In the digital age, the news media landscape has undergone a transformative expansion, offering a wide array of sources that serve as chroniclers of people times, shape public opinion, and support the democratic process and crucial pillars supporting the democratic process [1]. This proliferation brings significant challenges for verifying the authenticity of disseminated information [2]. Misinformation is widespread, often fueled by clickbait headlines and polarizing content shared on social media and other platforms [3]. The significant impact of news on our lives is evident in recent global events [4]. Whether it’s staying informed about the pandemic or following the stock market rally, people heavily depend on the news. Studies have found that much of the news on social media is intentionally misleading and is generally categorized under the broader term “fake news” [5]. Fake news is false information presented as news with the intent to mislead. It has been confirmed that spreading fake news impacts sociopolitical domains, human behavior, and the sovereignty of a country [6]. It has also been noted that Artificial Intelligence (AI) is a new tool for detecting and verifying fake news on social media platforms in a short time [7]. In the same line, sophisticated technologies should be used to stop the spread of fake news [8]. The increased use of AI in journalism marks a pivotal shift toward addressing these challenges, making a new era of news creation, dissemination, and consumption [9]. AI now parses large news streams, detects emerging patterns, and can even draft stories, expanding the scale and speed of reporting while raising new risks to accuracy and reliability. Unlike many existing studies that rely on single-run performance metrics, this research establishes a statistically significant benchmark by validating model efficacy across 20 independent runs. The robustness of our results is confirmed through rigorous ANOVA testing, yielding a substantial F-statistic of 2981.91, thereby offering a level of validation and reproducibility often missing in standard deep learning evaluations. The presented study addresses this risk—the spread of fake news—by applying multiple machine learning models to distinguish authentic content from fabrications. Although LSTM models are generally effective for sequential data, their sequence-modeling capability is not leveraged in this specific experimental setting due to the use of TF-IDF features. The results show high-confidence classification of genuine articles, underscoring AI’s practical role in news verification and support of journalistic integrity. Integrating the model into practices is a key step in automating pre-publication checks and enhancing media integrity.

The next sections of this paper is structured as follows. Section 2 delves into existing literature and bridging past research with the present inquiry into AI’s application in news validation. Section 3 outlines computational approach and analytical design, detailing the strategies employed to assess the efficacy of ML models in news classification. The discussion on the experimental hypothesis and modeling setup is prepared in Section 4, which outlines the research hypotheses and the structured framework created. Section 5 presents the empirical results. Finally, Section 6 summarizes key findings and suggests directions for future research in the critical and evolving field.

A recent survey in [10] consolidates empirical fake-news detection studies across major Natural Language Processing (NLP) and applied-AI venues, contrasting classical ML with deep models and focusing on reported algorithms and metrics; it exposes gaps in modality coverage and robustness evaluation, robustness-aware design. ZoFia is a two-stage zero-shot framework that (i) uses Hierarchical Salience with a Soft Cosine with Maximal Marginal Relevance (SC-MMR) selector to retrieve fresh external evidence and (ii) conducts a multi large language model, role-based debate to deliver interpretable judgments; it outperforms zero-shot and many few-shot baselines on two public datasets [11]. The co-attention mechanism in a Combined Graph neural network model (CMCG) fuses two Graph Neural Network (GNN) streams—user profiles and user preferences (news content, user history, and sharing cascades)—linked via a co-attention module to model who shares and what is shared, achieving 98.53% on GossipCop and 96.77% on PolitiFact, underscoring the benefits of profile–preference–propagation fusion over content-only baselines [12]. Tajrian et al. present a structured review of fake-news research, organizing it along two axes: how fake news is analyzed (knowledge, style, propagation, and source) and how it is detected (manual vs. AI-driven automatic methods) [13]. They also examine political news through sentiment analysis, showing how media framing shapes public perception. Furthermore, deep learning has introduced novel approaches to news classification. Zhou and Zafarani [14] used deep neural networks to distinguish between real and fake news stories. Their model demonstrates remarkable accuracy, highlighting the potential of deep learning in enhancing the reliability of news shared on social media platforms. Another pivotal area of investigation within the realm of news classification is the challenge posed by imbalanced datasets, particularly in non-English languages. For Bangla news classification, Hasib et al. balance a 437,948-item corpus using Random Under-Sampling (RUS) and Synthetic Minority Oversampling Technique (SMOTE), then benchmark classical ML (logistic regression, decision tree, Stochastic Gradient Descent (SGD)) against ANN, CNN, and Bidirectional Encoder Representations from Transformers (BERT) [15]. BERT tops performance with 99.04% accuracy on the balanced set vs. 72.23% on the imbalanced set, underscoring both the benefit of rebalancing and the advantage of transformer models [15]. Authors in [16] presented a study focusing on the classification, detection, and sentiment analysis of digital news using ML techniques. Their research addressed several challenges associated with digital news. Their findings show the Fake News Detection Model, with an accuracy of 87%, and a sentiment analysis model, with 89% accuracy. Kumar et al. benchmark classic and neural models (Naive Bayes, SVM, Passive-Aggressive, Random Forest, Logistic Regression, LSTM, BERT) on the ISOT dataset (44,898 samples) for fake-news detection, finding SVM achieves the best precision (99.88%), narrowly ahead of Random Forest and Passive-Aggressive [17]. Mishra and Sadia deliver a comprehensive survey of fake-news detection, synthesizing ML and DL approaches across major social platforms while cataloging datasets, features, and model families. They distill strengths and limitations—e.g., generalization gaps, evolving topics, and modality/propagation cues—providing a clear roadmap of effective techniques and open challenges [18].

