Fake Information Research Articles

Fake Information Detection by Gaining Knowledge of the Usage of Machine Learning

Fake Information Detection by Gaining Knowledge of the Usage of Machine Learning

Reputational Security and Media Security of Companies and Projects in the context of Sustainable Development Goals and ESG Principles

In the information field of high-tech projects, the following speech actions can be committed that form the objective side of offenses (crimes): defamation (defamatory speech actions), faking (fake speech acts), verbal extremism (extremist speech actions). It is proved that defamatory and fake information attacks are also possible in connection with the «interpretation» of publicly available «green» information about the organization: its statements and ESG strategy, non-financial reporting, etc. — for compliance with 1) planned entities — entities reflected in non-financial statements; 2) entities reflected in statements, strategies, non-financial reporting — actual data. Due to the fact that in the emerging judicial practice of ESG disputes, the attention of courts is paid to the form of statements, when considering such disputes, it is proposed to use the tools developed within the framework of judicial linguistics to identify the meaning (content) of units of various levels, determine the meaning or form of expression of meaning and classify it to a certain class (statement, opinion, assessment, assumption), establishing the presence or absence of certain content in the text, etc. The author proves that ensuring media security is an integral part of sustainable development and proposes a definition of the media security and reputational security of an organization (project). The paper provides for the risk management model of the media security of the organization (project). It is proved that from the point of view of project management, media security risk management claims to be an independent group of processes within the framework of project management. Media security risk management relies on the tools embedded in the methodology of the law of sustainable development: on system analysis and decision-making theory.

МЕДИАОБРАЗОВАНИЕ И МЕДИАКОМПЕТЕНТНОСТЬ

Media education and media competence are concepts that have entered our education and play an important role in it. A huge flow of information requires its critical understanding, and this requires skills that allow you to distinguish fake information from the truth. One of the main tasks of media education is to lay the foundations of the main competencies of a lifetime. The modern model of education presupposes education during the period of readiness for retraining, a culture of knowledge renewal

Smart Health Consulting and Online Health Shopping Portal with Product Recommendation System

Healthcare industry has become a big business. The healthcare industry produces large amounts of healthcare data daily that can be used to extract information for predicting diseases that can happen to a patient using health data. There are many tools related to disease prediction. But generally, there are no such tools that are used for the prediction of general diseases. So this system helps for the prediction of general diseases with great accuracy. As the demands for herbs and natural medication rose due to the recent outbreak. With that, an enormous amount of fake information is being passed on the internet. Lack of a centralized database of a person, and because of that, it is impossible to take proper precautions for the prevention of any viral disease. One cannot take proper care of themselves without a doctor's consultation. And visiting a doctor frequently is neither efficient nor affordable.

Detection of Malicious Social Boats Using Learning Automata with URL Feature

Bots have made an impact on a variety of social media platforms. Twitter has been hit particularly hard, with bots accounting for a sizable amount of its user base. These bots have been used for nefarious purposes like distributing fake information about politicians and increasing celebrities' perceived popularity. These bots have the ability to alter the outcomes of standard social media analysis. Malicious social bots have also been employed to spread incorrect information (for example, emailing fraudulent urls), which can have real-world effects. To detect such hostile behaviors, the suggested systems employ machine learning methods such as Naive Bayes and RF.

Stemming the Tide of Fake News about the COVID-19 Pandemic

While the world has been combating COVID, there has also been an ongoing “Infodemic,” caused by the spread of fake news about the pandemic. Due to the rapid data sharing on social media, the impact of fake news can be quite damaging. Citizens might mistake fakes news for real news. Human lives have been lost due to fake information about COVID. Our goal is to identify fake news on social media and help stem the spread by deep learning approaches. To understand the different characteristics in fake and real news, we conducted behavioral and sentiment analyses between fake and real news regarding the COVID pandemic. We then further built detection models based on feature elimination, and we identified differences of model robustness based on selected features.

Consumer Artificial Intelligence Mishaps and Mitigation Strategies

Although artificial intelligence (AI) promises to deliver ever more user-friendly consumer applications, recent mishaps involving fake information and biased treatment serve as vivid reminders of the pitfalls of AI. AI can harbor latent biases and flaws that can cause harm in diverse and unexpected ways. Before AI becomes interwoven into human society, it is important to understand how and when AI can fail. This article presents a timely survey of AI-induced mishaps that relate to consumer applications. The article also offers suggestions on mitigating strategies to manage the undesirable side effects of using AI for consumer applications. It, therefore, serves a dual purpose of creating awareness of current issues and encouraging other researchers in the consumer technology community to build better AI consumer applications.

