Spread Of False Information Research Articles

Impact of AI on Communication Relationship and Social Dynamics: A qualitative Approach

The integration of artificial intelligence (AI) into social media platforms has ushered in a new era of digital communication, offering unprecedented opportunities for content curation, relationship-building, and information exchange. Through this qualitative study, we have explored the multifaceted impact of AI-driven algorithms and natural language processing (NLP) technologies on user experiences and societal dynamics, addressing key research objectives and questions. Our findings underscore the transformative potential of AI in enhancing content curation and user interaction on social media platforms. Participants highlighted the benefits of personalized content recommendations and AI-enabled features such as chatbots, which streamline user interactions and provide instant support. Furthermore, AI algorithms play a crucial role in facilitating relationship-building through friend suggestions and group recommendations, fostering community engagement and social connections among users. However, alongside these benefits, our study also revealed significant challenges and ethical concerns associated with AI integration in social media. Participants expressed concerns about the proliferation of echo chambers and misinformation, fueled by algorithmic biases and the spread of false information through social bots. Privacy considerations emerged as a prominent issue, with participants emphasizing the need for transparency and accountability in AI implementation to safeguard user data and mitigate risks of algorithmic surveillance. In light of these findings, it is evident that the responsible deployment of AI technologies is paramount in ensuring positive user experiences and preserving the integrity of digital information ecosystems. Ethical considerations must guide the design and implementation of AI-driven algorithms, prioritizing transparency, fairness, and user empowerment. Platform operators, policymakers, and civil society stakeholders must collaborate to develop robust regulatory frameworks and governance mechanisms that uphold ethical standards and protect user rights in the digital age.

Fake News Detection

The spread of false information on the internet has become a major social issue, casting doubt on the veracity of information shared on these platforms. This study uses cutting-edge methods from machine learning (ML) and natural language processing (NLP) to present a complete framework for the detection of fake news. The purpose of this paper is to develop a model for detecting bogus news. A model is selected by using supervised learning techniques. In addition, we categorize news stories as real or fraudulent using the Naïve Bayes, Logistic Regression, and Random Forest algorithms. Our methodology offers an approach to false news identification that is more robust by taking into account the credibility of the news sources in addition to the content of the news. Using labeled datasets of fictitious and authentic news stories, we train our algorithms. A few methodologies were compared to achieve varying degrees of accuracy. When compared to the other two models, Random Forest is thought to have produced the best results in terms of accuracy. We assess our framework's effectiveness using real-world news articles and benchmark datasets, showcasing its versatility in correctly recognizing false information in a variety of settings and domains. We demonstrate the advantages of our method in terms of detection accuracy, scalability, and computational efficiency by comprehensive experimentation and comparative analysis. All things considered, our suggested framework is a major step forward in the fight against the dissemination of false information on the internet and provides a workable way to lessen the negative effects of fake news on people, communities, and society at large.

Enhancement of random forest algorithm applied in fake news detection

The widespread increase of fake news presents a serious obstacle in today's information-sharing situation, making it more difficult to distinguish fact from fabricated information. The study aims to improve machine learning algorithms' capacity to identify fake news to address this significant issue. To improve detection accuracy, Researchers applied a method that combines the Random Forest algorithm with Adaptive Boosting, or AdaBoost. By utilizing ensemble learning techniques, our method successfully leverages the collective intelligence of numerous decision trees, outperforming traditional approaches. By carefully experimenting with hyperparameters using the Random Search, The proposed method improved the algorithm's capacity to identify significant patterns suggestive of false information. The integration of AdaBoost to Random Forest produces a significant improvement, from 92.56% to 99.79% accuracy average difference, with an accuracy gain of 7% over the baseline Random Forest model. This improvement emphasizes how effective ensemble methods are at negotiating the complexities in terms of fake news detection. This study adds to the ongoing efforts to protect the integrity of information in the digital era by showcasing the efficacy of sophisticated machine-learning approaches in addressing the complex problem of fake news. The study underscores the critical importance of continuous innovation and adaptability in combating the increase of misinformation. Through interdisciplinary collaboration and technological advancements, the aim is to fortify defenses against the detrimental spread of false information, fostering an information ecosystem that prioritizes truth and resilience.

