Detecting Fake News Using AI

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 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.

The rise of fake news has become a significant global concern, undermining public trust and information integrity. This study explores the application of advanced machine learning algorithms for detecting fake news, leveraging a balanced dataset of real and fake news articles. Through rigorous preprocessing, including text cleaning and Term Frequency-Inverse Document Frequency (TF-IDF) vectorization, the study enhances data quality and model performance. Five machine learning models—Random Forest, Support Vector Machine (SVM), Neural Networks, Logistic Regression, and Naïve Bayes—are systematically evaluated using metrics such as accuracy, precision, recall, and F1-score. Results indicate that the Random Forest Classifier outperforms other models with an accuracy of 99.95% and balanced performance across metrics, demonstrating its robustness in distinguishing fake from real news. SVM and Neural Networks also achieve high accuracy, showcasing their capability in handling complex data. Logistic Regression and Naïve Bayes, while computationally efficient, exhibit relatively lower performance. The findings underscore the importance of ensemble methods and sophisticated preprocessing techniques in detecting fake news effectively. This research provides a methodological framework for scalable fake news detection, offering valuable insights for developing automated systems to combat misinformation and promote informed decision-making in the digital age.

Head is Where the Herd is: Fake News and Effect of Online Social Conformity on Islamophobia in Indians

Nowadays, current events related to diverse fields are published in newspapers, shared on social media and broadcasted on radio and television. The explosive growth in online news contents has made it very difficult to discriminate between real and fake. As a result, fake news has become prevalent and immensely challenging to analyze and verify. Indeed, it is a big challenge to the government and public to debate the situation depending on case to case. For this purpose, a mechanism has to be taken on fact-checking rumors and statements particularly those that get thousands of views and likes before being debunked and refuted by expert sources. Various machine learning techniques have been used to detect and classify fake news. However, these approaches are restricted in terms of accuracy. This study has applied a random forest (RF) classifier to predict fake or real news. For this purpose, twenty-three (23) textual features are extracted from ISOT Fake News Dataset. Four best feature selection techniques like chi2, univariate, information gain and feature importance are used to select fourteen best features out of twenty-three. The proposed model and other benchmark techniques are evaluated on benchmark dataset using best features. Experimental findings show that the proposed model outperformed state-of-the-art machine learning techniques such as GBM, XGBoost and Ada Boost Regression Model in terms of classification accuracy.

A fake news story can travel half way across the world as the truth puts on its socks. There are myriads of challenges facing journalists in spotting fake news hence its wide proliferation. Fake news has become a prominent subject of enquiry especially following its alleged influence of the 2016 general elections in US. Unfortunately, research on fake news has focused on social media, politics, elections, and economies. Few studies have focused on the challenges that TV journalists face in spotting fake news prompting this study. The specific research question was; what are the challenges facing television journalists in spotting fake news in Kenya? The study adapted a relativist-constructivist/interpretivist ontology and epistemology, qualitative approach and multiple case study methodology. Data was generated through in-depth interviews, direct observation and documents review. The study used purposive sampling to generate data from 16 journalists. Data was then analysed in themes and presented in narrative form. Key findings were that in spotting fake news, journalists faced challenges like; loss of viewers, lack of authoritative contacts, sources who gave fake news for personal, business, political, and economic benefits, ability of fake news to camouflage real news, speed of fake news, typologies of fake news, live reporting, inexperienced correspondents and interns, and social media. The study concludes that the challenges facing journalists in spotting fake news were majorly based on sources, technology, education, skills and training, and its typology. The study therefore recommends that editorial boards invest in experts to train journalists on styles, architecture, propagation and use of fake news, inoculation of journalists and audiences, raising fake news literacy levels, and use of technology based approaches like reverse search and fact checking sites.
 Key words: Fake news, journalists, spotting, challenges, television, Kenya

Fake news detection within online social media using supervised artificial intelligence algorithms

This study is to help readers to understand detection of fake news using machine learning. The main purpose of the planned system is to build an application which identifies fake news stories from a bunch of news stories to make people aware of fake news rumours. With the help of machine learning algorithms, we can detect and separate out the fake news. Nowadays, it is become harder to identify the original source of news stories, like looking for a needle in a haystack. In the modern world, news is a kind of communication that keeps us up to date on the latest events, topics, and people in the wider globe. A society relies on news for a variety of reasons, the most important of which is informing its members about events taking on in and around them that might influence them. Oral and traditional media, as well as digital communication methods, altered videos, memes, unconfirmed marketing, and social media have all contributed to the spread of rumors. As nowadays many people use social media in many cases people get wrong and misleading information and people share those stories without verifying whether it is real or fake news stories. Spreading false information on social media has become a major problem these days. That is why we need a system that can tell us whether something is false news or not. Applications are: 1. Fake news may be detected on social media using this approach. 2. The system can be used to help news channels to broadcast only real and classified news. 3. Users can easily detect and eliminate fake articles that contain misinformation intended to mislead readers.

