Spread Of Fake News Research Articles

FAKE NEWS, MISINFORMATION AND DISINFORMATION ABOUT COVID-19 IN SOCIAL MEDIA DURING THE PANDEMIC AND POST-PANDEMIC TIME (CASE OF GEORGIA)

Spreading fake news and disinformation is one of the global challenges over the last years. It is a challenge for Georgia too. This acute problem was also current at the beginning of the Covid-19 pandemic, when a number of misinformation appear in the social media as well as in the traditional media, regarding the origin and spread of the coronavirus, especially in Facebook which is the most popular social network in Georgia. A lot of fake news was spread and is still being spread in the post-pandemic period too. The Goal of this research paper is to explore the attitudes of Georgian Facebook users, specifically what kind of fake news and disinformation they met about Coronavirus in social media, how to identify them, how much they tried to verify them, how to explain their spread, how to fight them, etc. In order to explore this issue a social constructivism framework is used. As for the main research method, this study is based on the qualitative approach. For this purpose, the method of in-depth interviews - with Georgian active internet users – has been conducted. The respondents are doctors, journalists as well as representatives of different academic fields, who possess media literacy skills.

Implementation of the Critical Thinking Blended Apprenticeship Curricula and Findings per Discipline: Foreign Language Teaching

Critical thinking is a central element in higher education, designed to respond to authentic challenges that our society currently faces: the emergence and spread of fake news, disinformation, and manipulation. There is a consensus regarding CT’s importance and role in higher education. Nevertheless, CT skills are often implicitly mentioned and only occasionally included in university curricula explicitly. The present paper aims at analysing how CT can be measured and tested in higher education, and it proposes specific tasks designed to increase the use of CT within the theoretical framework defined by Peter Facione and Paul Elder. Updated course descriptions were tested, and students’ feedback was analysed and discussed. CT was measured by pre-questionnaires, mid-questionnaires, and post-questionnaires to establish the effectiveness of coherently implementing CT into the course descriptions. The survey includes answers to open questions to determine the suitability of the tasks proposed. The present research is part of the international Erasmus project “Critical Thinking for Successful Jobs”.

Content-Based Fake News Detection With Machine and Deep Learning: a Systematic Review

Fake news, which can be defined as intentionally and verifiably false news, has a strong influence on critical aspects of our society. Manual fact-checking is a widely adopted approach used to counteract the negative effects of fake news spreading. However, manual fact-checking is not sufficient when analysing the huge volume of newly created information. Moreover, the number of labeled datasets is limited, humans are not particularly reliable labelers and databases are mostly in English and focused on political news. To solve these issues state-of-the-art machine learning models have been used to automatically identify fake news. However, the high amount of models and the heterogeneity of features used in literature often represents a boundary for researchers trying to improve model performances. For this reason, in this systematic review, a taxonomy of machine learning and deep learning models and features adopted in Content-Based Fake News Detection is proposed and their performance is compared over the analysed works. To our knowledge, our contribution is the first attempt at identifying, on average, the best-performing models and features over multiple datasets/topics tested in all the reviewed works. Finally, challenges and opportunities in this research field are described with the aim of indicating areas where further research is needed.

Fake news, disinformation and misinformation in social media: a review.

Online social networks (OSNs) are rapidly growing and have become a huge source of all kinds of global and local news for millions of users. However, OSNs are a double-edged sword. Although the great advantages they offer such as unlimited easy communication and instant news and information, they can also have many disadvantages and issues. One of their major challenging issues is the spread of fake news. Fake news identification is still a complex unresolved issue. Furthermore, fake news detection on OSNs presents unique characteristics and challenges that make finding a solution anything but trivial. On the other hand, artificial intelligence (AI) approaches are still incapable of overcoming this challenging problem. To make matters worse, AI techniques such as machine learning and deep learning are leveraged to deceive people by creating and disseminating fake content. Consequently, automatic fake news detection remains a huge challenge, primarily because the content is designed in a way to closely resemble the truth, and it is often hard to determine its veracity by AI alone without additional information from third parties. This work aims to provide a comprehensive and systematic review of fake news research as well as a fundamental review of existing approaches used to detect and prevent fake news from spreading via OSNs. We present the research problem and the existing challenges, discuss the state of the art in existing approaches for fake news detection, and point out the future research directions in tackling the challenges.

Fake News Detection Model on Social Media by Leveraging Sentiment Analysis of News Content and Emotion Analysis of Users' Comments.