Urgensi Etika Dalam Literasi Digital Di Era Globalisasi

The purpose of this studywas to describe the importance of ethics in the context of digital literacy in the current era of globalization. By understanding the importance of ethics in digital literacy, it is hoped that this research can provide insights regarding optimizing the benefits of digital technology based on the principles of digital ethics.This research uses a library research method; the data is collected from source books, research articles, and other sources relevant to the topic discussed.The library research results showed that digital literacy has a very important role in facing complex challenges in the current era of globalization. Ethics in digital literacy acts as a guideline in using technology so that it has positive effects and minimizes negative effects. Awareness of digital responsibility is the foundation for a smarter and more empowered society in this digital era. Digital literacy ethics are very important in several ways, such as preventing the spread of false information, fighting cyberbullying and harassing speech, and respecting user privacy. Challenges in implementing digital literacy ethics include a lack of awareness of digital ethics, inequality in access and mastery of technology, regulatory and law enforcement challenges, and the role of education in overcoming challenges. Therefore, the importance of ethics in digital literacy and the role of education is crucial to overcoming various challenges that arise along with the development of technology and information.

Understanding the "Infodemic" Threat: A Case Study of the COVID-19 Pandemic.

The coronavirus disease 2019 (COVID-19) pandemic is notable among infectious diseases for its distinctive impact, which has halted millions of livelihoods owing to strict social distancing rules and lockdowns. Consequently, millions of individuals have turned to online sources, particularly social media, to remain informed about the virus. The transition to digital sources has resulted in an abundance of information, including both accurate and misleading or false content being shared and consumed on online platforms, contributing to what is commonly referred to as an "infodemic." Although these platforms have been valuable tools for healthcare professionals and public health authorities in disseminating crucial public health messages, they have also aided in the spread of misleading and false information. The widespread dissemination of false information has been instrumental in propagating harmful beliefs and behaviors such as vaccine hesitancy, promoting discriminatory attitudes, and endorsing false beliefs about the efficacy of certain therapeutic products for treating COVID-19. False information has undoubtedly become a challenge and burden for governments, health professionals, and the general population. This review has three main objectives: (1) to assess the scope of the "infodemic" issue, including investigating the factors contributing to the spread of false information online; (2) to examine the multifaceted consequences resulting from false information; and (3) to argue that an interdisciplinary, multi-layered approach, encompassing a focus on prevention, deterrence, and education, should be adopted to prevent the conception and dissemination of false information in this modern digital age.

Dynamics of Citizen Participation in Forming Identity in Forming Citizen Identity

This research aims to investigate the dynamics of citizen participation in shaping citizenship identity through a literature review. The research method employed is a literature review, where data are obtained from previous studies encompassing various sources such as journals, books, and scholarly reports. Thematic data analysis was conducted to identify patterns, trends, and relationships between citizen participation and citizenship identity. The research findings highlight the crucial role of citizen participation in building a strong citizenship identity, while challenges such as opinion polarization and the spread of false information through social media are also identified. In conclusion, through collaborative efforts among the government, civil society, and the private sector, communities can strengthen meaningful citizen participation and reinforce the foundation of an inclusivecitizenship identity.

Indonesia's Digital Security Strategy: Countering the Threats of Cybercrime and Cyberterrorism

Cybercrime is becoming a very serious threat to Indonesia's national security. The pervasive nature of cyberattacks raises questions about the effectiveness of current regulatory measures. This research aims to analyze how the government addresses security challenges in cyberspace in a sustainable manner. A descriptive qualitative method was used to evaluate the impact of cybercrime in Indonesia and its relationship with highly interrelated regulations. The results show that the threat of cybercrime and cyber-terrorism, in both physical and digital forms, has increased alarmingly in recent years. The impact of universal access to all relevant information has several consequences, including the spread of false information and the development of cybercrime networks. Significant measures are needed to disrupt the cyber terrorism industry to create a safe environment in Indonesia. The success of regulatory measures in Indonesia relies on a holistic approach involving the active participation of the government, society, and the private sector.

FAKE NEWS DETECTION

In order to counteract the spread of false information, this study explores the use of machine learning techniques, particularly decision tree classifiers and TF-IDF (Term Frequency-Inverse Document Frequency). Using a dataset of tagged news items, the model is trained to identify patterns that differentiate reliable content from unreliable information.A survey's analysis reveals a worrying trend: fake news has been more common from 2017 to 2018, growing gradually before experiencing a noticeable spike in 2019. But applying current models in 2020 has resulted in a slowdown in the spread of false information.A well-liked machine learning approach called the decision tree classifier provides interpretability by showing decisions as a tree structure. This helps to clarify how the model determines which news is real and which is phony by identifying important characteristics that contribute to the categorization process.In contrast, TF-IDF plays a crucial role in natural language processing tasks by assigning weights to terms according to their significance within a corpus of documents. Through the integration of TF-IDF, the model is able to better identify misinformation by capturing the meaning of words in news stories.All things considered, this study offers a viable strategy to deal with the growing problem of false news by using machine learning techniques and conducting an empirical examination of its effects over time Keywords – Include at least 4 keywords or phrases, must be separated by commas to distinguish them.

