Machine Learning Methods for Fake News Classification

Ksieniewicz, Paweł; Choraś, Michał; Kozik, Rafał; Woźniak, Michał

doi:10.1007/978-3-030-33617-2_34

Cited by 24 publications

(13 citation statements)

References 16 publications

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“…Researchers have been trying to train machine learning classifier to detect with higher accuracy [98]. The better trained a classifier is, the more accurate it is [99]. Within ML framework, the common algorithms that have achieved better results include Neural Networks, Naive Bayes, Decision Trees and SVM.…”

Section: Discussionmentioning

confidence: 99%

Are You a Cyborg, Bot or Human?—A Survey on Detecting Fake News Spreaders

Shahid

Staples

et al. 2022

IEEE Access

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One of the major components of Societal Digitalization is Online social networks (OSNs). OSNs can expose people to different popular trends in various aspects of life and alter people's beliefs, behaviors, and decisions and communication. Social bots and malicious users are the significant sources for spreading misinformation on social media and can pose serious cyber threats in society. The degree of similarity of user profiles of a cyber bot and a malicious user spreading fake news is so great that it is very difficult to differentiate both based on their attributes. Over the years, researchers have attempted to find a way to mitigate this problem. However, the detection of fake news spreaders across OSNs remains a challenge. In this paper, we have provided a comprehensive survey of the state of art methods for detecting malicious users and bots based on different features proposed in our novel taxonomy. We have also aimed to avert the crucial problem of fake news detection by discussing several key challenges and potential future research areas to help researchers who are new to this field.

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Section: Discussionmentioning

confidence: 99%

Are You a Cyborg, Bot or Human?—A Survey on Detecting Fake News Spreaders

Shahid

Staples

et al. 2022

IEEE Access

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“…The authors trained a bi-directional LSTM model with at least four different datasets and achieved an overall classification accuracy of 80%. Ksieniewicz et al [18] proposed decision tree ensembles diversified using the Random Subspace method to detect fake news.…”

Section: Related Workmentioning

confidence: 99%

OPCNN-FAKE: Optimized Convolutional Neural Network for Fake News Detection

Saleh¹,

Alharbi

Alsamhi

2021

IEEE Access

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Recently, there is a rapid and wide increase in fake news, defined as provably incorrect information spread with the goal of fraud. The spread of this type of misinformation is a severe danger to social cohesiveness and well-being since it increases political polarisation and people's distrust of their leaders. Thus, fake news is a phenomenon that is having a significant impact on our social lives, particularly in politics. This paper proposes novel approaches based on Machine Learning (ML) and Deep Learning (DL) for the fake news detection system to address this phenomenon. The main aim of this paper is to find the optimal model that obtains high performance. Therefore, we propose an optimized Convolutional Neural Network model to detect fake news (OPCNN-FAKE). We compare the performance of the OPCNN-FAKE with Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and The six regular ML techniques: Decision Tree (DT), logistic Regression (LR), K Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes (NB) using four fake news benchmark datasets. Grid search and hyperopt optimization techniques have been used to optimize the parameters of ML and DL, respectively. In addition, N-gram and Term Frequency-Inverse Document Frequency (TF-IDF) have been used to extract features from the benchmark datasets for regular ML, while Glove word embedding has been used to represent features as a feature matrix for DL models. To evaluate the performance of the OPCNN-FAKE, accuracy, precision, recall, F1-measure were applied to validate the results. The results show that OPCNN-FAKE model has achieved the best performance for each dataset compared with other models. Furthermore, the OPCNN-FAKE has a higher performance of cross-validation results and testing results than the other models, which indicates that the OPCNN-FAKE for fake news detection is significantly better than other models.

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“…News classification tasks include fake news detection [34,46] and new category classification. People use traditional approaches such as Naive Bayes and Support Vector Machines [15,54], and neural network approaches such as LSTM [51] and CNN [18].…”

Section: Related Workmentioning

confidence: 99%

NewsEmbed: Modeling News through Pre-trained Document Representations

Liu,

2021

Preprint

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Effectively modeling text-rich fresh content such as news articles at document-level is a challenging problem. To ensure a contentbased model generalize well to a broad range of applications, it is critical to have a training dataset that is large beyond the scale of human labels while achieving desired quality. In this work, we address those two challenges by proposing a novel approach to mine semantically-relevant fresh documents, and their topic labels, with little human supervision. Meanwhile, we design a multitask model called NewsEmbed that alternatively trains a contrastive learning with a multi-label classification to derive a universal document encoder. We show that the proposed approach can provide billions of high quality organic training examples and can be naturally extended to multilingual setting where texts in different languages are encoded in the same semantic space. We experimentally demonstrate NewsEmbed's competitive performance across multiple natural language understanding tasks, both supervised and unsupervised.

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Machine Learning Methods for Fake News Classification

Cited by 24 publications

References 16 publications

Are You a Cyborg, Bot or Human?—A Survey on Detecting Fake News Spreaders

Are You a Cyborg, Bot or Human?—A Survey on Detecting Fake News Spreaders

OPCNN-FAKE: Optimized Convolutional Neural Network for Fake News Detection

NewsEmbed: Modeling News through Pre-trained Document Representations

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