Fake News Classification Model Using Machine Learning

Easy access to equipment, software, and platforms to create, distribute, and provide access to fake news stories has exacerbated the problem of fake news, making for a large number of highly biased sources that are reaching the mainstream through social networks. The economics of emotion theory proposes that fake news headlines are created to evoke emotional responses in readers that will cause them to interact with the article in a way that allows the creator to make a profit (through clicking on the link to the full article, by sharing the article, etc.). This mixed methods study investigates the process by which individuals experience discrete emotional reactions to fake news headlines, and how these emotions contribute to the perpetuation of fake news through sharing behaviors. U.S. participants (n=879 across two waves) viewed one of eight false news headlines and reported their emotional reactions, belief in the headline, and potential sharing behaviors. In general, participants were more likely to believe headlines that aligned with their existing beliefs (e.g., liberals were more likely to believe negative news about conservatives), reacted with more negative emotions to headlines that attacked their party, and were more likely to report intentions to suppress (e.g., post a link to a fact check) fake news that attacked their own party. Emotional reactivity of participants was associated with response behavior intentions such that participants who reported high levels of emotions were more likely to take actions that would spread or suppress the fake news, participants who reported low levels of emotions were more likely to ignore or disengage from the spread of false news, and participants who reported high levels of negative emotions and low levels of positive emotions were more likely to suppress the spread of fake news and less likely to contribute to the spread of fake news. Our findings are synthesized into a process model that explains how discrete emotions and beliefs influence sharing behaviors. Implications for mitigating the spread of fake news are discussed in terms of this model.

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.

Elections worldwide have been marked by the spread of fake news. Online disinformation is everywhere, used as a political weapon in the battlefield of manipulation. This study focused on the Portuguese 2019 elections to assess the reach of fake news compared to mainstream media news and to verify whether fake news had specific targets. We reviewed all posts (N=1197) from newspaper Facebook pages and fake news Facebook pages published during the campaign to verify their engagement. BuzzSumo assessed popularity by counting all posts’ shares, reactions and comments. Iramuteq software related the content of all published headlines by analyzing clusters of the most frequent words. Findings show that fake news had no greater reach than real news during the election campaign. However, fake news are more likely to be shared, while real news tend to get more reactions and more comments. In fake news headlines the terms associated with left-wing and government are the most common. The prime minister and the Socialist Party are associated with negative connotations. Results suggest that Portuguese fake news are related to rightwing extremism and publish hate content targeting corruption and leftist policies in general. Unlike other countries, anti-immigration discourse and fearmongering were not prominent contents.

Machine learning methods are proposed to mitigate the spread of fake Brazilian news about politics so as not to harm society. Supervised algorithms are explored, requiring labeled news to train. However, labeling a high volume of news can be complex, onerous, time-consuming, error-prone, and costly. Hence, large language models (LLMs) have been used to detect fake news once LLMs are unsupervised methods that can play the role of classifiers. Most fake news detection studies explore the OpenAI LLMs (require payment) and lack an empirical evaluation with other LLMs. However, several open-source models obtain comparative and state-of-the-art (SOTA) results. We highlight that these models have yet to be explored in detecting fake Brazilian news about politics, which is crucial as it directly impacts society. In this sense, we propose a new dataset for detecting fake Brazilian news about politics and an empirical evaluation of open-source LLMs and OpenAI LLMs. In our results, the LLM from Google (Gemma) outperformed the other six LLMs, including GPT-4, proving to be the most promising model for detecting fake news about Brazilian politics.

The advent of the World Wide Web and the rapid adoption of social media platforms (such as Facebook and Twitter) paved the way for information dissemination that has never been witnessed in the human history before. With the current usage of social media platforms, consumers are creating and sharing more information than ever before, some of which are misleading with no relevance to reality. Automated classification of a text article as misinformation or disinformation is a challenging task. Even an expert in a particular domain has to explore multiple aspects before giving a verdict on the truthfulness of an article. In this work, we propose to use machine learning ensemble approach for automated classification of news articles. Our study explores different textual properties that can be used to distinguish fake contents from real. By using those properties, we train a combination of different machine learning algorithms using various ensemble methods and evaluate their performance on 4 real world datasets. Experimental evaluation confirms the superior performance of our proposed ensemble learner approach in comparison to individual learners.

