Fake News Detection Using Transformer and Ensemble Learning Models

On social media, misinformation can spread quickly, posing serious problems. Understanding the content and sensitive nature of fake news and misinformation is critical to prevent the damage caused by them. To this end, the characteristics of information must first be discerned. In this paper, we propose a transformer-based hybrid ensemble model to detect misinformation on the Internet. First, false and true news on Covid-19 were analyzed, and various text classification tasks were performed to understand their content. The results were utilized in the proposed hybrid ensemble learning model. Our analysis revealed promising results, establishing the capability of the proposed system to detect misinformation on social media. The final model exhibited an excellent F1 score (0.98) and accuracy (0.97). The AUC (Area Under The Curve) score was also high at 0.98, and the ROC (Receiver Operating Characteristics) curve revealed that the true-positive rate of the data was close to one in this model. Thus, the proposed hybrid model was demonstrated to be successful in recognizing false information online. © 2022 IEEE.

Fake News Encoder Classifier (FNEC) for Online Published News Related to COVID-19 Vaccines

In the past few years, social media and online news platforms have played an essential role in distributing news content rapidly. Consequently. verification of the authenticity of news has become a major challenge. During the COVID-19 outbreak, misinformation and fake news were major sources of confusion and insecurity among the general public. In the first quarter of the year 2020, around 800 people died due to fake news relevant to COVID-19. The major goal of this research was to discover the best learning model for achieving high accuracy and performance. A novel case study of the Fake News Classification using ELECTRA model, which achieved 85.11% accuracy score, is thus reported in this manuscript. In addition to that, a new novel dataset called COVAX-Reality containing COVID-19 vaccinerelated news has been contributed. Using the COVAX-Reality dataset, the performance of FNEC is compared to several traditional learning models i.e., Support Vector Machine (SVM), Naive Bayes (NB), Passive Aggressive Classifier (PAC), Long Short-Term Memory (LSTM), Bi-directional LSTM (Bi-LSTM) and Bi-directional Encoder Representations from Transformers (BERT). For the evaluation of FNEC, standard metrics (Precision, Recall, Accuracy, and F1-Score) were utilized.

CovidMis20: COVID-19 Misinformation Detection System on Twitter Tweets Using Deep Learning Models

Online news and information sources are convenient and accessible ways to learn about current issues. For instance, more than 300 million people engage with posts on Twitter globally, which provides the possibility to disseminate misleading information. There are numerous cases where violent crimes have been committed due to fake news. This research presents the CovidMis20 dataset (COVID-19 Misinformation 2020 dataset), which consists of 1,375,592 tweets collected from February to July 2020. CovidMis20 can be automatically updated to fetch the latest news and is publicly available at: https://github.com/everythingguy/CovidMis20. This research was conducted using Bi-LSTM deep learning and an ensemble CNN+Bi-GRU for fake news detection. The results showed that, with testing accuracy of 92.23% and 90.56%, respectively, the ensemble CNN+Bi-GRU model consistently provided higher accuracy than the Bi-LSTM model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

An Evaluation of the Perceptions of Socio-Humanities Students on the Skills of Critical Thinking

One of the fundamental objectives of higher education is to prepare students to think analytically in order to develop and create knowledge, thus going beyond receiving information and building simple knowledge. That is why higher education must adapt its teaching and learning process to cultivate critical thinking of students. In order for academia to ensure the effectiveness of the development of critical thinking skills, student perceptions cannot be neglected, given that they are the ultimate receptors. In addition, investigating students' perceptions of critical thinking skills could guide future training methods that promote their development. The purpose of this research is to explore students' attitudes and beliefs regarding critical thinking skills, as well as aspects of their development in academia and their transfer to other contexts. The study is conducted at the Dunarea de Jos University of Galaţi and uses the questionnaire survey method to assess students' perceptions of critical thinking, from several perspectives: the level of knowledge on critical thinking skills, strategies for learning critical thinking skills, the qualities of a critical thinker, the way students relate to the COVID 19 pandemic and fake news. To obtain the necessary data, the questionnaire was distributed through Microsoft Forms and subsequently analyzed to describe students' views on critical thinking. A sample of 101 students from specializations in the field of socio-humanities completed this survey. The results of the study showed that students invoke the need to overcome difficulties in developing critical thinking skills by: training teachers to use critical thinking in the classroom;including and practicing critical thinking skills in applied activities at courses and seminars;applying debates and discussions in the classroom as teaching and learning strategies;translating the theories learned in courses into different case studies;use of training skills, questionnaires, homework;participation in group projects in a collaborative environment. [ FROM AUTHOR] Copyright of Romanian Journal for Multidimensional Education / Revista Romaneasca pentru Educatie Multidimensionala is the property of Lumen Publishing House and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Fake News and the Illusion of Truth: The Influence of Media on German Political Discourse in the Wake of COVID-19

