University of Kwazulu-Natal

Can Ai Outsmart Fake News Detecting Misinformation With Ai Models In

Bonisiwe Shabane
-
can ai outsmart fake news detecting misinformation with ai models in

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. In the rapidly evolving digital age, the proliferation of disinformation and misinformation poses significant challenges to societal trust and information integrity.

Recognizing the urgency of addressing this issue, this systematic review endeavors to explore the role of artificial intelligence (AI) in combating the spread of false information. This study aims to provide a comprehensive analysis of how AI technologies have been utilized from 2014 to 2024 to detect, analyze, and mitigate the impact of misinformation across various platforms. This research utilized an exhaustive search across prominent databases such as ProQuest, IEEE Explore, Web of Science, and Scopus. Articles published within the specified timeframe were meticulously screened, resulting in the identification of 8103 studies. Through elimination of duplicates and screening based on title, abstract, and full-text review, we meticulously distilled this vast pool to 76 studies that met the study’s eligibility criteria. Key findings from the review emphasize the advancements and challenges in AI applications for combating misinformation.

These findings highlight AI’s capacity to enhance information verification through sophisticated algorithms and natural language processing. They further emphasize the integration of human oversight and continual algorithm refinement emerges as pivotal in augmenting AI’s effectiveness in discerning and countering misinformation. By fostering collaboration across sectors and leveraging the insights gleaned from this study, researchers can propel the development of ethical and effective AI solutions. This is a preview of subscription content, log in via an institution to check access. Price excludes VAT (USA) Tax calculation will be finalised during checkout. The data presented in this study are available on request from the corresponding author.

Baptista JP, Gradim A (2022) A working definition of fake news. Encyclopedia 2(1):66 Mouratidis, D.; Kanavos, A.; Kermanidis, K. From Misinformation to Insight: Machine Learning Strategies for Fake News Detection. Information 2025, 16, 189. https://doi.org/10.3390/info16030189

Mouratidis D, Kanavos A, Kermanidis K. From Misinformation to Insight: Machine Learning Strategies for Fake News Detection. Information. 2025; 16(3):189. https://doi.org/10.3390/info16030189 Mouratidis, Despoina, Andreas Kanavos, and Katia Kermanidis.

2025. "From Misinformation to Insight: Machine Learning Strategies for Fake News Detection" Information 16, no. 3: 189. https://doi.org/10.3390/info16030189 Mouratidis, D., Kanavos, A., & Kermanidis, K. (2025).

From Misinformation to Insight: Machine Learning Strategies for Fake News Detection. Information, 16(3), 189. https://doi.org/10.3390/info16030189 In today’s hyper-connected world, the rapid spread of information is both a blessing and a curse. While we have unprecedented access to knowledge, we also face an overwhelming tide of fake news and sophisticated misinformation, often amplified by social media algorithms. This digital deception can have serious real-world consequences, influencing public opinion, elections, and even personal safety.

But what if the very technology that contributes to this problem – artificial intelligence – could also be the solution? This blog post delves into the fascinating potential of AI to combat misinformation and deepfakes, exploring the challenges and promising advancements in this critical fight. The landscape of fake news has evolved dramatically in recent years. No longer are we just dealing with poorly written, obviously fabricated stories. The rise of deepfakes, AI-generated videos and audio that convincingly mimic real people saying or doing things they never did, presents a new level of threat. These sophisticated manipulations can erode trust in media, sow discord, and be weaponised for malicious purposes.

Studies have shown the alarming speed and reach of misinformation online. False stories can spread significantly faster and further than factual news, highlighting the urgent need for effective countermeasures. The sheer volume of online content makes manual fact-checking an impossible task, underscoring the necessity of automated solutions. It’s important to acknowledge that artificial intelligence itself plays a role in the spread of fake news. Algorithms used by social media platforms can inadvertently amplify sensational or emotionally charged content, which often includes misinformation. Furthermore, AI tools are increasingly being used to generate and disseminate fake content at scale.

Despite its role in the problem, artificial intelligence holds immense promise in the fight against fake news. Researchers and developers are exploring various AI-powered techniques to detect, flag, and ultimately combat online manipulation. 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.

People Also Search

Scientific Reports Volume 15, Article Number: 41522 (2025) Cite This

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. Th...

Results Show That The Proposed Model Outperforms Existing Methods, Achieving

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 an...

Contemporary Digital Platforms Particularly Social Media Networks Have Created Unprecedented

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...

The September 2024 Springfield Pet-eating Hoax Spread False Information About

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...

Specialized Fact-checking Websites And Platform-integrated Tools Such As The “community

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 m...