Advanced machine learning techniques for fake news detection: A comprehensive analysis

PurposeThe creation and dissemination of fake news can have severe consequences for a company’s brand. Researchers, policymakers and practitioners are eagerly searching for solutions to get us out of the “fake news crisis”. Here, one approach is to use automated tools, such as artificial intelligence (AI) algorithms, to support managers in identifying fake news. The study in this paper demonstrates how AI with its ability to analyze vast amounts of unstructured data, can help us tell apart fake and real news content. Using an AI application, this study examines if and how the emotional appeal, i.e., sentiment valence and strength of specific emotions, in fake news content differs from that in real news content. This is important to understand, as messages with a strong emotional appeal can influence how content is consumed, processed and shared by consumers.Design/methodology/approachThe study analyzes a data set of 150 real and fake news articles using an AI application, to test for differences in the emotional appeal in the titles and the text body between fake news and real news content.FindingsThe results suggest that titles are a strong differentiator on emotions between fake and real news and that fake news titles are substantially more negative than real news titles. In addition, the results reveal that the text body of fake news is substantially higher in displaying specific negative emotions, such as disgust and anger, and lower in displaying positive emotions, such as joy.Originality/valueThis is the first empirical study that examines the emotional appeal of fake and real news content with respect to the prevalence and strength of specific emotion dimensions, thus adding to the literature on fake news identification and marketing communications. In addition, this paper provides marketing communications professionals with a practical approach to identify fake news using AI.

Fake news detection on job advertisements has grabbed the attention of many researchers over past decade. Various classifiers such as Support Vector Machine (SVM), XGBoost Classifier and Random Forest (RF) methods are greatly utilized for fake and real news detection pertaining to job advertisement posts in social media. Bi-Directional Long Short-Term Memory (Bi-LSTM) classifier is greatly utilized for learning word representations in lower-dimensional vector space and learning significant words word embedding or terms revealed through Word embedding algorithm. The fake news detection is greatly achieved along with real news on job post from online social media is achieved by Bi-LSTM classifier and thereby evaluating corresponding performance. The performance metrics such as Precision, Recall, F1-score, and Accuracy are assessed for effectiveness by fraudulency based on job posts. The outcome infers the effectiveness and prominence of features for detecting false news. .

The phenomenon of Fake news is experiencing a rapid and growing progress with the evolution of the means of communication and Social media. Fake news detection is an emerging research area which is gaining big interest. It faces however some challenges due to the limited resources such as datasets and processing and analysing techniques. In this work, we propose a system for Fake news detection that uses machine learning techniques. We used term frequency-inverse document frequency (TF-IDF) of bag of words and n-grams as feature extraction technique, and Support Vector Machine (SVM) as a classifier. We propose also a dataset of fake and true news to train the proposed system. Obtained results show the efficiency of the system. In this work, we propose a system for Fake news detection that uses machine learning techniques. We used term frequency-inverse document frequency (TF-IDF) of bag of words and n-grams as feature extraction technique, and Support Vector Machine (SVM) as a classifier. We propose also a dataset of fake and true news to train the proposed system. Obtained results show the efficiency of the system.

Fake news is a growing problem on social media and can have significant negative consequences for individuals and society owing to its accessibility, low cost, and quick distribution. It is difficult to automatically identify bogus news that defies the current content-based analysis techniques. One of the key reasons is that current NLP algorithms still lack common sense, which is frequently necessary for understanding how to read the news. Recent research has demonstrated that the propagation patterns of true and fake news differ on social media. With the potential for automatic fake news detection, this study investigates the use of Deep Learning (DL) models to detect fake news on social media. A Neural Network (NN) model was developed using Natural Language Toolkit (NLTK), TensorFlow, and Natural Language Processing (NLP) for textual analysis. The model was trained on a dataset of fake and real news articles, and was able to achieve high accuracy in identifying fake news. The results of this study suggest that DL models can be valuable tools for detecting fake news on social media.

