A Hybrid Linguistic and Knowledge-Based Analysis Approach for Fake News Detection on Social Media

Seddari, Noureddine; Derhab, Abdelouahid; Belaoued, Mohamed; Halboob, Waleed; Al‐Muhtadi, Jalal; Bouras, Abdelghani

doi:10.1109/access.2022.3181184

Cited by 60 publications

(14 citation statements)

References 66 publications

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“…Numerous research studies already exist on false news detection utilizing various classification methods. It is critical to pick the proper feature reduction algorithm, as feature reduction has a significant impact on text classification performance [36]. For this reason, traditional machine learning-based methods perform poorly on high-dimensional features [37].…”

Section: Literature Reviewmentioning

confidence: 99%

AugFake-BERT: Handling Imbalance through Augmentation of Fake News Using BERT to Enhance the Performance of Fake News Classification

et al. 2022

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Fake news detection techniques are a topic of interest due to the vast abundance of fake news data accessible via social media. The present fake news detection system performs satisfactorily on well-balanced data. However, when the dataset is biased, these models perform poorly. Additionally, manual labeling of fake news data is time-consuming, though we have enough fake news traversing the internet. Thus, we introduce a text augmentation technique with a Bidirectional Encoder Representation of Transformers (BERT) language model to generate an augmented dataset composed of synthetic fake data. The proposed approach overcomes the issue of minority class and performs the classification with the AugFake-BERT model (trained with an augmented dataset). The proposed strategy is evaluated with twelve different state-of-the-art models. The proposed model outperforms the existing models with an accuracy of 92.45%. Moreover, accuracy, precision, recall, and f1-score performance metrics are utilized to evaluate the proposed strategy and demonstrate that a balanced dataset significantly affects classification performance.

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Section: Literature Reviewmentioning

confidence: 99%

AugFake-BERT: Handling Imbalance through Augmentation of Fake News Using BERT to Enhance the Performance of Fake News Classification

et al. 2022

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“…Karimi et al 187 proposed a fake news detection approach (MMFD) using an automated feature extraction method that can accept the news that comes from multiple sources and detect whether news is fake or real and classify them depending on their degrees of fakeness. Hosseinimotlagh and Papalexakis 188 used the tensor factorization, Wang et al 189 195 used some evaluation models such as count vectorizer, N-gram, and TF-IDF vectorizer with the ML algorithms, Kanagavalli et al 196 used classification based on bi-directional long short term memory (BiLSTM), Jarrahi and Safari 197 introduced convolutional neural network in sentence-level, many other authors [198][199][200][201] also used various ML based approach for detecting fake news on OSNs.…”

Section: Solutions For Fake News Detectionmentioning

confidence: 99%

“…Using deep convolutional layers, Tembhurne et al 193 developed a multi‐channel model (Mc‐DNN) that extracts the most important features without considering hand‐crafted features. Choudhury and Acharjee 194 used SVM, random forest, naive Bayes, and logistic regression classifiers, Raja and Raj 195 used some evaluation models such as count vectorizer, N‐gram, and TF‐IDF vectorizer with the ML algorithms, Kanagavalli et al 196 used classification based on bi‐directional long short term memory (BiLSTM), Jarrahi and Safari 197 introduced convolutional neural network in sentence‐level, many other authors 198‐201 also used various ML based approach for detecting fake news on OSNs.…”

Section: Ml‐based Solutions For Osn Platformmentioning

confidence: 99%

A comprehensive survey on online social networks security and privacy issues: Threats, machine learning‐based solutions, and open challenges

Bhattacharya

Roy

Chattopadhyay

et al. 2022

Security and Privacy

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Over the past few years, online social networks (OSNs) have become an inseparable part of people's daily lives. Instead of being passive readers, people are now enjoying their role as content contributors. OSN has permitted its users to share their information including the multimedia content. OSN users can express themselves in virtual communities by providing their opinions and interacting with others. As a consequence, the privacy and security threats in OSNs have emerged as a major concern to the research and business world. In the recent past, a number of survey works have been conducted to discuss different security and privacy threats in OSNs. However, till date, no survey work has been conducted that aims to classify and analyze various machine learning (ML)‐based solutions adapted for the security defense of OSNs. In this survey article, we present a detailed taxonomy with a classification of various works done on various security attacks in OSNs. We then review and summarize the existing state of art survey works on OSN security, and indicate the merits and limitations of these survey works. Next, we review all recent works that aim to provide ML‐based solutions toward defense of security attacks on OSNs. Finally, we discuss the future road‐map on OSN security and provide a comprehensive analysis on the open research issues with feasible measurements and possible solutions.

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“…Among the publications from Table-5, we found the works of Liu et al [46], Aldwairi et al [11], Pan et al [88], Wang et al [49], Wu et al [127], Lu et al [95] and Sahoo et al [136] as the most promising as far as content based model is concerned; the works of Rubin et al [116] and Wang et al [135] as the most promising when context based model is considered; and the most promising domain based model is developed by Jarrah et al [137]. There are a large number of existing empirical based models that combine content, context and domain to provide an effective fake news detection mechanism on social media including Castillo et al [12], Wang et al [64], Buntain et al [90], Ruchansky et al [57], Ajao et al [92], Zhou et al [132] and Seddari et al [139].…”

Section: Major Research Findingsmentioning

confidence: 99%

A Comparative Study of Computational Fake News Detection on Social Media

Manish

Jawed

Alam

et al. 2022

Preprint

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Social media platforms has become widely popular among netizens to share views and news. This can be attributed to easy affordability of digital devices, low cost Internet and free subscription to social media platforms. Individuals find social media platforms quite appealing where they can find like-minded people to exchange views and news. Studies have shown that a less credible person is more likely to propagate fake news in order to fulfill their objectives of any form - seeking attention, gaining financial benefits or influencing political views. Hence, fake news detection on social media has become one of the most envisaged research topics in recent times. Fake news detection on social media can be done through various methods based on sources, transmission, styles, and knowledge. The major contribution of this survey paper is to provide a comparative study of major computational methods for fake news detection on social media. The objective of this study is to help researchers carry further research using the details and the research gaps presented in the paper.

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A Hybrid Linguistic and Knowledge-Based Analysis Approach for Fake News Detection on Social Media

Cited by 60 publications

References 66 publications

AugFake-BERT: Handling Imbalance through Augmentation of Fake News Using BERT to Enhance the Performance of Fake News Classification

AugFake-BERT: Handling Imbalance through Augmentation of Fake News Using BERT to Enhance the Performance of Fake News Classification

A comprehensive survey on online social networks security and privacy issues: Threats, machine learning‐based solutions, and open challenges

A Comparative Study of Computational Fake News Detection on Social Media

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