Deep Fake Detection System

Deep learning methods are used by the Deep Fake Detection System to recognize "deepfakes," or distorted media content. Deepfakes are artificial media produced by sophisticated artificial intelligence algorithms that threaten the credibility of media. The goal of our project is to create a reliable system that can discriminate between authentic and modified content in order to stop the spread of false information and protect media integrity. Our goal is to improve deepfake detection efficiency and accuracy by conducting a thorough evaluation of deep learning-based detection techniques. Our technology aims to offer real-time detection capabilities by utilizing sophisticated neural networks and machine learning techniques. This will aid in the continuous endeavors to tackle the widespread occurrence of deepfakes in digital media. Key Words: Deep Fake Detection, Deep Learning, Media Integrity, Misinformation, Neural Networks

Information Warfare in the Digital Age: Legal Responses to the Spread of False Information under Public International Law

Information Warfare in the Digital Age: Legal Responses to the Spread of False Information under Public International Law

Internet Culture as a Nomadic model: representation of the Unconscious

The article examines the issue of the formation of a new reality through Internet culture. The subject of this work is the nomadic model of the World Wide Web. The object is modern news programs presented on the Internet. The purpose of the study is to identify patterns that influence the formation of a new genre of media as a way of communication in the Internet environment. The peculiarity of the Internet device allows us to develop the concept of a viewer-author due to a high level of interactivity. The presence of a multitude of information expressed through an uncontrolled nomadic model creates an atmosphere of a new "reality" based on false freedom and pseudo-trust. In addition, the creation of a "virtual double" by the viewer contributes to the manifestation of hidden emotions and antisocial behaviors as a manifestation of the archetype "Shadow" (K. Jung). The article puts forward a hypothesis: the peculiarity of the Internet networks, expressed through the nomadic model, creates a platform for the realization of the unconscious viewer. This work is based on the nomadic model of culture developed by philosopher J. Deleuze and psychiatrist F. Guattari. Through the concept of psychoanalysis by K. Jung and J. Lacan examines the concept of the unconscious, as well as the "virtual reality" of S. Zizek, necessary for the most complete analysis of the transmission of information through Internet genres. The relevance of the research is emphasized by the need to comprehend the processes of constructing reality in the Internet network, through new information genres, in conditions of high interactivity. The novelty of the work lies in a new theoretical understanding of the experience of receiving and transmitting information via the Internet, one of the main participants of which is the viewer himself. The analysis of information production methods becomes especially necessary in conditions of reduced data moderation, which leads to the spread of false information in modern society. The author's special contribution to the research of the topic consists in the analysis of information and analytical programs, in particular "Caution: News", presented on the Internet platform. The main conclusion of the conducted research is the confirmation of the scientific hypothesis: the Internet environment is a nomadic model, which is a platform for the realization of the unconscious viewer.

Evaluating the Impact of Social Media News on the Level of Students’ Fear and Anxiety during the COVID-19 Pandemic

Background: The spread of false and inaccurate news on social networks has increased during the COVID-19 pandemic, leading to fear and panic among users globally and posing a threat to their mental well-being. Aim: This study aims to investigate the impact of social networks on inducing fear and panic among students amidst the COVID-19 outbreak. Methods: This cross-sectional study was conducted at Varastegan Institute for Medical Sciences in December 2020, involving a sample of 261 students selected through the available sampling method. The data collection tool utilized was a questionnaire, subjected to content analysis for validation. The questionnaire's reliability, determined through the test-retest method, yielded a score of 0.825. Results: Survey results revealed that virtual social networks such as Telegram (73.9%) and Instagram (65.9%) were the primary platforms for receiving COVID-19 news during the pandemic. Furthermore, 62% of students reported using these networks to access health and COVID-19-related information. Moreover, 36% of respondents indicated experiencing psychological distress due to the dissemination of COVID-19 news. Conclusion: The spread of false information on social media significantly contributes to heightened anxiety and fear among students, leading to increased psychological distress. In order to mitigate the adverse effects of misinformation on social media, it is crucial to educate students on discerning between accurate and inaccurate news. Empowering students with skills to navigate reliable sources can enhance their knowledge and help them avoid unnecessary psychological distress.

