From Misinformation To Insight Machine Learning Strategies For Fake

Bonisiwe Shabane

-Dec 5, 2025, 11:19 PM

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 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to... The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from... To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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. Keywords: Fake news, Disinformation, Misinformation, Artificial intelligence, Machine learning, Supply chain disruptions, Effective decision making 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,... Most of the works used linguistics features to train the models; however, some works used visual features [111–113] or social features [114–116]. There are some studies that used multi-view learning approach [38,41,79,112,117–122]. Table 2 shows classification methods used in previous works in fake news detection.

The concept of untrue information spread deliberately, in hopes people would not tell it from a genuine piece of news, has entered the collective consciousness. In the recent years, fake news has reportedly poisoned democratic elections, raised doubts in the decisions made by governments, tarnished the reputation of individuals and organizations alike, and so on [2,4]. Researchers warn that there is a major concern to be had, as fake news has the limitless potential to gravely impact both individuals and societies [5], making the internet news outlets “become a powerful... 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. You have full access to this open access article

Social media usage has reached its peak across all age groups, resulting in vast quantities of data being generated daily, some internet users disseminate fake information for their own benefit. Manually checking each user profile is time-consuming, necessitating automated solutions. This paper addresses multimodal fake news detection, examining evolving landscape of fake information on social media. It highlights the limitations of techniques that rely on single modality. This review underscores the necessity of automated systems capable of detecting and monitoring fake information in multimodal content on social media platforms. The variety of multimodal datasets, tools, and machine learning techniques used to determine the authenticity and virality of online social media content.

The research outlines standard performance indicators to evaluate content, aiming to enhance the accuracy of this ongoing global issue. Furthermore, this study highlights the main challenges that are identified and suggests directions for future research to mitigate its harmful social impact. This paper emphasizes the importance of automating the verification of online information to ensure authenticity, thereby contributing to a more secure digital atmosphere. Avoid common mistakes on your manuscript. The internet provides access to a wealth of information through social media platforms, which enable communication with other users. Some people create fake content on social media platforms to gain financially, often taking advantage of features, such as likes, shares, and comments [1].

Although not all online content is reliable, many people believe that shared information is true. Individuals create and share fake news consisting of misleading information that simulates real media content. A small piece of shared misinformation will quickly gain attention and published within no time, which reflects the current trend on social media platforms [2]. Fake news originates from various sources and aims to influence readers’ perceptions of people or ideas [3]. Fake news is disseminated in different domains such as political, healthcare, business, and entertainment. This diffusion of information is impacting on society and public trust across social media platforms [4].

There are some instances that have gone through discussions. For example, in 1835, the “The Sun” newspaper article, which is called the “moon hoax”, was the first fake news published that mentioned there were human-like creatures on the moon [5]. In the 2016 U.S. democratic presidential campaign, there was a lot of fake stories shared on social media which gained significant attention and people changed opinions about the election [6]. Fake news has become a significant issue in today’s digital world, where misinformation spreads rapidly across social media and news platforms. Machine learning provides an effective way to detect fake news by analyzing patterns, linguistic features, and sources.

This article explores how to detect fake news using machine learning, covering the steps involved, commonly used algorithms, datasets, and real-world applications. Fake news refers to misleading or false information presented as legitimate news. It includes: Detecting fake news is challenging because it often mimics real news in style and structure but contains false or misleading claims. Machine learning models can analyze text, sources, and context to classify news articles as real or fake. These models rely on: ✅ Natural Language Processing (NLP) – Analyzing text patterns, sentiment, and readability.✅ Supervised learning – Training models using labeled datasets of real and fake news.✅ Deep learning – Advanced AI...

In the digital age, the rapid proliferation of misinformation and disinformation poses a critical challenge to societal trust and the integrity of public discourse. This study presents a comprehensive machine learning framework for fake news detection, integrating advanced natural language processing techniques and deep learning architectures. We rigorously evaluate a diverse set of detection models across multiple content types, including social media posts, news articles, and user-generated comments. Our approach systematically compares traditional machine learning classifiers (Naïve Bayes, SVMs, Random Forest) with state-of-the-art deep learning models, such as CNNs, LSTMs, and BERT, while incorporating optimized vectorization techniques, including TF-IDF, Word2Vec, and contextual... Through extensive experimentation across multiple datasets, our results demonstrate that BERT-based models consistently achieve superior performance, significantly improving detection accuracy in complex misinformation scenarios. Furthermore, we extend the evaluation beyond conventional accuracy metrics by incorporating the Matthews Correlation Coefficient (MCC) and Receiver Operating Characteristic–Area Under the Curve (ROC–AUC), ensuring a robust and interpretable assessment of model efficacy.

Beyond technical advancements, we explore the ethical implications of automated misinformation detection, addressing concerns related to censorship, algorithmic bias, and the trade-off between content moderation and freedom of expression. This research not only advances the methodological landscape of fake news detection but also contributes to the broader discourse on safeguarding democratic values, media integrity, and responsible AI deployment in digital environments. Terms of use | Privacy policy | Copyright © 2025 Farlex, Inc. | Feedback | For webmasters |

From Misinformation To Insight Machine Learning Strategies For Fake

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Mouratidis, D.; Kanavos, A.; Kermanidis, K. From Misinformation To Insight:

Information. 2025; 16(3):189. Https://doi.org/10.3390/info16030189 Mouratidis, Despoina, Andreas Kanavos, And Katia

3: 189. Https://doi.org/10.3390/info16030189 Mouratidis, D., Kanavos, A., & Kermanidis, K.

Https://doi.org/10.3390/info16030189 Open AccessThis Article Is Licensed Under A Creative Commons

Despite The Growing Importance Of Detecting Fake News In Politics,