Das, Amitava; Chakraborty, Tanmoy

doi:10.1007/978-3-030-73696-5_3

Cited by 247 publications

(141 citation statements)

References 17 publications

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“…The selection of appropriate data is a crucial element in neural network training. The work uses two datasets, the first one is available in ISOT (Information Security and Object Technology) research laboratory [1] and the second one is the COVID-19 dataset, which was prepared as part of the competition related to counteracting disinformation in the COVID-19 pandemic [26]. The ISOT dataset contains two files: one with fake news and the other with real (true) news.…”

Section: Data Collectionsmentioning

confidence: 99%

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Implementation of the BERT-derived architectures to tackle disinformation challenges

Kula

Kozik

Choraś

2021

Neural Comput & Applic

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Recent progress in the area of modern technologies confirms that information is not only a commodity but can also become a tool for competition and rivalry among governments and corporations, or can be applied by ill-willed people to use it in their hate speech practices. The impact of information is overpowering and can lead to many socially undesirable phenomena, such as panic or political instability. To eliminate the threats of fake news publishing, modern computer security systems need flexible and intelligent tools. The design of models meeting the above-mentioned criteria is enabled by artificial intelligence and, above all, by the state-of-the-art neural network architectures, applied in NLP tasks. The BERT neural network belongs to this type of architectures. This paper presents Transformer-based hybrid architectures applied to create models for detecting fake news.

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Section: Data Collectionsmentioning

confidence: 99%

“…Number of items in COVID-19 dataset[26] pre-trained BERT architectures allow designing reliable models for relatively small data sizes. The collection 2 contained 'text' column and a new, added 'label' column.…”

mentioning

confidence: 99%

Implementation of the BERT-derived architectures to tackle disinformation challenges

Kula

Kozik

Choraś

2021

Neural Comput & Applic

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“…Several research studies [2][3][4] used various classification algorithms to detect misinformation related to the COVID-19 pandemic. The study [2] used BERT embedding and a shallow neural network to classify COVID-19 tweets.…”

Section: Introductionmentioning

confidence: 99%

“…Another study [3] used ten machine learning algorithms with seven feature extraction methods to classify fake news on COVID-19. Furthermore, the study [4] used four machine learning classifiers, decision trees, logistic regression, gradient boost, and support vector machine, to detect fake news on social media.…”

Section: Introductionmentioning

confidence: 99%

An Evolutionary Fake News Detection Method for COVID-19 Pandemic Information

et al. 2021

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As the COVID-19 pandemic rapidly spreads across the world, regrettably, misinformation and fake news related to COVID-19 have also spread remarkably. Such misinformation has confused people. To be able to detect such COVID-19 misinformation, an effective detection method should be applied to obtain more accurate information. This will help people and researchers easily differentiate between true and fake news. The objective of this research was to introduce an enhanced evolutionary detection approach to obtain better results compared with the previous approaches. The proposed approach aimed to reduce the number of symmetrical features and obtain a high accuracy after implementing three wrapper feature selections for evolutionary classifications using particle swarm optimization (PSO), the genetic algorithm (GA), and the salp swarm algorithm (SSA). The experiments were conducted on one of the popular datasets called the Koirala dataset. Based on the obtained prediction results, the proposed model revealed an optimistic and superior predictability performance with a high accuracy (75.4%) and reduced the number of features to 303. In addition, by comparison with other state-of-the-art classifiers, our results showed that the proposed detection method with the genetic algorithm model outperformed other classifiers in the accuracy.

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“…The CONSTRAINT 2021 shared task (https://constraint-shared-task-2021.github.io/, accessed on 3 June 2021) [116] was organized in the context of the First Workshop on Combating Online Hostile Posts in Regional Languages during Emergency Situation (CONSTRAINT 2021) collocated with AAAI 2021. It encompassed a task for detecting fake news in English over a data set with real and fake news on COVID-19 [117] and a second task on hostile post detection in Hindi over a data set of 8200 hostile and non-hostile texts from various social media platforms such as Twitter, Facebook, or WhatsApp [118]. This latter task is a multi-label, multi-class classification problem where each post can belong to one or more of a set of classes: fake news, hate speech, offensive, defamation, and non-hostile posts.…”

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confidence: 99%

Sentiment Analysis for Fake News Detection

et al. 2021

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In recent years, we have witnessed a rise in fake news, i.e., provably false pieces of information created with the intention of deception. The dissemination of this type of news poses a serious threat to cohesion and social well-being, since it fosters political polarization and the distrust of people with respect to their leaders. The huge amount of news that is disseminated through social media makes manual verification unfeasible, which has promoted the design and implementation of automatic systems for fake news detection. The creators of fake news use various stylistic tricks to promote the success of their creations, with one of them being to excite the sentiments of the recipients. This has led to sentiment analysis, the part of text analytics in charge of determining the polarity and strength of sentiments expressed in a text, to be used in fake news detection approaches, either as a basis of the system or as a complementary element. In this article, we study the different uses of sentiment analysis in the detection of fake news, with a discussion of the most relevant elements and shortcomings, and the requirements that should be met in the near future, such as multilingualism, explainability, mitigation of biases, or treatment of multimedia elements.

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Fighting an Infodemic: COVID-19 Fake News Dataset

Cited by 247 publications

References 17 publications

Implementation of the BERT-derived architectures to tackle disinformation challenges

Implementation of the BERT-derived architectures to tackle disinformation challenges

An Evolutionary Fake News Detection Method for COVID-19 Pandemic Information

Sentiment Analysis for Fake News Detection

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