Fake news detection using an ensemble learning model based on Self-Adaptive Harmony Search algorithms

Huang, Yin-Fu; Chen, Po-Hong

doi:10.1016/j.eswa.2020.113584

Cited by 102 publications

(52 citation statements)

References 22 publications

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“…Results on multiple corpora show that Gradient Boosting achieves the best performance than any other individual models. A recent study (Huang & Chen, 2020) proposed a self-adaptive harmony search algorithm to get optimized weights of ensemble models. The proposed algorithm achieved outstanding performance with 99.4% accuracy.…”

Section: Related Workmentioning

confidence: 99%

Supervised ensemble learning methods towards automatically filtering Urdu fake news within social media

Akhter

Zheng

Afzal

et al. 2021

PeerJ Computer Science

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The popularity of the internet, smartphones, and social networks has contributed to the proliferation of misleading information like fake news and fake reviews on news blogs, online newspapers, and e-commerce applications. Fake news has a worldwide impact and potential to change political scenarios, deceive people into increasing product sales, defaming politicians or celebrities, and misguiding visitors to stop visiting a place or country. Therefore, it is vital to find automatic methods to detect fake news online. In several past studies, the focus was the English language, but the resource-poor languages have been completely ignored because of the scarcity of labeled corpus. In this study, we investigate this issue in the Urdu language. Our contribution is threefold. First, we design an annotated corpus of Urdu news articles for the fake news detection tasks. Second, we explore three individual machine learning models to detect fake news. Third, we use five ensemble learning methods to ensemble the base-predictors’ predictions to improve the fake news detection system’s overall performance. Our experiment results on two Urdu news corpora show the superiority of ensemble models over individual machine learning models. Three performance metrics balanced accuracy, the area under the curve, and mean absolute error used to find that Ensemble Selection and Vote models outperform the other machine learning and ensemble learning models.

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Section: Related Workmentioning

confidence: 99%

Supervised ensemble learning methods towards automatically filtering Urdu fake news within social media

Akhter

Zheng

Afzal

et al. 2021

PeerJ Computer Science

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“…Researchers [1] [2] have tried fake news detection using linguistic features other than vector space representations on fundamental machine learning models like SVM. With the advent of neural networks, an increase in computes power and a lot of data at disposal, researchers [3][4] [5] have even tried various combinations using Deep Learning techniques to overcome fake news. Researchers [6] have also tried ensemble 1 https://www.kaggle.com/c/fake-news/data techniques either by voting algorithms or by stacking techniques on top of each other.…”

Section: Related Workmentioning

confidence: 99%

Fake News Detection: a comparison between available Deep Learning techniques in vector space

Singh

2020

2020 IEEE 4th Conference on Information &Amp; Communication Technology (CICT)

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Fake News Detection is an essential problem in the field of Natural Language Processing. The benefits of an effective solution in this area are manifold for the goodwill of society. On a surface level, it broadly matches with the general problem of text classification. Researchers have proposed various approaches to tackle fake news using simple as well as some complex techniques. In this paper, we try to make a comparison between the present Deep Learning techniques by representing the news instances in some vector space using a combination of common mathematical operations with available vector space representations. We do a number of experiments using various combinations and permutations. Finally, we conclude with a sound analysis of the results and evaluate the reasons for such results.

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“…Although there are many fake news data sets available, a comprehensive and effective algorithm for detecting fake news has become one of the major obstacles. The existing research on false news detection can be roughly divided into two categories, namely, supervised learning methods based on machine learning [ 1 , 3 , 5 , 8 , 9 ], and supervised learning methods based on deep learning [ 4 , 6 , 19 – 21 ]. These models have achieved some results in various false news detection datasets.…”

Section: Introductionmentioning

confidence: 99%

“…Jwa [ 6 ] applied the Bidirectional Encoder Representations from Transformers model (BERT) model to detect fake news with a accuracy around 75%. An ensemble learning model combining four different models is proposed for fake news detection, and a higher accuracy of 72.3% is obtained in [ 4 ].The accuracy score obtained by FakeDetector with Deep Diffusive Network Model (DDNM) in [ 21 ] is 0.63. A model named as TI-CNN (Text and Image information based Convolutinal Neural Network) is proposed in [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

A novel self-learning semi-supervised deep learning network to detect fake news on social media

et al. 2021

Multimed Tools Appl

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Social media has become a popular means for people to consume and share news. However, it also enables the extensive spread of fake news, that is, news that deliberately provides false information, which has a significant negative impact on society. Especially recently, the false information about the new coronavirus disease 2019 (COVID-19) has spread like a virus around the world. The state of the Internet is forcing the world’s tech giants to take unprecedented action to protect the “information health” of the public. Despite many existing fake news datasets, comprehensive and effective algorithms for detecting fake news have become one of the major obstacles. In order to address this issue, we designed a self-learning semi-supervised deep learning network by adding a confidence network layer, which made it possible to automatically return and add correct results to help the neural network to accumulate positive sample cases, thus improving the accuracy of the neural network. Experimental results indicate that our network is more accurate than the existing mainstream machine learning methods and deep learning methods.

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Fake news detection using an ensemble learning model based on Self-Adaptive Harmony Search algorithms

Cited by 102 publications

References 22 publications

Supervised ensemble learning methods towards automatically filtering Urdu fake news within social media

Supervised ensemble learning methods towards automatically filtering Urdu fake news within social media

Fake News Detection: a comparison between available Deep Learning techniques in vector space

A novel self-learning semi-supervised deep learning network to detect fake news on social media

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