Fake news is the kind of news that is made deliberately to deceive the readers. It is a sort of purposeful publicity which is distributed as veritable information. Fake news is spread out through traditional news media and social media. Fake news has been an issue for quite a while. With the introduction of social media, the spread of fake news is increased, and it got hard to separate between true news and fake news. The spread of fake news involves worry as it manipulates public opinions. The widespread of fake news can adversely affect people and society in general. The problem of fake news has increased significantly in recent years. The scope and impact of social media have shifted dramatically. On the one hand, it is low cost and simple accessibility, as well as the ability to quickly exchange information attract people to read news from it. On the other hand, it empowers widespread fake news, which is only false data to deceive people. A stand-alone bidirectional encoder representations from transformers model is used in this paper for fake news detection. We try to pose the problem as a text classification problem and build a deep learning model for achieving the objective. We used the BERT model to develop fake news or real news classification solution for our solution in this model. We achieved more than 95% accuracy on the test set and a remarkable by the area under the curve (AUC) by a stand-alone BERT model.KeywordsNews detectionBERTDeep learningMachine learningSocial mediaTransformerNLP

PurposeThe continuous spread of the corona virus disease 2019 (COVID-19) pandemic has generated public health concern with avalanche of information accompanied by series of fake news. Thus, this study examined the effect of Information Literacy Competency (ILC) in curtailing the spread of fake news among Library and Information Science (LIS) undergraduates in Nigeria.Design/methodology/approachSurvey research design of the descriptive type was used to generate data from a group of LIS undergraduates online. The population of the study consisted of 138 LIS undergraduate students who participated in the survey (thus, n = 138). The data retrieved was subjected to descriptive analysis.FindingsThe study revealed that the students had high level of ILC (x ¯ = 3.42), and there was low prevalence level of COVID-19 pandemic fake news (x ¯ = 2.35) among them. The major causes of COVID-19 fake news were too much information in circulation concerning COVID-19 (x ¯ = 3.44) and the resultant inability to discern or spot fake news from verified and authentic news (x ¯ = 3.28). The study also revealed that ILC had a significant effect in curtailing the spread of COVID-19 fake news with a grand mean of 3.28 against the criterion mean of 2.5. It is implied that LIS undergraduates are educationally position to acquire ILC which is crucial to their identification of fake news and helps to curtail its spread.Research limitations/implicationsThe study is limited in its use of online group for data elicitation within a limited period of three weeks. Also, in its adoption of self-evaluation scale to measure ILC instead of standard information literacy test. Also, the high chances of social desirability bias in sections C and E serve as a limitation to the study.Practical implicationsThe study reinforces the need to enhance structures that flags fake news on social media platforms and integrating IL into schools’ curriculum at all levels.Originality/valueThis study seeks to pioneer a new area of focus on the relevance of ILC to different global issues that concern the health and well-being.

Information security is a key element in any crisis situation, where the dissemination of reliable, trustworthy, and timely information is vital for both society and government systems. In emergency situations, ensuring information security plays a critical role in maintaining social stability and effective crisis management. In this context, the media assume a crucial role, acting as the primary transmitters of information. They influence public opinion, guide public behavior, and help control the situation. Mass media not only convey information but also perform analytical functions, establish a level of trust in society, and affect the perception of crisis situations. However, due to the rapid development of information technologies, the unprecedented growth of information flows, and the circulation of information on digital platforms, the risks of disinformation, manipulation, and the spread of fake news are increasing. These threats can seriously undermine information security, causing panic, misperception, and destabilization of public systems. In times of crisis, when information must be clear and reliable, the spread of manipulative or fake news can lead to dangerous and unavoidable consequences. Therefore, the importance of responsible and professional media behavior in crisis situations cannot be underestimated. The media must exercise the utmost care in verifying information and assessing its credibility, ensuring that the information presented to the public is accurate and timely. This article will address the importance and responsibility of the media in the context of information security, as well as the challenges faced by the media in providing accurate and secure information to the public during crises.

The evolution of electronic media is a mixed blessing. Due to the easy access, low cost, and faster reach of the information, people search out and devour news from online social networks. In contrast, the increasing acceptance of social media reporting leads to the spread of fake news. This is a minacious problem that causes disputes and endangers the societal stability and harmony. Fake news spread has gained attention from researchers due to its vicious nature. proliferation of misinformation in all media, from the internet to cable news, paid advertising and local news outlets, has made it essential for people to identify the misinformation and sort through the facts. Researchers are trying to analyze the credibility of information and curtail false information on such platforms. Credibility is the believability of the piece of information at hand. Analyzing the credibility of fake news is challenging due to the intent of its creation and the polychromatic nature of the news. In this work, we propose a model for detecting fake news. Our method investigates the content of the news at the early stage i.e., when the news is published but is yet to be disseminated through social media. Our work interprets the content with automatic feature extraction and the relevance of the text pieces. In summary, we introduce stance as one of the features along with the content of the article and employ the pre-trained contextualized word embeddings BERT to obtain the state-of-art results for fake news detection. The experiment conducted on the real-world dataset indicates that our model outperforms the previous work and enables fake news detection with an accuracy of 95.32%.