This paper explores the phenomenon of fake news and conspiracy theories during the COVID-19 pandemic in Germany with special focus on the emergence of the Querdenker movement. Through a post-structuralist lens complemented by the work of Michel Foucault, the concept of truth will be analyzed to understand its role in society and democratic discourse as well as how the production of knowledge and truth has changed with emergence of the internet and social media. In this context it becomes apparent how fake news can be threatening to political discourse by undermining basic scientific information necessary for effective decision-making processes. Insights of this analysis will then be used to develop legal propositions to tackle the problem of fake news without interfering too much with the determination of truth and public discourse. © 2022, Onati International Institute for the Sociology of Law. All rights reserved.

An Interpretable Fake News Detection Method Based on Commonsense Knowledge Graph

Existing deep learning-based methods for detecting fake news are uninterpretable, and they do not use external knowledge related to the news. As a result, the authors of the paper propose a graph matching-based approach combined with external knowledge to detect fake news. The approach focuses on extracting commonsense knowledge from news texts through knowledge extraction, extracting background knowledge related to news content from a commonsense knowledge graph through entity extraction and entity disambiguation, using external knowledge as evidence for news identification, and interpreting the final identification results through such evidence. To achieve the identification of fake news containing commonsense errors, the algorithm uses random walks graph matching and compares the commonsense knowledge embedded in the news content with the relevant external knowledge in the commonsense knowledge graph. The news is then discriminated as true or false based on the results of the comparative analysis. From the experimental results, the method can achieve 91.07%, 85.00%, and 89.47% accuracy, precision, and recall rates, respectively, in the task of identifying fake news containing commonsense errors.

Fake News, Social Media, and the COVID-19 Pandemic: The Zimbabwean Experience

This chapter examines the role of COVID-19 fake news disseminated on social media platforms (Twitter, Facebook and WhatsApp) in Zimbabwe. The research was guided by three research questions: (1) What is the nature of COVID-19 fake news in Zimbabwe? (2) How have Zimbabweans responded to the fake news? (3) What are the consequences of the fake news to COVID-19 response efforts? The study provided answers to these questions via a Fake News Receptivity Test (FNRT) and a sample population of one hundred and seventy-three social media users drawn from Harare-Zimbabwe's capital. The research discovered that 82% of the sample population accepted the COVID-19 fake news as truth. Drawing from these findings, the paper maintains that the rapid spread and controversy surrounding the COVID-19 pandemic in Zimbabwe can also be attributed to the role of fake news. The fake news mainly manifests in the form of false information about the pandemic, erroneous medical advice, fraudulent preventive measures and scandalous rumours about COVID-19 vaccines. The study concluded that the widespread acceptance of COVID-19 fake news in Zimbabwe has resulted in low vaccination turnout, flouting of COVID-19 safety protocols and the spiking of COVID-19-related deaths and infections. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Perceptions of Spanish citizens about disinformation in times of COVID-19: effects and mechanisms to fight against false news

Disinformation has become one of the main problems for society and democracy. Despite the increase in research on this topic, citizen perception of the effects caused by fake news and how to combat it is yet an ongoing issue. Our objective is to analyze these two aspects in the context of COVID-19 in Spain. Also, we seek to identify the sociodemographic and political factors that determine them. We employed a quantitative survey run through an online panel (n=682). The results indicate that disinformation generates, as the primary effect, an increase in mistrust towards the media and politicians among Spanish citizens. Likewise, the incidence of disinformation in the change of opinion and the decision to vote has dropped. Concerning Coronavirus, falsehoods had a low impact on vaccination, although they promoted conspiracy theories among citizens. Men, young people and far-right sympathizers perceive the consequences of deception more intensely. Finally, the work of fact-checking agencies and legislation are the mechanisms to combat disinformation that citizens consider most reliable. These results have important implications for public institutions and journalism. © 2023 Scientific Association Icono14. All rights reserved.