Due to the widespread use of social media, people are exposed to fake news and misinformation. Spreading fake news has adverse effects on both the general public and governments. This issue motivated researchers to utilize advanced natural language processing concepts to detect such misinformation in social media. Despite the recent research studies that only focused on semantic features extracted by deep contextualized text representation models, we aim to show that content-based feature engineering can enhance the semantic models in a complex task like fake news detection. These features can provide valuable information from different aspects of input texts and assist our neural classifier in detecting fake and real news more accurately than using semantic features. To substantiate the effectiveness of feature engineering besides semantic features, we proposed a deep neural architecture in which three parallel convolutional neural network (CNN) layers extract semantic features from contextual representation vectors. Then, semantic and content-based features are fed to a fully connected layer. We evaluated our model on an English dataset about the COVID-19 pandemic and a domain-independent Persian fake news dataset (TAJ). Our experiments on the English COVID-19 dataset show 4.16% and 4.02% improvement in accuracy and f1-score, respectively, compared to the baseline model, which does not benefit from the content-based features. We also achieved 2.01% and 0.69% improvement in accuracy and f1-score, respectively, compared to the state-of-the-art results reported by Shifath et al. (A transformer based approach for fighting covid-19 fake news, arXiv preprint arXiv:2101.12027, 2021). Our model outperformed the baseline on the TAJ dataset by improving accuracy and f1-score metrics by 1.89% and 1.74%, respectively. The model also shows 2.13% and 1.6% improvement in accuracy and f1-score, respectively, compared to the state-of-the-art model proposed by Samadi et al. (ACM Trans Asian Low-Resour Lang Inf Process, https://doi.org/10.1145/3472620, 2021).

Filtering, vetting, and verifying digital information is an area of core interest in information science. Online fake news is a specific type of digital misinformation that poses serious threats to democratic institutions, misguides the public, and can lead to radicalization and violence. Hence, fake news detection is an important problem for information science research. While there have been multiple attempts to identify fake news, most of such efforts have focused on a single modality (e.g., only text‐based or only visual features). However, news articles are increasingly framed as multimodal news stories, and hence, in this work, we propose a multimodal approach combining text and visual analysis of online news stories to automatically detect fake news. Drawing on key theories of information processing and presentation, we identify multiple text and visual features that are associated with fake or credible news articles. We then perform a predictive analysis to detect features most strongly associated with fake news. Next, we combine these features in predictive models using multiple machine‐learning techniques. The experimental results indicate that a multimodal approach outperforms single‐modality approaches, allowing for better fake news detection.

The exponential growth of digital media and social networking platforms has transformed the way information is created and consumed. While these platforms provide rapid access to news, they have also facilitated the widespread dissemination of fake news. Fake news refers to intentionally false or misleading information presented as authentic news, which can influence public opinion, disrupt social harmony, and erode trust in media. Traditional manual fact-checking approaches are time-consuming and inadequate for handling the massive volume of online content. Therefore, automated fake news detection systems are essential. This paper proposes a machine learning-based approach for detecting fake news using Natural Language Processing (NLP) techniques. The proposed system pre-processes textual data and applies feature extraction methods such as Bag of Words (BoW) and Term Frequency–Inverse Document Frequency (TF-IDF). Multiple machine learning classifiers, including Logistic Regression, Naïve Bayes, Random Forest, and Support Vector Machine, are trained and evaluated. The performance of these models is assessed using accuracy, precision, recall, and F1-score. Experimental results demonstrate that classical machine learning models can effectively classify news articles as fake or real with reliable accuracy. The proposed system offers a lightweight and efficient solution for fake news detection and can be extended to real-time applications in the future.

This project investigates how Artificial Intelligence (AI), specifically supervised machine learning techniques, can be applied to detect and classify fake news with high accuracy. The motivation stems from the widespread dissemination of misinformation on social media, where false narratives often spread faster than verified content. To address this issue, two distinct classification models were implemented and evaluated: one combining TF-IDF vectorization with Random Forest classifier, and another using TF-IDF with Logistic Regression. The TF-IDF technique was used to convert raw textual data into meaningful numerical features, capturing word frequency and relevance within the corpus. Both models were trained and tested on a balanced dataset of true and fake news articles. Their performance was compared based on key metrics such as accuracy, precision, recall, and F1-score. The findings revealed that the Random Forest model consistently outperformed Logistic Regression, particularly when trained on uncleaned (raw) text. This suggests that Random Forest is more robust to linguistic noise, making it a more suitable choice for deployment in real-time fake news detection systems, especially on user-generated content platforms like social media.

