Fake Information Research Articles

The Influence of the Dissemination of Fake Information on Social Networks on the Minds of Student Youth

The Influence of the Dissemination of Fake Information on Social Networks on the Minds of Student Youth

Hybrid RFSVM: Hybridization of SVM and Random Forest Models for Detection of Fake News

The creation and spreading of fake information can be carried out very easily through the internet community. This pervasive escalation of fake news and rumors has an extremely adverse effect on the nation and society. Detecting fake news on the social web is an emerging topic in research today. In this research, the authors review various characteristics of fake news and identify research gaps. In this research, the fake news dataset is modeled and tokenized by applying term frequency and inverse document frequency (TFIDF). Several machine-learning classification approaches are used to compute evaluation metrics. The authors proposed hybridizing SVMs and RF classification algorithms for improved accuracy, precision, recall, and F1-score. The authors also show the comparative analysis of different types of news categories using various machine-learning models and compare the performance of the hybrid RFSVM. Comparative studies of hybrid RFSVM with different algorithms such as Random Forest (RF), naïve Bayes (NB), SVMs, and XGBoost have shown better results of around 8% to 16% in terms of accuracy, precision, recall, and F1-score.

People's Voice: Exploring Discussion Themes on Generative AI and Libraries on X

ABSTRACTThe launch of ChatGPT and other Generative Artificial Intelligence (GenAI) models has significantly influenced people's perceptions of AI, making its impact a widely discussed topic in various areas. In Library and Information Science (LIS), researchers experience the opportunities and challenges presented by GenAI, such as using the rich text from books and digital collections as training data for GenAI and enabling quick summarization and reference consultation for patrons. Meanwhile, concerns have arisen about the generation of fake information and nonexistent text by GenAI and their potential effects on users. This project explores the themes in public discussions regarding the impact of GenAI on libraries. To explore public opinions about the impact of GenAI in libraries, we collected over 50,000 posts from X (formerly Twitter) between November 1, 2022, and May 6, 2024. We manually sampled 1,000 posts focusing on GenAI in the context of libraries. We conducted content analysis to identify the key themes and relationships among these themes. This poster presents our initial findings on different themes and highlights the trends in public opinion. Our analysis provides a curated dataset for training APIs for future investigation.

Dual-stream fusion network with multi-head self-attention for multi-modal fake news detection

With the rapid advancement of social media platforms like Weibo and WeChat, alongside the emergence of deepfake technologies, tackling fake information has become a major challenge for society and government institutions. To address this, developing efficient and intelligent methods for fake news detection is crucial. This paper introduces a dual-stream fusion network model (DSF-MHSA) based on deep learning, designed to detect fake news across web pages, images, and text. The model tackles issues such as cross-lingual discrepancies, data imbalance, and multimodal information fusion by integrating deep learning models like ERNIE-M, AlexNet, and ShuffleNet, along with three multi-head self-attention mechanisms. It processes textual and image data separately to capture long-range dependencies and global information, enhancing understanding and recognition. A unified multi-head self-attention mechanism then merges these insights to strengthen cross-modal correlation detection. The model is tested on datasets from Twitter, Weibo, and IKCEST-2023, which includes real online news from various social media sources. Results show that the DSF-MHSA model achieves over 90 % accuracy, surpassing traditional models in news detection tasks. This research offers significant practical value for the identification and understanding of news content.

Digital Literacy Education for Older Adults: A Scoping Review

ABSTRACTIn the context of global aging, digital technologies are increasingly recognized as a means to address the challenges associated with aging. Although many studies have explored the relationship between older adults and digital literacy, there is a gap identifying their necessary skills, challenges, and learning motivations. This review identified 30 articles from 2014 to 2024 and delineated five areas of digital literacy skills that older adults acquire, the hurdles they face in this process, and what drives their learning efforts. The difficulties faced by older adults in learning digital literacy include unfamiliarity with technology, cognitive decline, emotional barriers, limited access to digital resources, and physical and health issues. These challenges may lead to frustration in their use of devices and apps, which may affect their willingness to learn. This study, despite a small sampling size, paves the way for future research, including evaluating online fake information and addressing safety risks.