The mnemonic effect of central and peripheral misinformation on social media

ABSTRACT The increasing use of social media has amplified the spread of false information. Yet little is known about the mnemonic consequences associated with exposure to different types of false information online. In two studies, we examined in a simulated online context how exposure to false information either central or peripheral in events affected memory. European American and Asian/Asian American college students (Study 1 N = 200; Study 2 N = 225) were presented with GIFs of daily life events and read tweets about the events that included four types of information: central true information, central false information, peripheral true information, and peripheral false information. They then took a True/False recognition test that included tweeted and untweeted true and false information and indicated how confident they were in their responses. Regardless of cultural background, participants in both studies demonstrated the misinformation effect, whereby they falsely recognised more and resisted less tweeted than untweeted false information. Furthermore, they showed higher susceptibility to peripheral than central false information exposed via tweets. Asian participants were less influenced by misinformation than European Americans in Study 2. These findings have important implications to combat misinformation in online environments.

Fake News Detection On Social Media:Review of Literature

In today's world, social media has become one of the most accessible sources of news for people worldwide due to its low cost, easy accessibility, and rapid dissemination. However, the harmful effects of fake news on individuals and society have been evident for a long time. Despite the challenges in detecting false information, numerous scholars are working to understand the issue and its characteristics. The purpose of this paper is to analyze contemporary frameworks that make use of different machine learning techniques in order to further our understanding of false news identification. The efficiency of these frameworks in recognizing and halting the spread of false information may be compared by examining various methods. A particular technique was used in the review process to provide a thorough and instructive assessment. Even though a lot of research has been done to identify false news, more can be done in the future. These studies have many limitations such as biased data, adaptability, credibility analysis and the news is only categorized as legitimate or unauthentic by their solutions. Nevertheless, a system of ratings or scores to assess the reliability of news is necessary for a workable approach.

The Educational Affordances and Challenges of ChatGPT: State of the Field

ChatGPT was released to the public in November 30, 2022. This study examines how ChatGPT can be used by educators and students to promote learning and what are the challenges and limitations. This study is unique in providing one of the first systematic reviews using peer review studies to provide an early examination of the field. Using PRISMA principles, 44 articles were selected for review. Grounded coding was then used to reveal trends in the data. The findings show that educators can use ChatGPT for teaching support, task automation, and professional development. These were further delineated further by axial sub codes. Eight student uses were 24/7 support, explain difficult concepts, conversational partner, personalized feedback and materials, provide writing support, offer self-assessment, facilitate engagement, and self-determination. In addition to be affordances of the AI, the data from the articles also showed limitations to ChatGPT and misuses, specifically, inaccuracies and hallucinations, potential bias, and tool limitations. Misuses are plagiarism and cheating, privacy issues and spread of false information. This study is a springboard for researchers, practitioners, policy makers and funders in understanding the emerging state of the field of ChatGPT.

Detection of fake news from social media using support vector machine learning algorithms

Never happened before in human history the spreading of fake news; now, the development of the Worldwide Web and the adoption of social media have given a pathway for people to spread misinformation to the world. Everyone is using the Internet, creating and sharing content on social media, but not all the information is valid, and no one is verifying the originality of the content. Identifying the content's essence is sometimes complicated for researchers and intelligent researchers. For example, during Covid-19, misinformation spread worldwide about the outbreak, and much false information spread faster than the virus. This misinformation will create a problem for the public and mislead people into taking the proper medicine. This work will help us to improve the prediction rate. Here we investigate the ability of machine learning classifiers and deep learning models: Naive Bayes, Logistic Regression, Support Vector Machine, Decision Tree, Random Forest and K-Nearest Neighbor. Deep learning models include Convolutional Neural Networks and Long Short-Term Memory (LSTM). The various types of machine learning and deep learning models will be trained and tested using the Covid-19 dataset (1,375,592 tweets).

Legal Problems regarding the Crime of Fake News (Hoax) during the Covid-19 Pandemic

The problem of spreading fake news/hoaxes. This happens almost all over the world, including Indonesia. One example of hoaxes in Indonesia is the spread of false information in North Sumatra province, especially through social media. Thesis writing technique This research approach is a normative legal system. The results of the study show that Developing a Criminal Law Policy to Combat False Information Crimes Law Number 19 of 2016 concerning Amendments to Law Number 11 of 2008 concerning the Criminal Code, Law no. 1 of 1946 concerning Criminal Law Regulations, and Law Number 1 of 2023 concerning Electronic Transaction Information (ITE). Provisions related to the COVID-19 pandemic are in the updated Criminal Code.