This study sought to investigate factors causing the spread of fake news on digital media (DM) and to explore the sometimes disastrous consequences of fake news on social media. The study also aimed to construct a framework for fake news disaster management to control the dangers of false news on DM. The study applied PRISMA guidelines and techniques for exploring, devising, and inclusion and exclusion criteria. The search was carried out through 15 of the world’s leading digital databases. As a result, 31 peer-reviewed studies published in impact-factor journals of leading databases were included. Findings showed that several factors influenced the sharing of fake news on digital media (DM) platforms. Six major trending factors were the rise of technologies, social connections, political reasons, the absence of a controlling center, online business and marketing, and quick dissemination of information. The study identified the disadvantages of fake news (FN) on digital media (DM). A framework was constructed for managing fake news disasters to control the spread of fake news on digital media. This paper offers important theoretical contributions through the development of a framework for controlling fake news spread on digital media and by providing a valuable addition to the existing body of knowledge. The study offers practical assistance to top management, decision makers, and policymakers to devise policies to effectively manage problems caused by fake news dissemination. It provides practical strategies to address fake news disasters on digital media for redefining social values. This research also assists digital media managers in utilizing the proposed framework and controlling the harmful impact of fake news on social media.

The online spread of fake news on various platforms has emerged as a significant concern, posing threats to public opinion, political stability, and the dissemination of reliable information. Researchers have turned to advanced technologies, including machine learning (ML) and deep learning (DL) techniques, to detect and classify fake news to address this issue. This research study explores fake news classification using diverse ML and DL approaches. We utilized a well-known “Fake News” dataset sourced from Kaggle, encompassing a labelled news collection. We implemented diverse ML models, including multinomial naïve bayes (MNB), gaussian naïve bayes (GNB), Bernoulli naïve Bayes (BNB), logistic regression (LR), and passive aggressive classifier (PAC). Additionally, we explored DL models, such as long short-term memory (LSTM), convolutional neural networks (CNN), and CNN-LSTM. We compared the performance of these models based on key evaluation metrics, such as accuracy, precision, recall, and the F1 score. Additionally, we conducted cross-validation and hyperparameter tuning to ensure optimal performance. The results provide valuable insights into the strengths and weaknesses of each model in classifying fake news. We observed that DL models, particularly LSTM and CNN-LSTM, showed better performance compared to traditional ML models. These models achieved higher accuracy and demonstrated robustness in classification tasks. These findings emphasize the potential of DL models to tackle the spread of fake news effectively and highlight the importance of utilizing advanced techniques to address this challenging problem.

Fake news can have a significant negative impact on society because of the growing use of mobile devices and the worldwide increase in Internet access. It is therefore essential to develop a simple mathematical model to understand the online dissemination of fake news. In this study, we propose a point process model of the spread of fake news on Twitter. The proposed model describes the spread of a fake news item as a two-stage process: initially, fake news spreads as a piece of ordinary news; then, when most users start recognizing the falsity of the news item, that itself spreads as another news story. We validate this model using two datasets of fake news items spread on Twitter. We show that the proposed model is superior to the current state-of-the-art methods in accurately predicting the evolution of the spread of a fake news item. Moreover, a text analysis suggests that our model appropriately infers the correction time, i.e., the moment when Twitter users start realizing the falsity of the news item. The proposed model contributes to understanding the dynamics of the spread of fake news on social media. Its ability to extract a compact representation of the spreading pattern could be useful in the detection and mitigation of fake news.

'Cure or Poison?' Identity Verification and the Spread of Fake News on Social Media

Social media usage is growing globally, with an exponential increase in low- and middle-income countries. Social media changes the ways in which information-sharing occurs, intensifying the population's exposure to misinformation, including fake news. This has important repercussions for global health. The spread of fake news can undermine the implementation of evidence-based interventions and weaken the credibility of scientific expertise. This is particularly worrisome in countries, such as Brazil, in a sociopolitical context characterized by a lack of popular trust in public institutions. In this project report, we describe our experience with the spread of fake news through the social media platform WhatsApp during the implementation of a cluster randomized controlled trial aimed at reducing dengue incidence in children in Fortaleza (Brazil). During initial visits to selected clusters, the research team was met with resistance. Then, soon after data collection started, fake news began circulating about the study. As a result, the research team developed strategies to dispel suspicion and further promote the study. However, the climate of violence and mistrust, coupled with the COVID-19 pandemic, forced the interruption of the study in 2019. The lessons learned from our experience in Fortaleza can be useful to other researchers and practitioners implementing large-scale interventions in this era of health-related misinformation.

In the study of fake news spreading, it is essential to know how different types of spreaders differ in terms of their characteristics, interconnections, and cascading flow. The fake news graph analyzer (FNGA) is an open-source software that provides the required computations for such extended analyses on large graphs. Moreover, FNGA generates data for graph visualizations. Also, FNGA is designed to consider the spreading of both fake and true news simultaneously in the graph, leading to a variety of confrontational patterns. FNGA facilitates future research on fake news and the diffusion of any contagion within a graph of entities.