Effects of AI-Generated Misinformation and Disinformation on the Economy

This study investigates the potential consequences of AI-driven misinformation/disinformation on the economy, which the World Economic Forum has identified as the most significant threat to global stability in the near term. To determine the effects of false and/or fake information on the economy, qualitative research method which involves collecting and analyzing information that is rich in detail, context, and meaning was preferred within the framework of this study, and the following inferences and conclusions were drawn regarding the potential implications and consequences of AI-generated mis/disinformation. Mis/disinformation can severely damage consumer trust, leading to reduced revenue and tarnished reputations for businesses. Fake news and reviews can harm consumers by influencing their decisions and can damage brands, resulting in crisis management efforts and decreased consumer loyalty. In financial markets, dis/misinformation can create divergent opinions among investors, leading to market volatility. Within supply chains, the spread of false information can disrupt operations and have significant economic consequences. AI-driven disinformation can pose additional risks, potentially affecting political stability and economic policies. As AI technology advances, countries are implementing stricter regulations, such as the EU AI Act, which may increase compliance costs, particularly for smaller businesses. This study can be considered important as it aims to contribute to a growing awareness of the complex and multifaceted nature of the impact of AI on the economy by investigating the potential negative effects of AI.

Explainable Prediction Technique for Analyzing Information Disorder Using Fuzzy Rough Sets

A persistent challenge in the domain of social media pertains to the comprehension and representation of information disorder incidents, which include but are not limited to erroneous information, theories of conspiracy, and racial discrimination. These issues frequently become apparent in the written content of posts that are shared on networking platforms and multiple sites. The complexity of information disorder research stems from the wide range of internet epidemic genres and the diverse effects that such information can have on people. Furthermore, the difficulty is exacerbated by the emergence of generative AI models that are capable of generating information that is difficult to identify as synthetic. The findings can assist analysts and decision-makers in acquiring a more in-depth comprehension of the phenomenon occurring in the field of information disorder. Hence, Information Disorder Prediction using Fuzzy Rough Set (IDP-FRS) has been proposed to predict the disinformation in the collected domain accurately and identify fake and real information. Through the incorporation of fuzzy sets of data into modelling and prediction techniques, the research improves the accuracy and interpretability of models, hence facilitating the discovery of disinformation in a more trustworthy manner. Moreover, the introduction of explainable prediction approaches gives users the ability to make educated choices concerning the appropriateness and dissemination of information, which in turn promotes openness and confidence in predictive models.

A Deniable Encryption Method for Modulation-Based DNA Storage.

Recent advancements in synthesis and sequencing techniques have made deoxyribonucleic acid (DNA) a promising alternative for next-generation digital storage. As it approaches practical application, ensuring the security of DNA-stored information has become a critical problem. Deniable encryption allows the decryption of different information from the same ciphertext, ensuring that the "plausible" fake information can be provided when users are coerced to reveal the real information. In this paper, we propose a deniable encryption method that uniquely leverages DNA noise channels. Specifically, true and fake messages are encrypted by two similar modulation carriers and subsequently obfuscated by inherent errors. Experiment results demonstrate that our method not only can conceal true information among fake ones indistinguishably, but also allow both the coercive adversary and the legitimate receiver to decrypt the intended information accurately. Further security analysis validates the resistance of our method against various typical attacks. Compared with conventional DNA cryptography methods based on complex biological operations, our method offers superior practicality and reliability, positioning it as an ideal solution for data encryption in future large-scale DNA storage applications.

Improving fake job description detection using deep learning-based NLP techniques

ABSTRACT In the modern online society, where social networks are increasingly developed, the authenticity of information is one of the essential needs. There are many problems with detecting fake information, such as fake news detection, fake review detection, etc. Fake job description (FJD) detection is an interesting problem many groups have studied recently. However, current studies still need improvement in predictability. Therefore, this study develops a new method named NLP2FJD that utilizes deep learning-based NLP techniques for improving FJD detection. Firstly, we utilize the pre-trained Word2Vec to extract features from textual information from the dataset. Next, combining textual information and meta-information in the experimental dataset to improve the performance of FJD detection. The above two improvements will help the recommender system significantly improve the predictive ability of the proposed model. Finally, the empirical experiments are conducted to confirm the effectiveness of the proposed method on the experimental dataset compared with cutting-edge methods. The experimental results demonstrate that the NLP2FJD framework transcends other experimental methods for FJD detection on the experimental dataset. Besides, this study also conducts ROC curve analysis to show how to determine the optimal threshold for distinguishing the fake or real job description on the experimental dataset.

Development and problems of the national journalistic model of Kazakhstan, considering social, ethical, managerial, and marketing mechanisms of the media environment

ABSTRACT Journalistic activity in Kazakhstan is a complex system that occupies an important place in the development and promotion of a national modernized model of public consciousness. The purpose of the study is to analyze the features of the development of the communicative model of the periodical press of Kazakhstan. The following methods were used to achieve the objectives: systematization, survey, and comparative. The results of the study determined that journalistic activity manifests itself in both professional and civil contexts, while outlining the saturation of content on the Internet. The survey of correspondents and journalists determined that the most influential topic that forms the public consciousness of Kazakhs is politics. Due to the active promotion of innovative technologies and the Internet, the media market is overflowing with various information, including fake, which negatively affects the consciousness of society. In order to effectively create a national model of society, representatives of the mass media must adhere to the principles of ethics, and actively promote the fight against fake information together with government agencies and society. The practical significance lies in the use of the results of the study by academics and specialists in journalism and communication.