Recent developments in the field have been significantly shaped by the advent of Transformer-based architectures. Vaswani et al. introduced the attention mechanism [19], which paved the way for models like BERT [20]. Their study utilized several ML and deep learning techniques to categorize news articles accurately. Their findings demonstrated remarkable results, achieving an accuracy of up to 94%.

The presented work explores various machine learning models to address the nuanced task of classifying text data into fake and true news articles. The suggested approach highlights the use of multiple computational techniques as shown in Fig. 1. Initially, introducing the foundational concepts of ANN, which mimic the human brain’s neural structure to capture complex relationships within the data. Moving forward, elucidating the CNN as a powerhouse in processing grid-like topologies of data, or in the case of this study, vector-based features extracted from text, by learning spatial hierarchies of features. The methodology further extends to LSTMs. Showcasing their exceptional ability to retain information across extended sequences, these models are especially well-suited for tasks involving time-series analysis or the processing of sequential text data.

Additionally, the DT model is explored, which provides a transparent decision-making path through a tree-like structure of decisions and outcomes. Lastly, the SVM-RBF is investigated, renowned for its effectiveness in high-dimensional spaces and non-linear data separability. Each model offers unique perspectives and mechanisms for feature extraction and classification that allow for the assesment of their efficacy individually and collectively. This diversified methodology not only enhances the generalization of this study but also contributes novel insights into the robustness of ML applications to discern the integrity of news articles. As depicted in Fig. 1, the process begins with data input and splitting into training, validation, and testing sets. TF-IDF embedding is used for feature extraction. Five models—ANN, CNN, LSTM, DT, and SVM-RBF—perform classification. Outputs are labeled as real or fake news based on evaluation metrics.

Artificial intelligence tools such as ChatGPT are used in a limited and responsible manner during the writing process of this work. The tools usage is used solely for enhancing the academic style and refining sentence structures. These tools did not contribute to the model design, data analysis, or interpretation of the obtained results. All the methodological decisions, experiments and scientific conclusions were made exclusively by the manuscript authors.

Based on the assumption that advanced ML models—particularly CNNs and LSTMs—are capable of accurately distinguishing between fake and real news articles, this study investigates the subtle textual differences that define each category. It is hypothesized that these models can effectively identify and leverage the differential patterns, stylistic features, and thematic disparities between fake and true news articles for classification purposes. Methodical experimental approach is done by using a diverse set of ML models, specifically ANN, CNN, LSTM, DT, and SVM-RBF, which were trained on the refined data set. Each model’s detailed parameters and architectural specifications are elaborated in Table 1, with selections informed by extensive preliminary tuning to optimize performance. Then employing a standard hold-out strategy, the dataset was partitioned into training (80%) and testing (20%) subsets. This partition facilitates an assessment of the models’ capacity for generalization to novel data. Finally the effectiveness of each model in correctly classifying news articles was measured using evaluation metrics including accuracy, precision, recall, and F1 score, providing a comprehensive perspective on overall performance.

