Detecting Fake News And Disinformation Using Artificial Intelligence

You have full access to this open access article Fake news and disinformation (FNaD) are increasingly being circulated through various online and social networking platforms, causing widespread disruptions and influencing decision-making perceptions. Despite the growing importance of detecting fake news in politics, relatively limited research efforts have been made to develop artificial intelligence (AI) and machine learning (ML) oriented FNaD detection models suited to minimize supply... Using a combination of AI and ML, and case studies based on data collected from Indonesia, Malaysia, and Pakistan, we developed a FNaD detection model aimed at preventing SCDs. This model based on multiple data sources has shown evidence of its effectiveness in managerial decision-making. Our study further contributes to the supply chain and AI-ML literature, provides practical insights, and points to future research directions.

Avoid common mistakes on your manuscript. The increased scholarly focus has been directed to fake news detection given their widespread impact on supply chain disruptions, as was the case with the COVID-19 vaccine. Fake news and misinformation are highly disruptive, which create uncertainty and disruptions not only in society but also in business operations. Fake news and disinformation-related problems are exacerbated due to the rise of social media sites. Regarding this, using artificial intelligence (AI) to counteract the spread of false information is vital in acting against disruptive effects (Gupta et al., 2021). It has been observed that fake news and disinformation (FNaD) harm supply chains and make their operation unsustainable (Churchill, 2018).

According to research, fake news can be classified into two distinct concepts of misinformation and disinformation (Petratos, 2021; Allcott & Gentzkow, 2017) defined fake news as “news articles that are intentionally and verifiably false,... 213). According to Wardle (2017), misinformation refers to “the inadvertent sharing of false information”, while disinformation can be defined as “the deliberate creation and sharing of information known to be false”. Among the negative consequences that fake news can have for companies are loss of sponsorships, reduced credibility, and loss of reputation which can adversely affect performance (Di Domenico et al., 2021). In such a context AI is shaping decision-making in an increasing range of sectors and could be used to improve the effectiveness of fake news timely detection and identification (Gupta et al., 2021). Whereas many new efforts to develop AI-based fake news detection systems have concentrated on the political process, the consequences of FNaD on supply chain operations have been relatively underexplored (Gupta et al., 2021).

Kaplan and Haenlein (2019) addressed AI “as a system’s ability to interpret external data correctly, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation”... Although emerging technologies such as AI may sometimes have negative effects, they can be utilized to combat disinformation. As scholarship is showing increasing interest in how AI can improve operationally and supply chain efficiencies (Brock & von Wangenheim, 2019), researchers have recently called for more studies on how organizational strengths and the... Fake news has considerable negative effects on firms’ operations, such as repeated disruptions of supply chains (Churchill, 2018). FNaD influence the use of a company’s product or services (Zhang et al., 2019; Sohrabpour et al., 2021) argued that leveraging AI to improve supply chain operations will likely improve firms’ planning, strategy, marketing,... Our research integrity and auditing teams lead the rigorous process that protects the quality of the scientific record

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 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.

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. AI makes it easier to create disinformation, false or decontextualized content, and to spread it quickly through existing channels. (Photo: Canva) In an information ecosystem where misinformation circulates faster than fact-checkers can respond, increasingly precise and efficient tools are needed to verify content, detect hoaxes and understand how false narratives spread. The following list brings together five tools that media outlets and fact-checking organizations use for tasks ranging from tracking disinformation and analyzing its dissemination patterns, to recovering deleted content and analyzing audiovisual material.

Fact Check Explorer allows users to insert a phrase, piece of data or a link to check if someone has already verified it. (Photo: Screenshot) Google has developed an ecosystem of fact-checking tools, some for fact-checkers specifically and others for the general public. The flagship tool is Fact Check Explorer, a specialized search engine that compiles claim reviews from multiple fact-checking organizations worldwide, including Chequeado (Argentina), Bolivia Verifica (Bolivia), El Sabueso (Mexico) and Cotejo.info (Venezuela). Despite being used to create deepfakes, AI can also be used to combat misinformation and disinformation. Image: Gilles Lambert/Unsplash

The proliferation of artificial intelligence (AI) in the digital age has ushered in both remarkable innovations and unique challenges, particularly in the realm of information integrity. AI technologies, with their capability to generate convincing fake texts, images, audio and videos (often referred to as 'deepfakes'), present significant difficulties in distinguishing authentic content from synthetic creations. This capability lets wrongdoers automate and expand disinformation campaigns, greatly increasing their reach and impact. However, AI is not a villain in this story. It also plays a crucial role in combating disinformation and misinformation. Advanced AI-driven systems can analyse patterns, language use and context to aid in content moderation, fact-checking and the detection of false information.

People Also Search

• Detecting fake news and disinformation using artificial intelligence ...
• Can AI Outsmart Fake News? Detecting Misinformation with AI Models in ...
• The use of artificial intelligence in counter-disinformation: a world ...
• PDF Using Artificial Intelligence to Detect and Mitigate Fake News
• From Misinformation to Insight: Machine Learning Strategies for Fake ...
• A hybrid deep learning framework for fake news detection using LSTM ...
• Mitigating Misinformation: A Machine Learning Model for Detecting Fake News
• Five tools to detect, analyze and counter disinformation
• How AI can also be used to combat online disinformation
• Detect Deepfake Threats: Deepfakes in Disinformation Explained