Approaches To Identify Fake News A Systematic Literature Review

Bonisiwe Shabane

-Dec 5, 2025, 4:57 PM

approaches to identify fake news a systematic literature review

This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. With the widespread dissemination of information via digital media platforms, it is of utmost importance for individuals and societies to be able to judge the credibility of it. Fake news is not a recent concept, but it is a commonly occurring phenomenon in current times. The consequence of fake news can range from being merely annoying to influencing and misleading societies or even nations. A variety of approaches exist to identify fake news.

By conducting a systematic literature review, we identify the main approaches currently available to identify fake news and how these approaches can be applied in different situations. Some approaches are illustrated with a relevant example as well as the challenges and the appropriate context in which the specific approach can be used. Keywords: Fake news, Machine learning, Linguistics, Semantics, Syntax, Algorithms, Digital tools, Social media Paskin (2018: 254) defines fake news as “particular news articles that originate either on mainstream media (online or offline) or social media and have no factual basis, but are presented as facts and not... The importance of combatting fake news is starkly illustrated during the current COVID-19 pandemic. Social networks are stepping up in using digital fake news detection tools and educating the public towards spotting fake news.

At the time of writing, Facebook uses machine learning algorithms to identify false or sensational claims used in advertising for alternative cures, they place potential fake news articles lower in the news feed, and... Twitter ensures that searches on the virus result in credible articles and Instagram redirects anyone searching for information on the virus to a special message with credible information (Marr 2020). These measures are possible because different approaches exist that assist the detection of fake news. For example, platforms based on machine learning use fake news from the biggest media outlets, to refine algorithms for identifying fake news (Macaulay 2018). Some approaches detect fake news by using metadata such as a comparison of release time of the article and timelines of spreading the article as well where the story spread (Macaulay 2018). A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.© Copyright 2025 IEEE - All rights reserved.

Use of this web site signifies your agreement to the terms and conditions. Thompson, R.C.; Joseph, S.; Adeliyi, T.T. A Systematic Literature Review and Meta-Analysis of Studies on Online Fake News Detection. Information 2022, 13, 527. https://doi.org/10.3390/info13110527 Thompson RC, Joseph S, Adeliyi TT.

A Systematic Literature Review and Meta-Analysis of Studies on Online Fake News Detection. Information. 2022; 13(11):527. https://doi.org/10.3390/info13110527 Thompson, Robyn C., Seena Joseph, and Timothy T. Adeliyi.

2022. "A Systematic Literature Review and Meta-Analysis of Studies on Online Fake News Detection" Information 13, no. 11: 527. https://doi.org/10.3390/info13110527 Thompson, R. C., Joseph, S., & Adeliyi, T.

T. (2022). A Systematic Literature Review and Meta-Analysis of Studies on Online Fake News Detection. Information, 13(11), 527. https://doi.org/10.3390/info13110527 Scientific Reports volume 15, Article number: 41522 (2025) Cite this article

In recent years, the widespread dissemination of fake news on social media has raised concerns about its impact on public opinion, trust, and decision-making. Addressing the limitations of traditional detection methods, this study introduces a hybrid deep learning approach that enhances the identification of fake news. The objective is to improve detection accuracy and model robustness by combining a Long Short-Term Memory (LSTM) network for contextual feature extraction with a Convolutional Gaussian Perceptron Neural Network (CGPNN) for classification. To further optimize performance, we integrated a metaheuristic Moth-Flame Whale Optimization (MFWO) algorithm for hyperparameter tuning. Experimental evaluation was conducted on four benchmark datasets ISOT, Fakeddit, BuzzFeedNews, and FakeNewsNet using standardized preprocessing techniques and TF-IDF-based text representation. Results show that the proposed model outperforms existing methods, achieving up to 98% accuracy, 95% F1-score, and statistically significant improvements (p < 0.05) over transformer-based and graph neural network models.

These findings suggest that the hybrid framework effectively captures linguistic patterns and textual irregularities in deceptive content. The proposed method offers a scalable and efficient solution for fake news detection with practical applications in social media monitoring, digital journalism, and public awareness campaigns. Overall, the framework delivers 3–8% higher accuracy and F1-score compared to state-of-the-art approaches, demonstrating both robustness and practical applicability for large-scale fake news detection. Fake news has existed long before the advent of digital technology, with the deliberate dissemination of false information dating back to ancient times. However, the proliferation of internet technologies and computational advancements has dramatically transformed the landscape of information sharing. Contemporary digital platforms particularly social media networks have created unprecedented opportunities for content generation and dissemination with minimal barriers to entry1.