Hypotheses Testing and Statistical Significance Analysis

This section outlines the suggested approach to statistically analyze the classification accuracies of the used ML models (ANN, CNN, LSTM, DT, SVM-RBF) over multiple runs. The aim is to identify whether significant differences exist not only between individual pairs of models but also in a one-to-many comparison fashion, assessing each model against the collective performance of others. To evaluate the performance differences between each pair of models, a series of paired t-tests [30] is employed. This method allows us to compare the mean accuracy scores of two models over 20 runs, assuming paired observations are normally distributed. A significant t-statistic, determined against the critical t-value from the t-distribution table at α=0.05, suggests a significant difference in the performance of the two compared models. For a broader comparison of each model’s performance against the collective performance of others, the Bonferroni-adjusted ANOVA test [31] is utilized. This adjustment is crucial to maintain the overall Type I error rate while conducting multiple comparisons. The ANOVA test was adjusted for multiple comparisons, with the Bonferroni correction factor applied to the significance level α. The F-statistic is calculated then compared against the critical F-value from the F-distribution table at a predetermined significance level (usually α=0.05). A significant F-statistic (exceeding the critical F-value) suggests that at least one model’s mean accuracy is significantly different from the others, warranting rejection of H₀. Given a significant ANOVA result, a post-hoc analysis is conducted to pinpoint which specific models’ means differ significantly. The Tukey’s HSD test [32] is employed for this purpose, offering a pairwise comparison among the models’ accuracy means while controlling for the Type I error rate. Models with a mean difference exceeding the HSD value are considered to have significantly different accuracies, confirming H₁ for those models. The application of the ANOVA test, followed by Tukey’s HSD post-hoc analysis, facilitates a comprehensive understanding of the comparative performance landscape of these ML models in the task of news classification. This statistical framework underscores a commitment to rigour and precision in validating advanced ML techniques for enhancing media integrity and information verification processes. Following identifying significant differences via the adjusted ANOVA test, proceeding with a post-hoc analysis using Tukey’s HSD test to ascertain specific models that differ in performance. The combination of paired t-tests for individual model comparisons, alongside a Bonferroni-adjusted ANOVA for a comprehensive assessment, ensures a thorough statistical examination of the used ML models’ efficacy in classifying news articles. By systematically analyzing performance disparities, this multifaceted statistical approach aims to validate the effectiveness of advanced ML techniques in distinguishing between fake and true news, thereby offering potent tools for media verification and integrity. The ANOVA test is chosen due to its effectiveness in identifying differences in group means in scenarios involving multiple groups.

As detailed in Table 1, the model parameters and architectural nuances are pivotal in contextualizing the experimental findings. The systematic selection of model configurations directly influences the robustness of the classification outcomes. The experimental setup and model training were conducted within a robust computational environment designed to support the intensive processing demands of ML workflows. This section outlines the technical specifications and software stack to facilitate the research. The implementation and testing of ML models were supported by comprehensive hardware, software tools, and libraries. Where the machine learning models were trained using a batch size of 32 and an initial learning rate of 0.001, which was dynamically adjusted via a scheduler based on validation loss. Training proceeded for a maximum of 100 epochs, with early stopping applied based on validation accuracy to mitigate overfitting. Training time varied by model complexity, with CNN and LSTM architectures requiring roughly 2–3 h on the designated GPU hardware. The dataset was randomly partitioned into 80% for training and 20% for testing, maintaining a stratified label distribution across both subsets. Model performance was assessed through a hold-out validation strategy, using test set metrics to evaluate generalizability. All experiments were executed in a controlled environment to ensure consistent and reproducible outcomes.