Nowadays, social media has become the main source of news around the world. The spread of fake news on social networks has become a serious global issue, damaging many aspects, such as political, economic, and social aspects, and negatively affecting the lives of citizens. Fake news often carries negative sentiments, and the public's response to it carries the emotions of surprise, fear, and disgust. In this article, we extracted features based on sentiment analysis of news articles and emotion analysis of users' comments regarding this news. These features were fed, along with the content feature of the news, to the proposed bidirectional long short-term memory model to detect fake news. We used the standard Fakeddit dataset that contains news titles and comments posted regarding them to train and test the proposed model. The suggested model, using extracted features, provided a high detection accuracy of 96.77% of the Area under the ROC Curve measure, which is higher than what other state-of-the-art studies offer. The results prove that the features extracted based on sentiment analysis of news, which represents the publisher's stance, and emotion analysis of comments, which represent the crowd's stance, contribute to raising the efficiency of the detection model.

An Effective Hybrid Deep Neural Network for Arabic Fake News Detection

Recently, the phenomenon of the spread of fake news or misinformation in most fields has taken on a wide resonance in societies. Combating this phenomenon and detecting misleading information manually is rather boring, takes a long time, and impractical. It is therefore necessary to rely on the fields of artificial intelligence to solve this problem. As such, this study aims to use deep learning techniques to detect Arabic fake news based on Arabic dataset called the AraNews dataset. This dataset contains news articles covering multiple fields such as politics, economy, culture, sports and others. A Hybrid Deep Neural Network has been proposed to improve accuracy. This network focuses on the properties of both the Text-Convolution Neural Network (Text-CNN) and Long Short-Term Memory (LSTM) architecture to produce efficient hybrid model. Text-CNN is used to identify the relevant features, whereas the LSTM is applied to deal with the long-term dependency of sequence. The results showed that when trained individually, the proposed model outperformed both the Text-CNN and the LSTM. Accuracy was used as a measure of model quality, whereby the accuracy of the Hybrid Deep Neural Network is (0.914), while the accuracy of both Text-CNN and LSTM is (0.859) and (0.878), respectively. Moreover, the results of our proposed model are better compared to previous work that used the same dataset (AraNews dataset).

Attention-Enriched Mini-BERT Fake News Analyzer Using the Arabic Language

Internet use resulted in people becoming more reliant on social media. Social media have become the main source of fake news or rumors. They spread uncertainty in each sector of the real world, whether in politics, sports, or celebrities’ lives—all are affected by the uncontrolled behavior of social media platforms. Intelligent methods used to control this fake news in various languages have already been much discussed and frequently proposed by researchers. However, Arabic grammar and language are a far more complex and crucial language to learn. Therefore, work on Arabic fake-news-based datasets and related studies is much needed to control the spread of fake news on social media and other Internet media. The current study uses a recently published dataset of Arabic fake news annotated by experts. Further, Arabic-language-based embeddings are given to machine learning (ML) classifiers, and the Arabic-language-based trained minibidirectional encoder representations from transformers (BERT) is used to obtain the sentiments of Arabic grammar and feed a deep learning (DL) classifier. The holdout validation schemes are applied to both ML classifiers and mini-BERT-based deep neural classifiers. The results show a consistent improvement in the performance of mini-BERT-based classifiers, which outperformed ML classifiers, by increasing the training data. A comparison with previous Arabic fake news detection studies is shown where results of the current study show greater improvement.

HyproBert: A Fake News Detection Model Based on Deep Hypercontext

News media agencies are known to publish misinformation, disinformation, and propaganda for the sake of money, higher news propagation, political influence, or other unfair reasons. The exponential increase in the use of social media has also contributed to the frequent spread of fake news. This study extends the concept of symmetry into deep learning approaches for advanced natural language processing, thereby improving the identification of fake news and propaganda. A hybrid HyproBert model for automatic fake news detection is proposed in this paper. To begin, the proposed HyproBert model uses DistilBERT for tokenization and word embeddings. The embeddings are provided as input to the convolution layer to highlight and extract the spatial features. Subsequently, the output is provided to BiGRU to extract the contextual features. The CapsNet, along with the self-attention layer, proceeds to the output of BiGRU to model the hierarchy relationship among the spatial features. Finally, a dense layer is implemented to combine all the features for classification. The proposed HyproBert model is evaluated using two fake news datasets (ISOT and FA-KES). As a result, HyproBert achieved a higher performance compared to other baseline and state-of-the-art models.

