A benchmark study of machine learning models for online fake news detection

Khan, Junaed Younus; Khondaker, Md. Tawkat Islam; Afroz, Sadia; Uddin, Gias; Iqbal, Anindya

doi:10.1016/j.mlwa.2021.100032

Cited by 145 publications

(101 citation statements)

References 21 publications

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“…Aphiwongsophon and Chongstitvatana [16] based their models on identifying fake news using selected data sourced from Twitter. It is likely that these approaches will fall victim to dataset bias and possibly perform poorly on a different category of news [10]. Gilda [17] explored some traditional machine learning approaches.…”

Section: Related Workmentioning

confidence: 99%

“…e models were evaluated independently and the linear support vector machine classifier achieved the best score. However, several advanced learning models are not applied although they have excelled in text classification [10].…”

Section: Related Workmentioning

confidence: 99%

“…Research using deep learning to identify fake news works has accomplished encouraging results [10]. Rashkin et al [19] used linguistic feature analysis and achieved the remarkable outcome of Long Short Term Memory.…”

Section: Related Workmentioning

confidence: 99%

“…Prior works have used many classical models. However, several unconventional learning models are not applied although they have proven best in numerous text classification problems [10]. An ensemble approach is proposed to help resolve the identified gap.…”

Section: Introductionmentioning

confidence: 99%

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Detection of Online Fake News Using Blending Ensemble Learning

Hansrajh

Adeliyi

Wing

2021

Scientific Programming

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The exponential growth in fake news and its inherent threat to democracy, public trust, and justice has escalated the necessity for fake news detection and mitigation. Detecting fake news is a complex challenge as it is intentionally written to mislead and hoodwink. Humans are not good at identifying fake news. The detection of fake news by humans is reported to be at a rate of 54% and an additional 4% is reported in the literature as being speculative. The significance of fighting fake news is exemplified during the present pandemic. Consequently, social networks are ramping up the usage of detection tools and educating the public in recognising fake news. In the literature, it was observed that several machine learning algorithms have been applied to the detection of fake news with limited and mixed success. However, several advanced machine learning models are not being applied, although recent studies are demonstrating the efﬁcacy of the ensemble machine learning approach; hence, the purpose of this study is to assist in the automated detection of fake news. An ensemble approach is adopted to help resolve the identified gap. This study proposed a blended machine learning ensemble model developed from logistic regression, support vector machine, linear discriminant analysis, stochastic gradient descent, and ridge regression, which is then used on a publicly available dataset to predict if a news report is true or not. The proposed model will be appraised with the popular classical machine learning models, while performance metrics such as AUC, ROC, recall, accuracy, precision, and f1-score will be used to measure the performance of the proposed model. Results presented showed that the proposed model outperformed other popular classical machine learning models.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detection of Online Fake News Using Blending Ensemble Learning

Hansrajh

Adeliyi

Wing

2021

Scientific Programming

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“…On the other hand, these methods have been proven useful only when combined with more complex analysis methods. Khan et al (2019) stated that meanwhile, the linguistic-based features extracted from the news content are not sufficient for revealing the in-depth underlying distribution patterns of fake news (Shu et al, 2017). Auxiliary features, such as the news author's credibility and the spreading patterns of the news, play more important roles for online fake news prediction.…”

Section: Related Workmentioning

confidence: 99%

Using of n-grams from morphological tags for fake news classification

Kapusta

Drlík

Munk

2021

PeerJ Computer Science

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Research of the techniques for effective fake news detection has become very needed and attractive. These techniques have a background in many research disciplines, including morphological analysis. Several researchers stated that simple content-related n-grams and POS tagging had been proven insufficient for fake news classification. However, they did not realise any empirical research results, which could confirm these statements experimentally in the last decade. Considering this contradiction, the main aim of the paper is to experimentally evaluate the potential of the common use of n-grams and POS tags for the correct classification of fake and true news. The dataset of published fake or real news about the current Covid-19 pandemic was pre-processed using morphological analysis. As a result, n-grams of POS tags were prepared and further analysed. Three techniques based on POS tags were proposed and applied to different groups of n-grams in the pre-processing phase of fake news detection. The n-gram size was examined as the first. Subsequently, the most suitable depth of the decision trees for sufficient generalization was scoped. Finally, the performance measures of models based on the proposed techniques were compared with the standardised reference TF-IDF technique. The performance measures of the model like accuracy, precision, recall and f1-score are considered, together with the 10-fold cross-validation technique. Simultaneously, the question, whether the TF-IDF technique can be improved using POS tags was researched in detail. The results showed that the newly proposed techniques are comparable with the traditional TF-IDF technique. At the same time, it can be stated that the morphological analysis can improve the baseline TF-IDF technique. As a result, the performance measures of the model, precision for fake news and recall for real news, were statistically significantly improved.

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Fake news detection using deep learning integrating feature extraction, natural language processing, and statistical descriptors

Madani

Motameni

Mohamadi

2022

Security and Privacy

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Fake news potentially causes serious problems in society. Therefore, it is necessary to detect such news, which is, of course, associated with some challenges such as events, verification and datasets. Reference datasets related to this area face various problems, like the lack of sufficient information about news samples, no subject diversity, etc. The present paper proposes a model using feature extraction and machine learning algorithms for dealing with some of these problems. In the feature extraction phase, two new features (named coherence and cohesion), along with other key features, were extracted from news samples. In the detection phase, initially, the news samples of each dataset were sorted based on a specific order (easier samples in the beginning and harder ones towards the end) using a hybrid method consisting of statistical descriptors and a k-nearest neighbor algorithm. Then, inspired by the human learning principles, the sorted news samples, were sent to the Long-Short-Term Memory and classical machine learning algorithms for the detection of fake news. The obtained results indicated the higher performance of the proposed model in fake news detection compared to benchmark models.

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A benchmark study of machine learning models for online fake news detection

Cited by 145 publications

References 21 publications

Detection of Online Fake News Using Blending Ensemble Learning

Detection of Online Fake News Using Blending Ensemble Learning

Using of n-grams from morphological tags for fake news classification

Fake news detection using deep learning integrating feature extraction, natural language processing, and statistical descriptors

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