Believe in Artificial Intelligence? A User Study on the ChatGPT’s Fake Information Impact

Believe in Artificial Intelligence? A User Study on the ChatGPT’s Fake Information Impact

Fake social media news and distorted campaign detection framework using sentiment analysis & machine learning

Social networking platforms have become one of the most engaging portals on the Internet, enabling global users to express views, share news and campaigns, or simply exchange information. Yet there is an increasing number of fake and spam profiles spreading and disseminating fake information. There have been several conscious attempts to determine and distinguish genuine news from fake campaigns, which spread malicious disinformation among social network users. Manual verification of the huge volume of posts and news disseminated via social media is not feasible and humanly impossible. To overcome the issue, this research presents a framework to use sentiment analysis based on emotions to investigate news, posts, and opinions on social media. The proposed model computes the sentiment score of content-based entities to detect fake or spam and detect Bot accounts. The authors also present an investigation of fake news campaigns and their impact using a machine learning algorithm with highly accurate results as compared to other similar methods. The results presented an accuracy of 99.68 %, which is significantly higher as compared to other methodologies delivering lower accuracy.

Fake News Detection Using Machine Learning

Abstract: The pervasive spread of misinformation in today's digital environment presents a daunting problem, influencing public opinion and possibly causing unfavorable social consequences. This paper conducts a thorough investigation into the effectiveness includes a broad variety of false information in text, images, and video formats. Our study carefully assesses SVM, random forest, MLP, and naive Bayes classifiers using a large and painstakingly selected dataset. We provide a classifier of voting to improve accuracy and consistency by integrating the forecasts from these several models. Voting Classifier's outstanding performance, attaining a perfect accuracy rate and highlighting the ensemble approach's inherent robustness. The research emphasizes the vital ensemble methods' key part in strengthening the foundation the identification of bogus news systems and providing a strong barrier against the unrelenting propagation of fake information.

Survey for Detecting AI-generated Content

In large language models (LLMs) field, the rapid advancements have significantly improved text generation, which has blured the distinction between AI-generated and human-written texts. These developments have sparked concerns about potential risks, such as disseminating fake information or engaging in academic cheating. As the responsible use of LLMs becomes imperative, the detection of AI-generated content has become a crucial task. Most existing surveys on AI-generated text (AIGT) Detection have analysed the detection approaches from a computational perspective, with less attention to linguistic aspects. This survey seeks to provide a fresh perspective to drive progress in the area of LLM-generated text detection. Futhermore, in order to make the assessment more explainable, we emphasize the great importence of leveraging specific parameters or metrics to linguistically evaluate the candidate text.

Disinformation and Narrative Building: A Study on Chinese Local Applications

The current research aimed to measure the impact of information source, information channel, information verification,and information community response on narrative building in China. The motivation of this research was to provide recommendations to reduce the spread of fake information on social media platforms, which can harm the community. The population of this study was Chinese, and a sample of 390 respondents was considered for research findings. Partial Least Square – Structural Equation Model (PLS-SEM) was used to analyze data. The research empirically confirmed that information sources, information channels, information verification, and information community response are significant in narrative building in China. The findings of this research contributed to the inconclusive discussion by the previous studies in the literature. The study practically recommends the findings focus on information source, channel, verification, and community response before working on it

Meta-Regulation: An ideal alternative to the primary responsibility as the regulatory model of generative AI in China

Generative AI with stronger responsiveness and emergent abilities has triggered a global boom and is facing challenges such as data compliance risks during the pretraining process and risks of generating fake information, which has raised concerns among global regulatory authorities. The European Union, United States, United Kingdom, and other countries and regions are gradually establishing risk-based, scenario-based, and outcome-based governance models for generative AI. China recently introduced new regulations for the management of generative AI, which adopt a governance model focusing on generative AI service providers. It suggests that China is continuing the principle of primary responsibility in Internet governance, which encompasses legal responsibility, contractual obligations, and ethical responsibility. However, the governance model based on primary responsibility emphasizes the accountability of generative AI model service providers, with relatively limited regulation on other important entities such as users and large-scale dissemination platforms, which may not be conducive to achieving China's regulatory goals for the AI industry. In comparison, the Meta-Regulation model could be an ideal alternative for China. As a classic theory explaining the public-private relationship, the ‘Meta-Regulation’ aligns with the generative AI governance requirements. Based on the Meta-Regulation theory, the governance of generative AI in China should move towards a direction of emphasizing safety, transparency, collaborative governance, and accountability. In line with this, it is necessary to include users and large-scale dissemination platforms within the regulatory scope and establish overarching governance objectives that ensure the responsible distribution of duties among stakeholders, with regulatory authorities assuming ultimate oversight responsibility and technical coordination. At the level of specific improvement measures, it is possible to integrate the three stages of model development, usage, and content dissemination of generative AI. During the model development stage, generative AI providers have specific transparency obligations. In the usage stage, a self-regulatory system centered around platform autonomy should be constructed. In the content dissemination stage, the proactive notification obligations of the dissemination platforms should be clearly defined. Additionally, the enforcement of technical interoperability requirements is necessary, thereby promoting the orderly development of generative AI applications.

