Fake news detection within online social media using supervised artificial intelligence algorithms

Özbay, Feyza Altunbey; Alataş, Bilal

doi:10.1016/j.physa.2019.123174

Cited by 276 publications

(133 citation statements)

References 32 publications

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“…Similarly, Pérez-Rosas et al (2018) extracted handcrafted features from news, and built combined feature sets to train linear SVM model. Recently, Ozbay and Alatas (2020) presented a two-step method for fake news detection, which first conducts text mining and then applied twenty-three supervised classification methods to the news datasets. Besides, metaheuristic algorithms could also be used to detect fake news.…”

Section: Related Workmentioning

confidence: 99%

SemSeq4FD: Integrating global semantic relationship and local sequential order to enhance text representation for fake news detection

Wang

Yang

et al. 2021

Expert Systems with Applications

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The wide spread of fake news has caused huge losses to both governments and the public. Many existing works on fake news detection utilized spreading information like propagators profiles and the propagation structure. However, such methods face the difficulty of data collection and cannot detect fake news at the early stage. An alternative approach is to detect fake news solely based on its content. Early content-based methods rely on manually designed linguistic features. Such shallow features are domain-dependent, and cannot easily be generalized to cross-domain data. Recently, many natural language processing tasks resort to deep learning methods to learn word, sentence, and document representations. In this paper, we propose a novel graph-based neural network model named SemSeq4FD for early fake news detection based on enhanced text representations. In SemSeq4FD, we model the global pair-wise semantic relations between sentences as a complete graph, and learn the global sentence representations via a graph convolutional network with self-attention mechanism. Considering the importance of local context in conveying the sentence meaning, we employ a 1D convolutional network to learn the local sentence representations. The two representations are combined to form the enhanced sentence representations. Then a LSTM-based network is used to model the sequence of enhanced sentence representations, yielding the final document representation for fake news detection. Experiments conducted on four real-world datasets in English and Chinese, including cross-source and cross-domain datasets, demonstrate that our model can outperform the state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

SemSeq4FD: Integrating global semantic relationship and local sequential order to enhance text representation for fake news detection

Wang

Yang

et al. 2021

Expert Systems with Applications

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show abstract

“…The result reveals that the multimodal features and cross-modal similarity relationships are effective in detecting fake news. Whereas, in [ 30 ] authors proposed a technique for fake news detection by combining text mining techniques and supervised artificial intelligence algorithms, where the result shows that the best mean values in terms of precision, accuracy, recall, and f-measure have been obtained from the decision-tree, CVPS, ZeroR algorithms. In [ 31 ], the authors adopted a deep neural network(Convolution and Recurrent neural network) for the feature extraction process to predict fake news.…”

Section: Related Workmentioning

confidence: 99%

A unified approach for detection of Clickbait videos on YouTube using cognitive evidences

Varshney

Vishwakarma

2021

Appl Intell

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“…Mainly the fake news classification process is provided over a chosen specific and previously prepared dataset. Similarly, the authors of [14] used manually collected datasets for the fake news detection. The work in [13] provides a concept that can identify fake content using the automated system.…”

Section: Related Workmentioning

confidence: 99%

Improvement of Misleading and Fake News Classification for Flective Languages by Morphological Group Analysis

Kapusta

Obonya

2020

Informatics

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Due to the constantly evolving social media and different types of sources of information, we are facing different fake news and different types of misinformation. Currently, we are working on a project to identify applicable methods for identifying fake news for floating language types. We explored different approaches to detect fake news in the presented research, which are based on morphological analysis. This is one of the basic components of natural language processing. The aim of the article is to find out whether it is possible to improve the methods of dataset preparation based on morphological analysis. We collected our own and unique dataset, which consisted of articles from verified publishers and articles from news portals that are known as the publishers of fake and misleading news. Articles were in the Slovak language, which belongs to the floating types of languages. We explored different approaches in this article to the dataset preparation based on morphological analysis. The prepared datasets were the input data for creating the classifier of fake and real news. We selected decision trees for classification. The evaluation of the success of two different methods of preparation was carried out because of the success of the created classifier. We found a suitable dataset pre-processing technique by morphological group analysis. This technique could be used for improving fake news classification.

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Fake news detection within online social media using supervised artificial intelligence algorithms

Cited by 276 publications

References 32 publications

SemSeq4FD: Integrating global semantic relationship and local sequential order to enhance text representation for fake news detection

SemSeq4FD: Integrating global semantic relationship and local sequential order to enhance text representation for fake news detection

A unified approach for detection of Clickbait videos on YouTube using cognitive evidences

Improvement of Misleading and Fake News Classification for Flective Languages by Morphological Group Analysis

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