Publishing Fake Information Online-Case of Online Vietnam Magazines (Thanhnien Newspaper, Tuoi tre newspaper, Vietnamnet.vn, dantri.com.vn, giaoduc.edu.vn, sctv.com.vn, etc.) From an Approach of German and EU Laws and Cybersecurity Regulations

Publishing fake information have at least three bad effects on the community: creating disinformation, anxiety, and disorder in society. Still using qualitative analytical methods with synthesis and inductive methods, the authors will address 2 cases of Vietnam magazines: thanhnien. vn and tuoitre.vn, vietnamnet. vn, giaoduc.edu.vn, dantri.com.vn, (online newspapers) and recently sctv.com.vn with their issue of publishing fake news online, which increasing as a problem in recent years 2015-2020. In this paper, we also use the European approach and laws on exploring the issue of publishing and delivering false information via the internet and social media. Last but not least, the views and ideologies of V.I Lenin and Ho Chi Minh on journalism and journalists are mentioned for educating the young generation.

A Novel Machine Learning Approach for Forgery Detection and Verification in Digital Image

Image manipulation can cause many ethical, economical, and political issues for everyone. It can be used to create fake information, fake ids, fake online profiles, fake news. A proper technique for image detection and verification is thus, a hot research area, which helps the affected people to overcome the forgery attacks. Due to the increased interaction among people through social networking sites, image forgery is prevalent and it is an essential activity to detect any forged image. This paper’s main objective is to identify fraud or tampered images effectively. By using the methods of copy move forgery detection, the obtained results are more accurate in a minimum time as compared to previous algorithms.

User perceptions of fake news sharing behavior on social media through social networking sites

In the age of information technology, online social networks are part of our daily lives and are the main source of obtaining and transmitting information, which can be a blessing or a curse. Although social networks facilitate access to news and information, one issue remains of serious significance, namely the phenomenon of fake news. The short time of spreading fake news in the online social environment is the main cause for concern, and users' attitudes towards fake news can facilitate or reduce its spread. Therefore, the main objective of the current study is to perform an overall analysis of users' perceptions on the behavior and attitudes toward distributing fake news through social networks. To ensure a comprehensive interpretation of the research topic, we analyzed both the reasons behind the behavior of distributing fake news and the active or passive actions that users apply in relation to them. As verifying the authenticity of the source is an essential component of the preventive behavior of fake information distribution, an analysis of the action was performed to verify the credibility of the sources among users. Therefore, the detailed and joint analysis of the above variables gives a note of originality to this study. In addition, the results of the study have significant practical implications for social platforms and are intended to provide a better understanding of how online social network users perceive fake information and interact with it. More specifically, they can be used in the development of predictive models that have the role of automating the identification of fake news in the context of machine learning algorithms and big data.

POLITICAL POLARIZATION AND ITS IMPACT ON MENTAL HEALTH: WHERE DO WE STAND?

POLITICAL POLARIZATION AND ITS IMPACT ON MENTAL HEALTH: WHERE DO WE STAND?

Identifying Informative Coronavirus Tweets using Recurrent Neural Network Document Embedding

The coronavirus pandemic has led to the spread of tremendous fake news and misleading information through tweets. Hence, an interesting task of classifying tweets into informative and uninformative has motivated researchers to employ machine learning techniques. The state-of-the-art studies showed high dependency on transformers architecture. However, the transformers architecture suffers from the catastrophic forgetting problem where important contextual information is being forgotten by the gradients. Therefore, this paper proposes a document embedding using Recurrent Neural Network. Lastly, three classifiers of LR, SVM and MLP have been used to classify documents into Informative and Uninformative. Using the benchmark dataset of WNUT-2020 at Task 2, LR classifier obtained the highest f-measure of 0.91. This result demonstrates the efficacy of RNN to generate sophisticated document embedding.

Social and Communication Mechanisms of Adaptation of the Population to the Crisis Conditions of the Pandemic in the all-Russian, Regional and Sectoral Context

The modern era is analyzed as an era of crises. In the XX and XXI centuries, the crises intensified and became permanent. Crisis conditions are understood, on the one hand, emergency situations, and on the other – relationships within the framework of social conflict. Both occur during the coronavirus pandemic. The authorities, commercial structures, public organizations strive to develop and implement mechanisms for adapting the population to the crisis, which have both an economic and non-monetary nature. The article discusses social and communication campaigns that complement the measures to directly combat the pandemic and are aimed at optimizing crisis moods. An overview of current research is given, examples of communication actions of state organizations at the federal and regional levels, commercial structures in various industries are given; the need to counteract fake information and conspiracy versions of the pandemic is justified.