Disinformation in the age of the Covid-19 pandemic: How does belief in fake news and conspiracy theories affect canadians' reactions to the crisis?

This chapter examines the concepts of mistrust, news habits, and attitudes toward social and health measures associated with Covid-19 crisis. The aim is to assess how beliefs in fake news and conspiracy theories impact the reaction of the Canadian population to the coronavirus pandemic. The analyses presented in this chapter aim to answer specific research questions who want (1) to investigate to what extent the pandemic-related conspiracy theories and fake news permeated the beliefs of Canadians;(2) to find the socio-demographic characteristics of the Canadian who subscribes to pandemic-related conspiracy theories and fake news;(3) to bring out the types of information sources used by the Canadians who subscribes to pandemic-related conspiracy theories and fake news;and (4) to reveal the social attitudes of the Canadian who subscribes to pandemic-related conspiracy theories and fake news. © Springer Nature Switzerland AG 2023. All rights reserved.

Fake news during the war in Ukraine: coping strategies and fear of war in the general population of Romania and in aid workers.

Introduction: In addition to the health crisis that erupted during the COVID-19 pandemic, the war between Russia and Ukraine is impacting the mental health and wellbeing of the Romanian population in a negative way. Objectives: This study sets out to investigate the impact that social media consumption and an overload of information related to the armed conflict between Russia and Ukraine is having on the distribution of fake news among Romanians. In addition, it explores the way in which several psychological features, including resilience, general health, perceived stress, coping strategies, and fear of war, change as a function of exposure to traumatic events or interaction with victims of war. Methods: Participants (N = 633) completed the General Health Questionnaire (GHQ), the CERQ scale with its nine subscales, the Perceived Stress Scale (PSS), and the BRS scale (Brief Resilience Scale), the last of which measures resilience. Information overload, information strain and the likelihood of the person concerned spreading fake news were assessed by adapting items related to these variables. Findings: Our results suggest that information strain partially moderates the relationship between information overload and the tendency to spread false information. Also, they indicate that information strain partially moderates the relationship between time spent online and the tendency to spread false information. Furthermore, our findings imply that there are differences of high and moderate significance between those who worked with refugees and those who did not as regards fear of war and coping strategies. We found no practical differences between the two groups as regards general health, level of resilience and perceived stress. Conclusion and recommendations: The importance of discovering the reasons why people share false information is discussed, as is the need to adopt strategies to combat this behavior, including infographics and games designed to teach people how to detect fake news. At the same time, aid workers need to be further supported to maintain a high level of psychological wellbeing.

Everyday non-partisan fake news: Sharing behavior, platform specificity, and detection.

Concern over the impact of fake news on major socio-political events is growing. The use of deliberate misinformation is thought to have played a role in the outcome of the UK EU referendum, the 2016 US presidential election, and in the effectiveness of COVID-19 public health messaging. As a result, recent research has tended to focus on hyper-partisan (e.g., US politics; Democrat/Republican), person specific (e.g., Hillary Clinton/Donald Trump) content that incorporates emotive and hyperbolic language. However, in this study, we focus on an alternative form of fake news, across a variety of topics (e.g., Crime, Immigration, and Health), that avoids these characteristics, and which may therefore be more pervasive and difficult to detect. In a three-part study, we examined participants sharing intentions for fake news (including platform preference; Facebook, Twitter, Instagram, and WhatsApp), their ability to explicitly detect fake news, and whether individual differences on psychological measures of critical thinking ability, rational thinking, and emotional stability predict sharing behavior and detection ability. The results show that even our well-informed sample (political science students) were not immune to the effects of fake news, some issues (e.g., health and crime) were more likely to be shared than others (e.g., immigration), and on specific platforms (e.g., Twitter, Facebook). In addition, we show that individual differences in emotional stability appears to be a key factor in sharing behavior, while rational thinking aptitude was key to fake news detection. Taken together, this study provides novel data that can be used to support targeted fake news interventions, suggesting possible news topic, sharing behavior, and platform specific insights. Such interventions, and implications for government policy, education, and social media companies are discussed.