The information revolution has brought about democratization of information access, yet it has also enabled fast dissemination of both genuine and false content. The replacement of traditional media channels with social media as primary information sources has led to the fast spread of misleading content. False information spreads past its original targets to affect society at large while damaging public trust in authentic news sources and creating false public reactions to factual reporting. Research showed that fabricated content spread more widely on Facebook and Twitter than accurate reporting during the 2016 U.S. presidential election2. The September 2024 Springfield pet-eating hoax spread false information about Haitian immigrants eating domestic pets after political figures shared it despite its baseless origins from an unsubstantiated social media post3.

The financial incentives behind fake news proliferation should not be ignored. Research shows that major technology platforms gain indirect advantages from users engaging with provocative false content. Websites that produce fabricated news generate significant revenue through online advertising systems which creates financial incentives for spreading misinformation4. The financial aspect became clear in July 2024 when false information about a Southport tragedy led to civil disturbances throughout the United Kingdom5. Unsubstantiated claims about government fund misappropriation to media outlets which independent fact-checkers later disproved demonstrate how misinformation affects public discourse at its highest levels6. Specialized fact-checking websites and platform-integrated tools such as the “community notes” system implemented on X (formerly Twitter) have emerged to fight this trend.

The International Federation of Library Associations and Institutions has created frameworks to help users detect unreliable content and Bozkurt et al.8 have conducted systematic assessments of current detection and prevention methods. These initiatives recognize that political strategies frequently build upon misinformation which affects financial markets and investment choices and crisis management. The intentional creation of fake news that looks authentic creates major obstacles for detection systems9. Social media platforms enable users to share content with their connected network which leads to increased potential impact10. The 2016 survey showed that 62% of American adults obtained news from social media platforms while 49% did in 2012 and 47% used social media as their main news source thus making fake news... The situation demands immediate implementation of effective protective measures against misinformation.

The implementation of complete detection systems faces multiple technical obstacles because of reference datasets and event coverage and consumption patterns and verification processes and content divergence11. The research community has developed multiple solutions to tackle these issues yet fake news detection accuracy remains a persistent challenge which drives ongoing investigations into better detection methods. The rapid growth of false information online requires immediate development of automated systems which can analyze large volumes of content. Deep learning techniques show exceptional potential when used for social network content evaluation12. Traditional fake news detection methods have mainly depended on content analysis of news articles’ intrinsic features while social context models that study information diffusion patterns have been adopted in recent times13. The enormous amount of content and its fast spread across platforms makes manual assessment impossible so automated systems must be developed to quickly assess information reliability.

The development of automated models has focused on either news content or social context features. The approaches use data mining algorithms to extract fake news characteristics which are based on established social and psychological theories. A general classification model for fake news identification consists of two stages: feature extraction and model construction from a data mining perspective. The system extracts relevant content characteristics during feature extraction before using these representations to differentiate between authentic and fabricated news in the model construction phase14,15,47,48,49. Received 2022 May 19; Revised 2022 Sep 27; Accepted 2022 Oct 29; Issue date 2022. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source.

These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Emerging of social media creates inconsistencies in online news, which causes confusion and uncertainty for consumers while making decisions regarding purchases. On the other hand, in existing studies, there is a lack of empirical and systematic examination observed in terms of inconsistency regarding reviews. The spreading of fake news and disinformation on social media platforms has adverse effects on stability and social harmony. Fake news is often emerging and spreading on social media day by day. It results in influencing or annoying and also misleading nations or societies.

Several studies aim to recognize fake news from real news on online social media platforms. Accurate and timely detection of fake news prevents the propagation of fake news. This paper aims to conduct a review on fake news detection models that is contributed by a variety of machine learning and deep learning algorithms. The fundamental and well-performing approaches that existed in the past years are reviewed and categorized and described in different datasets. Further, the dataset utilized, simulation platforms, and recorded performance metrics are evaluated as an extended review model. Finally, the survey expedites the research findings and challenges that could have significant implications for the upcoming researchers and professionals to improve the trust worthiness of automated fake news detection models.

Keywords: Fake news detection models, Deep learning, Machine learning, Dataset utilized, Simulation platforms, Recorded performance metrics, Research gaps and challenges Fake news identification is one of the eminent research topics, which has been studied in recent years (Sengupta et al. 2021). Fake news is often spread by yellow journalism before digital technology with the intention of glorious news like hilarious news, accidents, rumors, and crime news (Islam et al. 2020). In the digital era, it is simpler for spreading fake news while a user may distribute fake news to neighbors, their friends, and so on due to the unique characteristics of social media (Habib...

Approaches To Identify Fake News A Systematic Literature Review

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