The Self-perception Debate: Do social media and platforms need better regulation?

With the increasing development of social media in today’s society, the development of new media is also accompanied by the impact on self-identity and the spread of false information on the network, which makes user cognition more easily misled and leads to over-dependence. Based on the “cognitive dissonance theory”, “existence theory” and from the perspective of users and the background of the current use of social media, this paper analyzes the impact of social media on self-identity, the spread of rumors and false information, and excessive dependence, and finally discusses its regulation measures.

Visual Elements in Digital Technology Platforms and Visualized Communication in Spreading of False Information

Digital technology has demonstrated humongous benefits in spreading of information and awareness creation. Digital media platforms are observable facts that have turned out to be pervasive as the presence and use of digital platforms by netizens. Even more puzzling in several discourses is the near absence of the cultural strengthening and self-motivated of existence, particularly in Nigeria. This represents a new boundary of challenge. The use of visual elements in digital technology platforms and visualized communication in spreading of false information in Nigeria should be regulated. Its format is most used in promoting this practice. The major platforms used are WhatsApp, Facebook, YouTube and Instagram. This study utilized Survey design to obtain secondary data in literature and descriptive statistics were adopted using a self-structured questionnaire. The study adopts Technological Determinism theory and Disruptive Technology theory in observation of distinctiveness submissions to better knowledge of netizens behavioural pattern. Findings reveal that that digital technology user’s spread false information to gain attention, although significant confidence is placed on digital media platforms; many netizens hide their real identities. The study found out that false information spread through it creates panic and tension, pseudo identities dictate because sharing personal information violates long-standing culture of social identity secrecy entrenched in customary traditions in many parts of Nigeria. It was also found that emergence and continual spreading of false information need to be dealt with by imbibing societal value re-orientation for digital technology users; building their understanding and educating the masses. Spreading of false information has given rise to insecurity in Nigerian. Several unknown armed groups, tribal mercenaries and religious fundamentalist are proof of banditry and insecurity in Nigeria. Aside explaining themselves with overt and covert cultural distinctions of digital technology users, the study concludes that software engineers in Nigeria require developing modified software and re-categorizing bots accounts, and chat-bots especially into algorithmic template because of the socio-psychological peculiarities of the clime. The paper recommends the potential valuing and strengthening of digital technology platforms in ensuring reduction of spreading of false information in Nigeria.

The Influence of Internet-Derived Misinformation on Medical Treatment Decisions: A Social Learning Perspective

Purpose “Internet-Derived Information Obstructing Treatment” refers to the phenomenon where patients make their health decisions based on misinformation found online, leading to poor or harmful medical choices. DeGroot’s social learning model is used in this study to enquire at how false information spreads on the internet and influences people’s choices about treatment. We examine how likely patients will choose the wrong treatment, impacting their mental health, such as by making anxiety and depression symptoms worse. In addition, we examine the dynamics of low, high, and mixed levels of influence in the spread of false information. Methods We derived a mathematical framework to analyse the impact of peer-to-peer information sharing on medical decisions using DeGroot’s social learning model by modelling the process of belief updates in digital health networks. Each person’s decision-making is impacted by the views and information they encounter from others; this is known as the multiplier effect of social learning and is incorporated into the model. This method mimics the increasing likelihood that patients will encounter health-related disinformation as they engage with various online communities. Low misinformation influence, high misinformation influence, and mixed initial beliefs were the three scenarios that were modelled in the study. Treatment choices and mental health results were evaluated in light of each scenario. Based on patients’ exposure to false information through internet platforms, the odds ratios (ORs) for negative medical decisions, anxiety, and depression were computed. Results In all of these situations, people were much more likely to make a bad treatment choice when they had incorrect information. Repeated interactions with peers who had been influenced by online false information raised patients’ chances of making bad treatment choices by 50% in the high misinformation scenario (95% confidence interval [CI]: 1.25–1.80). Some patients who were given false information had 40% more anxiety and 35% more depression (OR: 1.40, 95% CI: 1.10–1.70 and OR: 1.35, 95% CI: 1.05–1.65, respectively). According to the model, social learning made false information more powerful, and this was most clear in situations where false information was common. Conclusion DeGroot’s social learning process is a good way to explain how false information found on the internet can affect a patient’s choice of treatment and mental health. Misinformation from the internet can lead to bad medical decisions and higher mental health risks, especially in networks where peers have a lot of power. The results stress the need for targeted interventions to improve digital health literacy and stop the spread of false information in online health communities. Future research should look into ways to stop the social learning processes that keep spreading false information.