Combating the infodemic: COVID-19 induced fake news recognition in social media networks.

COVID-19 has caused havoc globally due to its transmission pace among the inhabitants and prolific rise in the number of people contracting the disease worldwide. As a result, the number of people seeking information about the epidemic via Internet media has increased. The impact of the hysteria that has prevailed makes people believe and share everything related to illness without questioning its truthfulness. As a result, it has amplified the misinformation spread on social media networks about the disease. Today, there is an immediate need to restrict disseminating false news, even more than ever before. This paper presents an early fusion-based method for combining key features extracted from context-based embeddings such as BERT, XLNet, and ELMo to enhance context and semantic information collection from social media posts and achieve higher accuracy for false news identification. From the observation, we found that the proposed early fusion-based method outperforms models that work on single embeddings. We also conducted detailed studies using several machine learning and deep learning models to classify misinformation on social media platforms relevant to COVID-19. To facilitate our work, we have utilized the dataset of "CONSTRAINT shared task 2021". Our research has shown that language and ensemble models are well adapted to this role, with a 97% accuracy.

Seven years of studying the associations between political polarization and problematic information: a literature review.

This literature review examines the intersection between political polarization and problematic information, two phenomena prominent in recent events like the 2016 Trump election and the 2020 COVID-19 pandemic. We analyzed 68 studies out of over 7,000 records using quantitative and qualitative methods. Our review revealed a lack of research on the relationship between political polarization and problematic information and a shortage of theoretical consideration of these phenomena. Additionally, US samples and Twitter and Facebook were frequently analyzed. The review also found that surveys and experiments were commonly used, with polarization significantly predicting problematic information consumption and sharing.

Panic buying and fake news in urban vs. rural England: A case study of twitter during COVID-19

This paper explores the potential association between the spread of fake news and the panic buying behavior, in urban and rural UK, widely accessible on Twitter since COVID 19 was announced by the WHO as a global pandemic. It describes how consumer's behavior is affected by the content generated over social media and discuss various means to control such occurrence that results in an undesirable social change. The research methodology is based on extracting data from texts on the subject of panic buying and analysing both the total volume and the rate of fake news classification during COVID-19, through crowdsourcing techniques with text-mining and Natural Language Processing models. In this paper, we have extracted the main topics in different phases of the pandemic using term frequency strategies and word clouds as well as applied artificial intelligence in exploring the reliability behind online written text on Twitter. The findings of the research indicate an association between the pattern of panic buying behavior and the spread of fake news among urban and rural UK. We have highlighted the magnitude of the undesired behavior of panic buying and the spread of fake news in the rural UK in comparison with the urban UK.

RDGT-GAN: Robust Distribution Generalization of Transformers for COVID-19 Fake News Detection

Social media platforms have become a vital source of information during the outbreak of the pandemic (COVID-19). The phenomena of fake information or news spread through social media have become increasingly prevalent and a powerful tool for information proliferation. Detecting fake news is crucial for the betterment of society. Existing fake news detection models focus on increasing the performance which leads to overfitting and lag generalizability. Hence, these models require training for various datasets of the same domain with significant variations in the distribution. In our work, we have addressed this overfitting issue by designing a robust distribution generalization of transformers-based generative adversarial network (RDGT-GAN) architecture, which can generalize the model for COVID-19 fake news datasets with different distributions without retraining. Based on our experimental findings, it is evident that the proposed model outperforms the current state-of-the-art (SOTA) models in terms of performance.

Translated COVID-19 Fake News Human Input and Localization As Benchmarks of Disinformation Intent

This article sets out to chart various aspects of the relationship between COVID-19 fake news and the process of translation by analyzing how several English-language deceptive reports were rendered into Romanian. In Romania, the emergence of the novel coronavirus coincided with a surge of interest in the fake news phenomenon among experts, the wider public, and most notably, the country's authorities, which blocked fake news websites temporarily in an effort to combat the infodemic. However, neither the Decree which instituted this measure nor the existing research into the phenomenon distinguishes between original and translated reports, which led to criticism regarding the extent to which the intent to disinform is provable in the case of such articles. In the present essay, the author argues, through case studies, that the higher the level of human input and localization, the higher the chances that a translated fake news report can successfully be proven to be part of a disinformation agenda.