In this research, an in-depth evaluation is performed to assess the effectiveness of five distinct models: ANN, CNN, LSTMs, DTs, and SVM-RBF. Each model underwent 20 independent training and evaluation runs to ensure a robust assessment of its performance. The accuracy metric was recorded for each run, providing a basis for the subsequent statistical analysis. The obtained findings reveal distinct performance characteristics across the models, with ANN and CNN models demonstrating exceptional capability in classifying news articles. The LSTM model, while slightly less accurate, showcased its proficiency in handling feature-rich data, a critical attribute for text-based tasks. In contrast, the classical ML models, DT and SVM-RBF, lagged in performance, underscoring the challenges these models face with complex, high-dimensional datasets. The experimental runs yielded the following mean accuracies with their corresponding standard deviations (SD) over 20 iterations as summarized in Fig. 7. The bar chart reports mean ±SD for each model (ANN, CNN, LSTM, DT, and SVM–RBF), highlighting the comparative performance across architectures.

The analysis was started with an ANOVA test to confirm whether the observed model performance differences were statistically significant. This test returned an F-statistic of 2981.91 and a p-value less than 0.001. It demonstrates significant differences in model accuracies. The result necessitated additional exploration through post-hoc analysis to identify the specific models among differences that occurred. Using Tukey’s HSD test for pairwise comparison of model accuracies. Differences were significant, as shown in Table 2.

There is no significant performance difference between ANN and CNNs. In contrast, the notable performance differences between these previous models and LSTM, DT, and SVM-RBF indicate that ANN and CNN are more efficient. Importantly, the decision tree model exhibited the weakest performance, and SVM-RBF also underperformed compared to LSTM. It highlights LSTM’s robustness in capturing data dependencies, even though its performance is slightly lower than that of ANN and CNN when utilizing TF-IDF features, which lack explicit sequential time-steps. The comparable effectiveness of both ANN and CNN models suggests that tasks involving complex pattern recognition or high-dimensional data processing could greatly benefit from these architectures. Furthermore, the low performance of LSTM, relative to ANN and CNN, may indicate a trade-off between capturing temporal dependencies and overall predictive accuracy when the input representation is non-sequential. The trade-off is important, particularly in applications where model complexity and training time are significant factors. The classical models, such as DT and SVM-RBF, may not perform as well as CNN and ANN models. The variation in model performance highlights the necessity of choosing a model that aligns with the specific requirements. Table 3 compares with other related studies with various data.

This study set out to tackle a contemporary challenge in digital news verification by employing various machine learning models to distinguish between genuine and fabricated news headlines. Through careful experimentation and thorough data analysis, the presented study demonstrates that AI can effectively enhance the media’s credibility. The results indicate that CNN and ANN achieve the highest accuracy of 99.13%. Crucially, this study validates these findings through extensive statistical testing, including ANOVA and Tukey’s HSD analysis across 20 independent runs. This rigorous evaluation confirms that deep learning architectures significantly outperform classical baselines (such as DT and SVM-RBF) in capturing intricate textual patterns, proving their efficacy even when utilizing standard high-dimensional features like TF-IDF. The superior performance of the ANN and CNN models highlights the increasing significance of neural networks in text classification tasks where robust feature mapping is required. The outcomes of this research hold value beyond the academic realm, offering tangible benefits for journalists, media outlets, and providers of IT solutions. With the predictive capabilities of CNN and LSTM, media stakeholders can implement automated systems to efficiently scan large volumes of news content and identify any spam articles. Technological advancement is crucial for ensuring the prevalence of factual reporting worldwide and fostering informed public discourse. However, while this work represents a significant advancement, it is important to recognize its limitations. The exceptionally high accuracy observed in this study may be partially attributed to the specific linguistic characteristics of the dataset, where fake news often exhibits distinct lexical patterns distinguishable by TF-IDF. Misinformation tactics are becoming increasingly sophisticated, and language constantly evolves, necessitating regular updates to machine learning models and validation across more diverse datasets to prevent overfitting. In the future, the features of news content could be enhanced by incorporating more multimodal data, such as images and videos, to improve the effectiveness of fake news detection systems. Additionally, exploring transfer and unsupervised learning approaches offers significant potential for advancing the automated understanding and classification of news content. The present research can be extended into a critical area of digital journalism that focuses on optimizing machine learning in the increasingly complex landscape of journalistic practice. As a society situated at the intersection of media and technology, the study highlights the advancements made in this domain while emphasizing the need for greater collaboration to push the boundaries of AI while preserving the credibility of news.