Misinformation, disinformation, malinformation, and/or fake news have gained attention for good and bad in South Africa, especially since the COVID-19 pandemic. The research-based and non-research-based interventions to tackle misinformation have also been slowly gaining traction, particularly through fact checkers, fake news reporting systems such as those by real411, research on automated systems to detect fake news online using machine learning, sentiment analysis of fake news, tagging of fake news data, and so on. Nevertheless, the spread of misinformation and/or fake news still represents a serious threat and challenge to social media platform owners, citizens, lawmakers, governments, and businesses alike. We hypothesized that the awareness, engagement, influence, and impact levels of misinformation on citizens, politicians, journalists, and lawmakers are relatively low, especially in South Africa. However, no sufficient research has been done in this area to understand engagements, awareness, and reporting of fake news online. This research uses open-source intelligence and selected machine learning techniques to analyse publicly collected social media data to monitor and measure the awareness and engagements of fake news in South Africa over a period of 30 days. The research further identifies key drivers of spreading or reporting misinformation online. We conclude that misinformation engagements on social media in South Africa are active, but only in affluent regions and influenced by mobile device users, who are mostly male. The study recommends further research that may support raising misinformation awareness and positive engagements on social media.

This paper argues that the harm of fake news lies partly in its inequality-reinforcing effect. It shows how fake news interacts with two background inequalities, namely the unequal distribution of opportunities to become competent knowers and the unequal social status of oppressed minorities. First, I argue that fake news reinforces epistemic inequality among its recipients. The epistemic harm of believing fake news does not uniformly affect all members of society. Instead, epistemic inequality makes some citizens more vulnerable to believing fake news. In turn, believing fake news amplifies epistemic inequality by making those who fall for fake news increasingly less competent as knowers. Second, I argue that fake news amplifies status inequality within society by downgrading the status of minorities who are negatively represented in fake news stories. A significant subset of fake news targets minorities who are not recognized as equal within society, such as immigrants, Muslims and Roma minorities. The harm of being negatively portrayed in anti-minority fake news stories falls asymmetrically on members of the targeted minorities, reinforcing their oppression. Focusing on anti-immigrant fake news as the most widespread and paradigmatic example of anti-minority fake news, I show how it amplifies cultural imperialism, marginalization and systemic violence against members of immigrant minorities. Anti-minority fake news is also specifically harmful to members of targeted minorities as political agents because it contributes to denying their equal status as members of the democratic political community. This subset of fake news stories is therefore particularly corrosive to the democratic system and its core values.

Research background: Social media plays a crucial role today in enhancing or limiting how fake news is spread. Whether devised by man or developed by artificial intelligence, it has the power to rapidly change consumers’ minds, encouraging them to adopt new behaviors, perceive situations differently, or even act in total opposition to what might be expected. The new dynamics of communication highlights the need for an organizational response adapted to new AI technologies and to the dissemination of fake news within social media networks. Purpose of this article: This paper aims to reveal, by means of bibliometric analysis and a systematic literature review, the generative capabilities of artificial intelligence in the creation and spread of fake news in the business environment, acknowledging the role of previous research in predicting accurately the constant developments in contemporary society. Methods: The analysis is based on a PRISMA flowchart to examine how artificial intelligence technologies contribute to the creation of fake news whilst also highlighting potential artificial intelligence regulations and standards for limiting the dissemination of false information. Initially, the database included over 3,400 highly cited articles retrieved from Scopus and Web of Science, published in the last years, from which a total of 203 were selected for inclusion in the analysis. The bibliometric analysis follows research directions related to detection methods and strategies, legislation and policies governing artificial intelligence technologies used in the creation and dissemination of fake news connected to the business environment. Fake news typologies relating to the advancement of artificial intelligence new technologies are also explored. Findings & value added: By analysing important phrases, including false information, misinformation, disinformation, mal-information, and deepfakes, this research investigates the categorization of fake news linked to the business environment and social media concepts. It underscores the need for better truth comprehension and the significance of fact-checking in preventing the spread of false information, with governance and institutional implications in terms of the economics of artificial intelligence-generated fake news in the social media market. While previous studies have examined the fake news phenomenon from several angles, there is still a research gap, as the literature concentrates more on how fake news is consumed rather than how it is created. This research aims to bridge the gap by providing a comprehensive examination of fake news research from the perspectives of fake news typology, creation, detection, and regulatory means.

An overview of online fake news: Characterization, detection, and discussion

In the present era, social media platforms such as Facebook, WhatsApp, Twitter, and Telegram are significant sources of information distribution, and people believe it without knowing their origin and genuineness. Social media has fascinated people worldwide in spreading fake news due to its easy availability, cost-effectiveness, and ease of information sharing. Fake news can be generated to mislead the community for personal or commercial gains. It can also be used for other personal benefits such as defaming eminent personalities, amendment of government policies, etc. Thus, to mitigate the awful consequences of fake news, several research types have been conducted for its detection with high accuracy to prevent its fatal outcome. Motivated by the aforementioned concerns, we present a comprehensive survey of the existing fake news identification techniques in this paper. Then, we select Machine Learning (ML) models such as Long-Short Term Memory (LSTM), Passive Aggressive Algorithm, Random Forest (RF), and Naive Bayes (NB) and train them to detect fake news articles on the self-aggregated dataset. Later, we implemented these models by hyper tuning various parameters such as smoothing, drop out factor, and batch size, which has shown promising results in accuracy and other evaluation metrics such as F1-score, recall, precision, and Area under the ROC Curve (AUC) score. The model is trained on 6335 news articles, with LSTM showing the highest accuracy of 92.34% in predicting fake news and NB were showing the highest recall. Based on these results, we propose a hybrid fake news detection technique using NB and LSTM. At last, challenges and open issues along with future research directions are discussed to facilitate the research in this domain further.

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%.

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.