The Heterogeneity of Language Policies and its Related Health Crisis Communication about COVID-19 in Lesotho and South Africa

On 7 January 2020, the World Health Organization (WHO) reported a new unknown pneumonia outbreak in China. Over the following weeks, COVID-19 spread to an endemic situation mainly concentrated in the Hubei province, with few isolated cases outside China. The WHO hesitated until 11 March to use the term pandemic, although cases of COVID-19 were reported already in Europe at that time. Sub-Saharan Africa only reported its first cases in mid-February, with some travellers from or returning mainly from Italy, but also Austria, Germany and France. It was only then that the media in South Africa started to report extensively about the COVID-19 outbreak. Various governmental ministries in South Africa or Lesotho informed their populations about COVID-19 and their regulations to control the outbreak of the disease. Although most of the information was in English, a substantial amount of audio and video files were available in several African Languages in South Africa, plus many governmental announcements in Sesotho in Lesotho. Governments and healthcare professionals were aware of the risks of fake news being spread, but they tried to be as transparent as possible with the public. This paper analyses how different countries like Lesotho and South Africa broadcasted COVID-19 in media, official government websites and social media platforms. Cognizance of language usage in media will be looked at as most African countries are multilingual and such life-threatening topics and far-reaching political interventions in all spheres of life should be communicated as inclusively and transparent as possible. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Arabic fake news detection in social media Based on AraBERT

Social media has become a source of information for many people because of its freedom of use. As a result, fake news spread quickly and easily, regardless of its credibility, especially over the past decade. The vast amount of information being shared has fraudulent practices that negatively affect readers' cognitive abilities and mental health. In this study, we aim to introduce a new Arabic COVID-19 dataset for fake news related to COVID-19 from Twitter and Facebook. Afterward, we applied two pre-Trained models of classification AraBERT and BERT base Arabic. As a result, AraBERT models obtained better accuracy than BERT base Arabic in two datasets. © 2022 IEEE.

Determinants of the Ability to Identify Fake News Among Seniors in Portugal: A Proposal

The COVID-19 pandemic boosted the production and circulation of false information, especially online, leading the World Health Organization to classify this phenomenon as an infodemic, i.e., a misinformation epidemic. In addition to this, the growing aging of the population is a reality not only in Portugal, but throughout the world. The Internet, and in particular social networks, can be an important contribution to the well-being of the elderly, reducing their social isolation. However, it makes them even more susceptible to the consumption of false information. Considering the increasing contact with fake news, it is important to evaluate the determinants of the ability of the elderly to identify fake news. In this article we present a research proposal with a quantitative methodology, based on a hypothetical-deductive process, supported by a self-administered online questionnaire survey for data collection, to meet this objective. © 2022 Associacao Portuguesa de Sistemas de Informacao. All rights reserved.

COVID-19, "Black Lives Matter” and Indigenous Australians: A Tale of Two Intersecting Pandemics

The purpose of this chapter is to examine the impact COVID-19 has had on Aboriginal and/or Torres Strait Islander people and their communities. The Black Lives Matter protests occurred in the middle of the COVID-19 pandemic, therefore, it is necessary to explore the response(s) by the Aboriginal and/or Torres Strait Islander community in attending the protests and their engagement around the BLM movement and subsequent media attention. It is imperative to ensure Aboriginal and/or Torres Strait Islander voices are centered when researching issues that affect communities and individuals. This chapter will ensure the voices of Aboriginal and/or Torres Strait Islander people convey the issues, impacts and perspectives that COVID-19 and the implications of the BLM movement have and continue to have in community. Indigenous communities in Australia are not a monoculture;therefore, it is imperative to ensure the inclusion of the diverse Aboriginal and Torres Strait Islander experiences of the varying populations. The intersectionalities of fake news, Indigeneity and racism will be the main themes when researching the affects that COVID-19 has had, and may continue to have, on Aboriginal and/or Torres Strait Islander people and their communities. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.