Due to the widespread use of social networks by people of all ages, distinguishing between fake and real news on these platforms has become a significant challenge in today's world. Individuals who disseminate fake news on social networks often aim to achieve various commercial, political, and economic goals. Therefore, identifying and distinguishing real news from fake news is crucial in addressing this issue. The objective of this research is to present an intelligent model for detecting fake news using a news propagation tree in the social network Twitter. The dataset used in this study is sourced from the political news section of the Fake News Net website. Initially, a news propagation tree was constructed for both real and fake news using this dataset, followed by the development of features from structural, temporal, and syntactic perspectives based on the news propagation tree. Finally, machine learning algorithms were employed to build a model for predicting fake and real news. The results indicated that among the algorithms used for modeling, the Random Forest algorithm, with an accuracy of 75.8%, was the best model for distinguishing fake news from real news.

Fake news analysis and detection is a method that requires using machine learning classifiers to recognize and detect news content as fake or real. No research work has been carried out on fake news analysis and detection using the machine learning algorithms using Truncated singular value decomposition (TSVD) on the ‘ISOT Fake News Dataset’ but there is a need for a better design that can provide better accuracy. This research aims to develop and use systematic progression in identifying fake news analysis using machine learning algorithms with TSVD. The machine learning algorithms which include the light Gradient Boosting Machine, Cat boosting classifier, and support vector machine with TSVD were used on ‘The ISOT Fake News Dataset’ to recognize the fake news from real news in the dataset, thereby improving the performance of these classifiers by cleaning the data to manage the data imbalances more efficiently. The recommended design yields an exactness of 97.33%. The postulated technique out classifies the current classifiers which possess an exactness of 95.05%. We recommend that Machine learning classifiers using TSVD should be applied in Fake News analysis and detection since it gives a better performance accuracy in fake news analysis and detection

Nowadays, with the advancement of technology, the use of news sources has also undergone a great evolution. News sources have constantly evolved from past to present, ranging from magazines to radios, from newspapers to televisions. The fact that it has become so easy to access news has caused society to pay more attention to fake news. Fake news has the ability to influence society through news sources such as social media, which can reach wider audiences with the development of technology. The difficulties of users in accessing accurate and reliable sources in this information flow that shapes their daily lives increases the potential for the spread of fake news, and it becomes increasingly difficult to distinguish between real and fake news. In this study, classification models for fake news detection were designed using machine learning algorithms. The dataset, which includes fake and real news examples, contains 42,000 examples. Each class, including fake and real samples, contains 22,000 sample data. In order to increase data quality, accuracy and usability, preprocessing methods were applied to the data set. The removal of numbers, stop words, and html tags was done in the pre-processing step to remove unnecessary information from the text. Models were created for fake news detection with singular and ensemble classification algorithms. Performance evaluation of the models was performed using 5-fold cross-validation. In the performance comparisons of the models, values such as accuracy, sensitivity, specificity, tp rate and fp rate were calculated. The highest performance results were observed in the random forest classification algorithm with an accuracy rate of 76%.

Recently, fake news has been widely spread through the Internet due to the increased use of social media for communication. Fake news has become a significant concern due to its harmful impact on individual attitudes and the community’s behavior. Researchers and social media service providers have commonly utilized artificial intelligence techniques in the recent few years to rein in fake news propagation. However, fake news detection is challenging due to the use of political language and the high linguistic similarities between real and fake news. In addition, most news sentences are short, therefore finding valuable representative features that machine learning classifiers can use to distinguish between fake and authentic news is difficult because both false and legitimate news have comparable language traits. Existing fake news solutions suffer from low detection performance due to improper representation and model design. This study aims at improving the detection accuracy by proposing a deep ensemble fake news detection model using the sequential deep learning technique. The proposed model was constructed in three phases. In the first phase, features were extracted from news contents, preprocessed using natural language processing techniques, enriched using n-gram, and represented using the term frequency–inverse term frequency technique. In the second phase, an ensemble model based on deep learning was constructed as follows. Multiple binary classifiers were trained using sequential deep learning networks to extract the representative hidden features that could accurately classify news types. In the third phase, a multi-class classifier was constructed based on multilayer perceptron (MLP) and trained using the features extracted from the aggregated outputs of the deep learning-based binary classifiers for final classification. The two popular and well-known datasets (LIAR and ISOT) were used with different classifiers to benchmark the proposed model. Compared with the state-of-the-art models, which use deep contextualized representation with convolutional neural network (CNN), the proposed model shows significant improvements (2.41%) in the overall performance in terms of the F1score for the LIAR dataset, which is more challenging than other datasets. Meanwhile, the proposed model achieves 100% accuracy with ISOT. The study demonstrates that traditional features extracted from news content with proper model design outperform the existing models that were constructed based on text embedding techniques.