Половое и сексуальное образование как мера индивидуальной профилактики сексуальных преступлений среди несовершеннолетних

Illiteracy in interpersonal sex relations, as well as in matters of sex communications, contributes to the development of tolerance of Russian society to sexual violence, a decrease in the age of persons committing sex crimes, a steady increase of violent crime rates (crimes against sexual freedom as well as statutory crimes), and other illegal acts of sexual nature. The mass media offers veiled propaganda of same-sex marriage, impunity, and promiscuity. Violence is increasingly used as a form of conflict resolution. The lack of skills and abilities to identify fake information on the Internet and the media by the rising generation leads to brainwashing, destructive types of interpersonal relationships between minors, the perception of violence as a normal way of life in society, and deformation of sexual relations among young people. The state of health of adolescents is also of concern, as the number of crimes committed by minors under the influence of alcohol or drugs has increased. Among other unsettling trends, in recent years, the number of minors and young adults infected by sexually transmitted diseases has increased as well. Despite the fact that the number of abortions among adolescents has decreased, only in 2020, about 630 thousand abortions were performed on women of reproductive age – that number on average corresponds to the population of Ulyanovsk. The primary cause of the current state of affairs is the lack of appropriate sexual education among minors and young adults, as well as a lack of spiritual and moral guidance.

A Method for Detection of Rare Industrial Fake Film Based on Deep Learning

In order to solve the industrial fake film problem produced by repeated imaging in the field of nondestructive testing, this paper proposes an image similarity comparison algorithm of X-ray film based on the Siamese neural network. Firstly, in order to accurately locate and detect weld areas from high-resolution X-ray film, a robust location and detection algorithm based on the Gaussian function is proposed. Next, in order to solve the problem that the deep learning model system extracts features according to squares by default, which results in the disappearance of film features with high horizontal and vertical features, effective weld regions are used and processed in blocks. Then, a small sample data set for weld similarity discrimination is established by using each block region, and an industrial fake film discrimination model based on twin neural networks is constructed. Finally, the corresponding weights are set for different fake feature information areas, and a similarity comparison system for industrial fake film detection is proposed. The experiment shows that the method in this paper can not only accurately locate the weld area but also accurately detect the fake film in the industry, avoiding the influence of the geometric transformation of the image and the insufficient number of data sets in the process of similarity comparison, and has a good detection effect.

Consumers’ untrust and behavioral intentions in the backdrop of hotel booking attributes

PurposeThe study aims to investigate how hotel booking attributes (i.e. perceived privacy, perceived certification and perceived assurance) engender consumers’ untrust and consequent behavioral intentions (i.e. altruistic behavior and trusting intentions). It also unveils the role of hotel attributes performance as a moderator between hotel booking attributes and consumers’ untrust.Design/methodology/approachData were collected through an online platform by engaging 454 Chinese respondents. SPSS 25.0 and AMOS 24.0 (structural equation modeling) were used for data analysis and interpretation.FindingsResults demonstrate that hotel booking attributes positively substantiate consumers’ untrust which, in turn, develops altruistic behavior and negative trusting intentions. Moreover, hotel attribute experience significantly moderates the relationships between perceived privacy, perceived certification and consumers’ untrust. Notably, hotel attribute performance insignificantly influences the association between perceived assurance and untrust.Research limitations/implicationsThis study used the Chinese context and examined Chinese domestic travelers and the nonbranded hotel industry. Notwithstanding its limitations, the findings help hospitality and tourism firms, en bloc, to manage their review websites by explicitly disclosing policies regarding customers’ privacy and assurance, winning their trust through third-party certification and employing data scientists to develop algorithms to sieve fake information proactively.Originality/valueThis study develops an original conceptual framework by using the untrust model in this research. Our findings add to the research on consumer behavior, information processing, service management and trust and suggest practical implications for hospitality firms.

Media Source Characteristics Regarding Food Fraud Misinformation According to the Health Information National Trends Survey (HINTS) in China: Comparative Study.