The spread of fake news: Disclosure willingness role

The epidemic has had a profound negative impact on individuals worldwide, leading to pervasive anxiety, fear, and mental instability. Exploiting these fears, a significant amount of fake information proliferates and spreads rapidly on social networks. This study explores the factors that cause individuals to believe fake news under stressful and fearful conditions by applying the truth-default theory. Data was collected online in Vietnam, using Smart PLS software to analyze the research model. The findings indicated that risk perception, media trust, trust in celebrity posts, and stress were factors that urge users to believe news posted on social media, and even they actively share this news on their own channels. Disclosure willingness moderated the relationship between adoption fake news and sharing it. Both theoretical and practical implications were discussed.

Digital Technologies as a Means of Exercising Electoral Rights of Сitizens of the Russian Federation

The paper provides an overview of digital resources used during election campaigns, highlighting their positive and negative sides supported by cases from practice. Electronic democracy is considered as a logical consequence of introduction of information technologies to the life of society. The paper analyzes remote electronic voting as a subsidiary way of implementing active suffrage in the Russian Federation; The author considers a "Mobile Voter" as an alternative to absentee ballots that meets the requirements of the time; digital services provided through the federal state information system "Unified Portal of State and Municipal Services", as well as the possibilities of artificial intelligence in the framework of elections. It is pointed to the promotion of digital resources for the implementation of not only active but passive suffrage, which is expressed in the possibility of collecting signatures by candidates online. We witness an increase in the importance of AI technologies during election campaigns, as well as the need for legislative regulation of the limits of their use due to the risks of spreading fake information that can influence the opinion of voters about running candidates and, as a result, the election results. Since artificial intelligence technologies are new, there are gaps in the legal regulation of the issue under consideration in the Russian Federation. Thus, the paper indicates the impossibility of solving the identified problem by the point adoption of certain norms and, using the example of foreign experience, emphasizes the need for a systematic approach to the development of an appropriate regulatory legal act and highlights the possibility of forming a new branch of law (a digital branch) due to the announcement of the adoption of the digital code.

Research on the Impact of Social Media on the Decision-Making of Young Consumers in the Context of Clothing Choices

To improving markets’ responsiveness towards consumers and then optimizing marketing strategy, it is of the essence to analyze how young consumers’ intentions are affected by social media and how the fashion industry develops and changes. To make the study more understandable and authoritative, the authors use the case of Zara, a well-known brand of casual wear, to reveal the background of fast fashion expansion and then provide a role model for position and target market, as well as a special marketing strategy. Afterward, a hand-conducted questionnaire by authors was carried out, specifically underlining the effectiveness of closing recommendations and driving purchasing decisions among young consumers. Leveraging influencers and utilizing user-generated content are approved to increase brand engagement and build brand loyalty. On the one hand, the shopping experience has been changed by technological convenience and transparent information. On the other hand, fake information and consumer privacy security are bringing risks currently. Simultaneously, competition on social media is intensifying. To solve these problems, governments and enterprises are responsible for fact-checking, verification, monitoring, reporting misinformation, and encoding data. Also, users are suggested to use private tools and raise security awareness. For companies, they can focus on creating unique consumer experiences and advocating ecologically conscious practices to increase competitiveness. In conclusion, the authors provide an analysis of social media and young consumers’ preferences, suggesting improvement and drawing a blueprint for the development of casual clothing.

A Method for Identifying False News using Deep Learning Approach

The quick access to information on social media networks as well as its exponential rise also made it difficult to distinguish between fake information or real information. The fast dissemination by way of sharing has enhanced its falsification exponentially. It is also important for the credibility of social media networks to distribute fake information. It thus became a study challenge to automatically check for misstatement of information through its source, content, or publisher. This paper demonstrates an approach to the identification by the artificial intelligence of false statements made by public figures. As a software system, two algos are being applied and a series of data tested. The highest result obtained for binary (true or false) labeling is 99 percent. Python 3.6 is the simulation method used here