Implementation of Islamic Cyber Ethics on Digital Platform Use

The introduction of the 'Comprehensive Direction of the Industrial Revolution 4.0 (IR 4.0)' and 'National Digital' by the Malaysian government is seen to increase accessibility further and accelerate the involvement of the public with ICT. At the same time, the threat of the COVID-19 pandemic and the enforcement of the Movement Control Order (MCO) is seen to have increased the use of digital platforms for various purposes and needs. The advancement of this technology has indirectly made it easier for people to interact, whether in economic or social aspects. However, this sophistication certainly presents a variety of new challenges, especially involving the way of life and ethics of society. The spread of fake and negative news, cyberbullying, extreme entertainment, and borderless crime increasingly fill today's digital platforms. Therefore, this study is essential in exploring implementing Islamic cyber ethics on digital platform use. Thus, this study aims to explore cyber ethics based on Islam and its application in the current use of digital platforms. The study was conducted qualitatively using literature data such as theses and journals to examine elements of cyber ethics according to Islam. The study's findings were analyzed inductively to summarize the application of Islamic cyber ethical aspects in digital platforms today, especially in Malaysia. The widespread use of digital media without monitoring and self-control exposes every user to ethical and moral violations. Therefore, digital platform users should show good manners and ethics in interacting, primarily through cyber platforms, to create a more harmonious and safer digital ecosystem. Further field research is proposed to obtain more accurate data on the direction and implementation of Islamic cyber ethics among digital platform users.

A Hybrid Multitask Learning Framework with a Fire Hawk Optimizer for Arabic Fake News Detection

The exponential spread of news and posts related to the COVID-19 pandemic on social media platforms led to the emergence of the disinformation phenomenon. The phenomenon of spreading fake information and news creates significant concern for the public health and safety of the population. In this paper, we propose a disinformation detection framework based on multi-task learning (MTL) and meta-heuristic algorithms in the context of the COVID-19 pandemic. The developed framework uses an MTL and a pre-trained transformer-based model to learn and extract contextual feature representations from Arabic social media posts. The extracted contextual representations are fed to an alternative feature selection technique which depends on modified version of the Fire Hawk Optimizer. The proposed framework, which aims to improve the disinformation detection rate, was evaluated on several datasets of Arabic social media posts. The experimental results show that the proposed framework can achieve accuracy of 59%. It obtained, at best, precision, recall, and F-measure of 53%, 71%, and 53%, respectively, on all datasets; and it outperformed the other algorithms in all measures.

Using Cascade Theories to Explain the Spread of Fake News in the Asian Context

ABSTRACT Understanding the nature of the spread of misinformation is critical to an effort to control or minimize it. This paper uses existing cascade theories and ties them into propaganda frameworks to help understand the complex nature of fake news in contemporary media. We draw upon the availability cascades, cognitive cascades, and propaganda models to structure and process misinformation diffusion in the hope that it may help counter its spread. We use examples of fake news from Singapore, Myanmar, and India to conclude that the complexities of fake news creation, belief, and dissemination are significant, as is its enduring nature.

A framework for fake news detection based on the wisdom of crowds and the ensemble learning model

Nowadays, the rapid development of social networks has led to the proliferation of social news. However, the spreading of fake news is a critical issue. Fake news is news written to intentionally misinform or deceive readers. News on social networks is short and lacks context. This makes it difficult for detecting fake news based on shared content. In this paper, we propose an ensemble classification model to detect fake news based on exploiting the wisdom of crowds. The social interactions and the user?s credibility are mined to automatically detect fake news on Twitter without considering news content. The proposed method extracts the features from a Twitter dataset and then a voting ensemble classifier comprising three classifiers namely, Support Vector Machine (SVM), Naive Bayes, and Softmax is used to classify news into two categories which are fake and real news. The experiments on real datasets achieved the highest F1 score of 78.8% which was better than the baseline by 6.8%. The proposed method significantly improved the accuracy of fake news detection in comparison to other methods.

An explainable model for content moderation

The spread of fake news on social media has eroded trust in traditional news outlets and institutions. In response, social media platforms have incorporated machine learning, algorithms that can learn patterns in data without explicit programming, into their content moderation systems to help remove fake posts. However, these algorithms often misinterpret language and make poor moderation decisions. We need to better understand how machine learning algorithms interpret language. Explainability is a challenge with neural networks, a popular machine-learning model inspired by how neurons communicate in the brain. In this study, we sought to develop an explainable model for content moderation comparable in accuracy to a traditional neural network, focusing on classifying real and fake news articles. We hypothesized that by identifying keywords and quantifying their contribution to an article’s credibility, we could predict credibility by summing the contributions of all the keywords in an article. We trained a convolutional neural network to classify articles using a 3,000-keyword vocabulary, achieving 85.8% accuracy. We used the Shapley additive explanations algorithm to calculate each keyword's median contribution to the model’s predictions. We created a linear model that summed the median contributions of keywords in an article, achieving a comparable 81.0% accuracy. We then examined keywords with the largest median contributions. Clickbait and COVID-19 terms correlated with fake news. Legal and political terminology correlated with real news. Our results demonstrate the potential for explainable models to improve our understanding of content moderation algorithms and fake news linguistics.