BackgroundOngoing rumors and fake news regarding food fraud, adulteration, and contamination are highly visible. Health risk information circulating through media and interpersonal communication channels has made health crisis an important research agenda.ObjectiveThis study explored the issue of food fraud and the effect of misinformation. Further, it assessed whether and how these issues have provided evidence-based interventions for food handlers and regulators to mitigate fraud misinformation.MethodsThe Health Information National Trends Survey (HINTS) was adopted for a collaborative study in China, after which a cross-sectional survey with door-to-door interviews was performed. Participants from Beijing and Hefei were selected using multistage sampling of adults in May 2017. Based on 4 government surveillance reports on food rumors and safety incidents, a descriptive analysis, correlation analysis, and analysis of variance were performed on the data.ResultsA total of 3090 results were gathered and analyzed. Among the respondents, 83.6% (2584/3090) heard at least one food rumor. Learning about food fraud was correlated with interpersonal connections (eg, doctors or health specialists) for accessing food health information. Overall, Chinese citizens with a higher level of interpersonal connection were more likely to be concerned about food incidents with a statistical difference (P<.001). Interpersonal connection was the most frequent communication source (698/1253, 55.7%), followed by traditional media (325/1253, 25.9%) and internet portals (144/1253, 11.5%). There was a significant relationship between media use and media category in Beijing (P<.001) and Hefei (P<.001). Overall, responses to food fraud and incident risks were lower in Beijing than in Hefei (P=.006). The respondents in Beijing were confronted more frequently by food rumors (range 346-1253) than those in Hefei (range 155-946). The urban dwellers in Beijing and their rural counterparts in Hefei also differed in terms of perceiving different levels of food risks from different media sources. The food rumor narratives that examined the conspiracy belief showed that social media played more important roles in influencing attitudes against misinformation for users in Hefei than in Beijing.ConclusionsThis study shows that consumers have to be on guard against not only fake food, but also spreading fake information and rumors, as well as conspiracy beliefs involving fake food. This study focused on characterizing media sources, types of food fraud misinformation, and risk perceptions of food safety, which mix urgency and suspicion, and attempted to provide evidence-based interventions for risk management guidance, with the hypothesis of significant correlations between media types and sources, and consumer exposure and perception levels of food rumors and risks.

SCLAVOEM: hyper parameter optimization approach to predictive modelling of COVID-19 infodemic tweets using smote and classifier vote ensemble.

Fake COVID-19 tweets are dangerous since they are misinformative, completely inaccurate, as threatening the efforts for flattening the pandemic curve. Thus, aside the COVID-19 pandemic, dealing with fake news and myths about the virus constitute an infodemic issue, which must be tackled by ensuring only valid information. In this context, this study proposed the Synthetic Minority Over-Sampling Technique (SMOTE) and the classifier vote ensemble (SCLAVOEM) method as a fake news classifier and a hyper parameter optimization approach for predictive modelling of COVID-19 infodemic tweets. Hyper parameter optimization variables were deployed across specific points of the proposed model and a minority oversampling of training sets was applied within imbalanced class representations. Experimental applications by the SCLAVOEM for COVID-19 infodemic prediction returned 0.999 and 1.000 weighted averages for F-measure and area under curve (AUC), respectively. Thanks to the SMOTE, the performance increases of 3.74 and 1.11%; 5.05 and 0.29%; 4.59 and 8.05% was seen in three different data sets. Eventually, the SCLAVOEM provided a framework for predictive detecting ‘fake tweets’ and three classifiers: ‘positive’, ‘negative’ and ‘click-trap’ (piège à clics). It is thought that the model will automatically flag fake information on Twitter, hence protecting the public from inaccurate and information overload.

Injecting Disinfectants to Kill the Virus: Media Literacy, Information Gathering Sources, and the Moderating Role of Political Ideology on Misperceptions about COVID-19

ABSTRACT Fake information about COVID-19 continues to circulate widely, including false causes and cures. The current study examined the (a) relationship between information gathering sources and misperceptions; (b) association between literacy variables and misperceptions; and (c) the moderating role of political ideology on these relationships. Conservative ideology, younger age, conservative media use, information gathering from social media, and information gathering from Donald Trump were positively associated with COVID-19 misperceptions. Meanwhile, information gathering from local media, CDC, and scientists was negatively related to COVID-19 misperceptions. Interaction models showed critical conditional patterns with political ideology. For example, liberals with higher media literacy for content held lower COVID-19 misperceptions, but this did not hold true for conservatives. The results revealed a need to facilitate more exposure to alternative viewpoints to counteract the echo chamber of misinformation that conservatives appear to trust regardless of self-reported media literacy.

An Awareness and Acceptance of the COVID-19 Vaccine among Foreign Students in Higher Institutions in China

The COVID-19 vaccination drive has made much buzz around the world. The vaccination is been discussed around the global for a long time and lot of rumors and fake information spreads all over the social media. The situation has created many fears and uncertainties among people which is affecting the vaccination drive in many countries. This study investigates the approach Chinese universities used in the vaccination drive for international students and how they respond to this vaccination drive. The study used a mixed-method approach to collect data from the respondents. Both their quantitative responses and their narratives were considered as the base for the analysis. The results of this study found that Chinese higher educational institutions approach for international students was very formal. On one end, the administration was able to carry out the vaccination drive successfully; the approach was failed to eliminate the fears and nervousness of the international students regarding vaccines. Furthermore, while fear and nervousness are found among the international students regarding the vaccination, most of the students did acknowledge the benefits of taking the vaccine for their health and as a gateway to move around or outside the country. The study highlights a crucial administrative approach from the Chinese higher educational institutions regarding an ongoing phenomenon. The results of this study can be used for administrative purposes so that the students' feedback should be considered while implanting some policy for their satisfaction and better results.