Evaluating Pretrained Transformer-based Models for COVID-19 Fake News Detection

Since the outbreak of COVID-19, social media plays an important role to circulate pandemic news around the world. Some malevolent users may take an advantage of this and spread fake news to attract people for business and research purposes. In this paper, we take an approach by applying existing machine learning algorithms to detect fake news in social media and show a comparison of their performances. In our study, the support vector classifier (SVC) outperforms the rest of the classifiers based on different statistical metrics. Therefore, the SVC classifier has been considered as our proposed classifier model to identify fake COVID-19 news in social media. Two word clouds have also been generated based on the appearance of words in the news that shows an insignificant difference between true and fake news.

Misinformation becomes rampant in the digital age and social media provide people with the opportunities for engaging more actively in society. The objectives of the study are: (i) to ascertain the extent to which residents of Kano have been exposed to digital images on Covid-19 that often accompany fake news in social media; (ii) to establish the extent to which residents of Kano are influenced by fake news on Covid-19 with images; (iii) to find out the factors that often lead to the influence of fake news with digital images on Covid-19 among social media users in Kano; (iv) to ascertain the social media platforms mostly used in spreading fake news about Covid-19 in the state. Theoretically, Technological Determinism and Perception theories were adopted to analyze these issues. A Positivist approach to data generation and analysis was adopted using the survey method. Two local governments were selected for the study: Tarauni and Kano Municipal. Tarauni local government area had the highest number of the Covid-19 cases, while Kano Municipal had the lowest number. The population of the study consist of 593,087 with a sample size of 400 respondents derived from Taro Yamane’s sample size prediction table. The respondents were reached through cluster sampling. A total of 400 copies of questionnaires were administered to respondents in Tarauni and Kano Municipal. However, only 385 copies, which represent (96%), were retrieved and found usable for the study as the remaining 15 were not returned. The study found that Kano residents were significantly exposed to digital images that often accompany fake news in social media. They read news online every day, prefer news accompanied by images, share and like news online. The study also found that Kano residents are influenced by fake news with digital images on Covid-19 to a very great extent, especially on Facebook. Factors responsible for proliferation of fake news on social media include: perception or instinct, eagerness to be the first to share images and lack of knowledge about image verification tools. The study concludes that ignorance and the old belief that pictures do not lie were responsible for this.

Social media and the Internet have suffered from false messages and false news since the outbreak of the COVID-19 pandemic. The intention is often to mislead readers and/or make them believe something that is not real. All that increases the need for automatic methods that can detect fake news in social media. In this paper, we proposed a classification model based on machine learning and deep learning algorithms to classify COVID-19 tweets into two classes using Apache Spark and the Python API Tweepy, the proposed idea uses the features of tweets to detect fake news. Experimental results show that the random forest algorithm gives best results with an accuracy equal to 79% and that the sentiment of tweets plays an important role in the detection of fake news. By applying the proposed model on our COVID-19 dataset, 67% of tweets are classified as REAL while 37% are classified FAKE.

PurposeThe purpose of this study is to examine the respondents’ evaluation of the pervasiveness of fake news through various SM platforms in Kuwait. The authors also examined the respondents’ attitudes toward most fake news on SM. A total of 1,539 Kuwaitis were selected.Design/methodology/approachThe questionnaire was the major tool for this study. The respondents, from whom demographic information was obtained, were asked about which SM platforms most frequently spread fake news, their attitudes toward the subjects most frequently involved in spreading fake news, their degree of use of the six SM platforms and interest in various subjects, and the attitudes toward the negative nature of SM news. SPSS was used for the data analysis.FindingsThe results showed that WhatsApp was the most likely to be used to disseminate fake news; Twitter and Instagram ranked second. The younger subjects were affected more by text and voice clips than the older ones.Originality/valueThe study, hopefully, produces new knowledge on the subject of fake news in social media, especially in the Arab world, since there are few studies conducted in the region. The study showed that WhatsApp was the SM tool most likely to be responsible for disseminating fake news in Kuwait, which may shed light on the usage of this application to be a news tool, rather than merely an interpersonal communication medium.

Social media can be a double-edged sword for society, either as a convenient channel exchanging ideas or as an unexpected conduit circulating fake news through a large population. While existing studies of fake news focus on theoretical modeling of propagation or identification methods based on machine learning, it is important to understand the realistic propagation mechanisms between theoretical models and black-box methods. Here we track large databases of fake news and real news in both, Weibo in China and Twitter in Japan from different cultures, which include their traces of re-postings. We find in both online social networks that fake news spreads distinctively from real news even at early stages of propagation, e.g. five hours after the first re-postings. Our finding demonstrates collective structural signals that help to understand the different propagation evolution of fake news and real news. Different from earlier studies, identifying the topological properties of the information propagation at early stages may offer novel features for early detection of fake news in social media.

Although fake news has been present in human history at any time, nowadays, with social media, deceptive information has a stronger effect on society than before. This article answers two research questions, namely (1) Is the dissemination of fake news supported by machines through the automatic con...

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

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

Multi-view co-attention network for fake news detection by modeling topic-specific user and news source credibility

With the exponential growth of user-generated content on social media platforms, the spread of fake news has become a critical challenge, undermining public trust and influencing societal behavior. This research focuses on fake news detection and knowledge verification using Deep Attention Networks, which can capture fine-grained semantic relationships and contextual dependencies in textual data. To achieve realistic and comprehensive evaluation, datasets are collected from diverse social media platforms including Twitter, Facebook, and Reddit, encompassing both verified and misleading content. The proposed methodology involves the implementation of a Deep Attentionbased model and its hierarchical comparison with existing state-of-the-art models such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Bi-LSTM, and Transformerbased architectures (e.g., BERT). Each model is evaluated using multiple performance metrics -accuracy, precision, recall, F1-score, and AUC - under various conditions tailored to different social media dynamics. A detailed analysis is presented through graphical representations such as bar charts, ROC curves, and confusion matrices, offering a visual and quantitative understanding of model effectiveness. The results consistently show that the Deep Attention Network provides superior performance in identifying and verifying fake news, especially in linguistically complex and context-rich scenarios. This research underscores the significance of attention mechanisms in enhancing the reliability and interpretability of fake news detection systems and lays the foundation for more robust content verification strategies on digital platforms.

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.

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%.

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.

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).

COVID-19 fake news detection: A hybrid CNN-BiLSTM-AM model