Fakes and Trash Streams as a Threat to Information Security

The article examines new relations for Russian practice related to the introduction of a new concept of “fake news” into the legal field, the spread of fake news, the demonstration of trash streams and emerging problems of information security of the state, society, and the individual. The state's attention to the problem of deliberate dissemination of false information has increased many times due to the widespread use of the Internet by various segments of the population, where the flow of false information continues to increase many times. In fact, there is an information war; special technologies are used to influence the psyche of a person who finds it difficult to understand the events taking place, to distinguish lies from the truth. At the same time, fake news can concern a wide variety of aspects — from politics and specific actions of certain individuals to predictions about the future of humanity and individual peoples. The article shows the three most popular topics for fakes. These are topics related to a special military operation; healthcare; politics and state security. The reasons for the unreliability of information may be different (incorrect choice of research methodology, insufficient empirical base in scientific and/ or sociological research, deliberate falsification of facts, deliberate dissemination of false information, etc. ). Cases of particular interest and importance for law are cases when the creation and dissemination of false information is intentional and has a negative impact on information security as an individual, and society as a whole. Fake information is not the only threat to information security in Internet. From a means of communication and a source of knowledge, the Internet is turning into a means of manipulating public consciousness, a means of influencing individual groups of people and, above all, children and adolescents who lack life experience. And if false information is a centuries—old phenomenon, then trash streams are products of the Internet. The article analyzes this phenomenon and a number of reasons for its popularity, in particular, the gradual formation of an emphasis on violence, since it was violence that allowed increasing the number of viewers and, accordingly, donations. To date, the current legislation does not know the concept of “trash streams”, respectively; there is no special legal regulation of trash streams, as well as their prohibition. So far, they, as an independent social phenomenon, are outside the legal field. But trash streams, under certain circumstances, can be partially regulated by both legal and non-legal means.

A pandemia de covid-19 e as fake news: uma revisão da literatura

Abstract This article presents the results of the narrative review on fake news that occurred during the most critical period of the COVID-19 pandemic. A word search was carried out in the BVSMS, BVSalud, and Scielo databases, using the expression fake news AND COVID-19, notícia falsa AND COVID-19, and desinformación AND COVID-19, since these expressions addressed the two languages selected for the review: Portuguese and Spanish. The inclusion criterion favored articles that used the web as a source of research, published between 2020 and 2022. A total of 24 articles were analyzed, 14 written in Portuguese and 10 in Spanish. The year 2022 concentrated the largest number of publications (n=11), and we concluded that social networks are the vehicles for the greatest dissemination of this type of information. The spread of fake news during the second year of the COVID-19 pandemic was as worrying as the disease itself, but the literature has not explored sufficiently the phenomenon that still follows the pandemic still underway. Most of the analyzed texts advised to the use of the news dissemination strategy to disseminate useful information about health, concluding that the analyzed studies demonstrated that the false news were incomplete or misleading, but most were unintentional.

Las redes sociales y la difusión de noticias falsas en Nigeria: la protesta de endSARS en perspectiva

Las redes sociales y la difusión de noticias falsas en Nigeria: la protesta de endSARS en perspectiva