The dissemination of fake news in an online social network platform has been a real concern nowadays. Through social media, news articles are posted by many sources like news channels, websites, or even newspaper websites. There is a need to be sure that the information posted is only from credible sources and these posts have to authenticate. The intensity of genuineness of the news posted online cannot be measured absolutely and is still challenging. A novel Score -based Multi-Source Fake News Detection framework is proposed in this work to automate the detection of fake news from multiple news sources. This framework extracts the text-based features from genuine and fake news articles using Term Frequency - Inverted Document Frequency. Then the credibility score of the source s is calculated based on the site_url features and Top Level Domain. By assimilating the text-based features with the credibility score of multi-source, the credibility of the news is estimated. The proposed framework is applied to the Machine Learning (ML) classifiers to examine their performance in the detection of fake news. The experimental results determine the efficacy of the proposed framework with the Gradient Boosting algorithm of about 99.5% to the utmost level.

The proliferation of fake news across online media platforms poses significant challenges to public trust,societal harmony,and information integrity.Many of the things that people read online, particularly on social media, seem true, but they are frequently not. Fake news is defined as news, articles, or hoaxes that are purposefully created to mislead or deceive readers..This study develops a machine learning-based system for detecting fake news on digital platforms. Utilizing algorithms such as K-Nearest Neighbors (KNN), Naive Bayes, Logistic Regression, Random Forest, Gradient Boosting and Support Vector Machines (SVM), the system processes textual data using natural language processing (NLP) techniques like term frequency-inverse document frequency (TF-IDF) and bag-of- words for feature extraction.The performance of each algorithm is evaluated using metrics such as accuracy, precision, recall, and F1-score to identify the most effective model. By identifying the most effective machine learning model, this research aims to provide a scalable solution that can help reduce misinformation and enhance the credibility of online information.By distinguishing the best A1 model,this expectation expects to give a versatile arrangement that can assist with lessening deception and improve the believability of online data. Keywords- News and Fake,Natural Language Processing,Feature Extraction,a classification and Model Evaluation

SummaryIn the present epoch of computing, the world has changed from older conventional print media to social platform channels. Fake news articles have the prospects to handle the opinions of the public and so may harm human groupings. Therefore, it is necessary to explore the authenticity and credibility of the news flash being shared on the internet community. Hence, this research paper devises an efficient and robust fake news detection model, named Exponential Chimp Optimization Algorithm (EChOA)‐based Deep Neuro‐Fuzzy Network (DNFN) for detecting fake news. The introduced model utilizes a MapReduce framework that includes the mapper and reducer phases for processing big data for detecting fake news. First phase of processing is the Mapper work, in which every input used in the database is processed and creates an intermediate key‐value pair. In the reducer phase, the fusion of features is performed by arranging the features with the help of computing the optimal parameter and Rand similarity coefficient using a Deep Q Network (DQN). Here, the detection of fake news is obtained by DNFN, and the DNFN is done using implemented EChOA. The EChOA‐based DNFN effectively generates robust and effective fake news detection performance by choosing the optimal feature subsets through feature fusion. The EChOA is designed by integrating the Exponential Weighted Moving Average (EWMA) and Chimp Optimization Algorithm (ChOA). Moreover, the EChOA‐based DNFN method outperformed various former fake news detection approaches and attains the highest performance based on the testing accuracy is 0.909, sensitivity is 0.937, and specificity is 0.891 using the FakeNewsNet dataset.