How the work of a Ukrainian journalist changed during the war

Journalism and war or journalism in the conditions of war is a special type of journalism, specific media communication aimed at covering military conflicts. Today, Ukraine is directly involved in the war, so it is very important to study the changes that took place in journalism during the military confrontation between Ukraine and Russia. The article analyzed how the work of a Ukrainian journalist changed during the war from the point of view of both the audience and the journalists themselves. The main emphasis is on the analysis of the professional skills of journalists, which are transformed under the influence of war and impose many responsibilities and restrictions on journalists, and the specifics of the information war as a second front. Journalistic activity in the conditions of war is analyzed from the point of view of social responsibility and the struggle for information space in the conditions of propaganda and fakes, which are an integral part of the struggle for victory. Disinformation and the spread of fake news, which are tools of socio-political and military confrontation, also became the subjects of this study. It is important for recipients of news messages to develop the ability to think critically, check facts, and analyze primary sources to establish true information. Such skills make the work of Ukrainian journalists much easier, and it will be more difficult for propagandists from the aggressor country to win the heads and hearts of our people who observe information hygiene. The article examines the special language of the media in the conditions of war, analyzes the change in journalists’ rhetoric, the transformation of speech, and the introduction to the modern use of war-related neologisms, which play a significant role in journalistic narratives, the correlation, and correspondence between information policy and journalistic standards, which can change in the conditions of war. Based on foreign experience, in particular Israel, examples of media models that can be applied in the Ukrainian context are given. It is important that Ukrainian experts are able to competently and professionally build the media world in the conditions of war in such a way that it does not harm either the military or the victims of Russian aggression.

Sentimental analysis of COVID-19 twitter data using deep learning and machine learning models

The novel coronavirus disease (COVID-19) is an ongoing pandemic with large global attention. However, spreading fake news on social media sites like Twitter is creating unnecessary anxiety and panic among people towards this disease. In this paper, we applied machine learning (ML) techniques to predict the sentiment of the people using social media such as Twitter during the COVID-19 peak in April 2021. The data contains tweets collected on the dates between 16 April 2021 and 26 April 2021 where the text of the tweets has been labelled by training the models with an already labelled dataset of corona virus tweets as positive, negative, and neutral. Sentiment analysis was conducted by a deep learning model known as Bidirectional Encoder Representations from Transformers (BERT) and various ML models for text analysis and performance which were then compared among each other. ML models used were Naïve Bayes, Logistic Regression, Random Forest, Support Vector Machines, Stochastic Gradient Descent and Extreme Gradient Boosting. Accuracy for every sentiment was separately calculated. The classification accuracies of all the ML models produced were 66.4%, 77.7%, 74.5%, 74.7%, 78.6%, and 75.5\%, respectively and BERT model produced 84.2%. Each sentiment-classified model has accuracy around or above 75%, which is a quite significant value in text mining algorithms. We could infer that most people tweeting are taking positive and neutral approaches.

Empirical Studies of Russian–Ukrainian War Related Fake News (Part 1)

The Russian–Ukrainian war, which broke out on February 24, 2022, resulted in several paradigm shifts in cyber warfare. One aspect of these changes is psychological operations. Russia and Ukraine have conducted extensive psychological operation campaigns to fulfil their war objectives, which have since been intense along modified intentions. This series of studies examines the impact of war-related fake news through various empirical research.In the first part of the paper, the authors examine the emergence of psychological operations and related terms in the international academic literature using network analysis methodology. In the second part of the paper, the authors use sentiment and network analysis to investigate the spread of different fake news. In the third study, the authors measure the attitudes toward the perception of the Hungarian Defence Forces from the perspective of the war in the neighbouring country.

Optimal Quad Channel Long Short-Term Memory Based Fake News Classification on English Corpus

The term ‘corpus’ refers to a huge volume of structured datasets containing machine-readable texts. Such texts are generated in a natural communicative setting. The explosion of social media permitted individuals to spread data with minimal examination and filters freely. Due to this, the old problem of fake news has resurfaced. It has become an important concern due to its negative impact on the community. To manage the spread of fake news, automatic recognition approaches have been investigated earlier using Artificial Intelligence (AI) and Machine Learning (ML) techniques. To perform the medicinal text classification tasks, the ML approaches were applied, and they performed quite effectively. Still, a huge effort is required from the human side to generate the labelled training data. The recent progress of the Deep Learning (DL) methods seems to be a promising solution to tackle difficult types of Natural Language Processing (NLP) tasks, especially fake news detection. To unlock social media data, an automatic text classifier is highly helpful in the domain of NLP. The current research article focuses on the design of the Optimal Quad Channel Hybrid Long Short-Term Memory-based Fake News Classification (QCLSTM-FNC) approach. The presented QCLSTM-FNC approach aims to identify and differentiate fake news from actual news. To attain this, the proposed QCLSTM-FNC approach follows two methods such as the pre-processing data method and the Glove-based word embedding process. Besides, the QCLSTM model is utilized for classification. To boost the classification results of the QCLSTM model, a Quasi-Oppositional Sandpiper Optimization (QOSPO) algorithm is utilized to fine-tune the hyperparameters. The proposed QCLSTM-FNC approach was experimentally validated against a benchmark dataset. The QCLSTM-FNC approach successfully outperformed all other existing DL models under different measures.