Spread Of Fake News Research Articles

Controlling perpetrators of spreading fake news in the Southern Sumatra regional police

The continuous development of information technology brings significant changes in various fields of community life. Society and the internet have a very complex and significant relationship, especially in terms of using the internet as a means of socialisation and interaction. In the context of social media abuse, people are often trapped in complex issues that lead to hoaxes and hate speech. This study examines policies, control measures, and punishment for hoax spreaders in the South Sumatra Regional Police jurisdiction. This research employs qualitative research using empirical studies and a legal, sociological framework. This research aims to contribute to the corpus of literature and information regarding the perpetrators of spreading false news (hoax) at the South Sumatra Regional Police. The findings of this study reveal that criminal law enforcement against fake news spreaders (hoaxes) in the South Sumatra Regional Police have procedures or efforts to resolve cases of fake news (hoax); the procedure includes receiving reports/complaints, investigations, investigations, sending SPDP (Notice of Commencement of Investigation), sending case files, and transferring suspects and evidence. Law enforcement entities involved in combating fake news are the Police, Prosecutors, and Judges. This research provides the latest data in 2022 regarding cases of spreading fake news (Hoax) in the South Sumatra Regional Police.

GAMC: An Unsupervised Method for Fake News Detection Using Graph Autoencoder with Masking

With the rise of social media, the spread of fake news has become a significant concern, potentially misleading public perceptions and impacting social stability. Although deep learning methods like CNNs, RNNs, and Transformer-based models like BERT have enhanced fake news detection. However, they primarily focus on content and do not consider social context during news propagation. Graph-based techniques have incorporated the social context but are limited by the need for large labeled datasets. To address these challenges, this paper introduces GAMC, an unsupervised fake news detection technique using the Graph Autoencoder with Masking and Contrastive learning. By leveraging both the context and content of news propagation as self-supervised signals, our method reduces the dependency on labeled datasets. Specifically, GAMC begins by applying data augmentation to the original news propagation graphs. Subsequently, these augmented graphs are encoded using a graph encoder and subsequently reconstructed via a graph decoder. Finally, a composite loss function that encompasses both reconstruction error and contrastive loss is designed. Firstly, it ensures the model can effectively capture the latent features, based on minimizing the discrepancy between reconstructed and original graph representations. Secondly, it aligns the representations of augmented graphs that originate from the same source. Experiments on the real-world dataset validate the effectiveness of our method.

Unveiling Implicit Deceptive Patterns in Multi-Modal Fake News via Neuro-Symbolic Reasoning

In the current Internet landscape, the rampant spread of fake news, particularly in the form of multi-modal content, poses a great social threat. While automatic multi-modal fake news detection methods have shown promising results, the lack of explainability remains a significant challenge. Existing approaches provide superficial explainability by displaying learned important components or views from well-trained networks, but they often fail to uncover the implicit deceptive patterns that reveal how fake news is fabricated. To address this limitation, we begin by predefining three typical deceptive patterns, namely image manipulation, cross-modal inconsistency, and image repurposing, which shed light on the mechanisms underlying fake news fabrication. Then, we propose a novel Neuro-Symbolic Latent Model called NSLM, that not only derives accurate judgments on the veracity of news but also uncovers the implicit deceptive patterns as explanations. Specifically, the existence of each deceptive pattern is expressed as a two-valued learnable latent variable, which is acquired through amortized variational inference and weak supervision based on symbolic logic rules. Additionally, we devise pseudo-siamese networks to capture distinct deceptive patterns effectively. Experimental results on two real-world datasets demonstrate that our NSLM achieves the best performance in fake news detection while providing insightful explanations of deceptive patterns.

Uncovering the authorship: Linking media content to social user profiles

The extensive spread of fake news on social networks is carried out by a diverse range of users, encompassing private individuals, newspapers, and organizations. With widely accessible image and video editing tools, malicious users can easily create manipulated media. They can then distribute this content through multiple fake profiles, aiming to maximize its social impact. To tackle this problem effectively, it is crucial to possess the ability to analyze shared media to identify the originators of fake news. To this end, multimedia forensics research has advanced tools that examine traces in media, revealing valuable insights into its origins. While combining these tools has proven to be highly efficient in creating profiles of image and video creators, it is important to note that most of these tools are not specifically designed to function effectively in the complex environment of content exchange on social networks. In this paper, we introduce the problem of establishing associations between images and their source profiles as a means to tackle the spread of disinformation on social platforms. To this end, we assembled SocialNews, an extensive image dataset comprising more than 12,000 images sourced from 21 user profiles across Facebook, Instagram, and Twitter, and we propose three increasingly realistic and challenging experimental scenarios. We present two simple yet effective techniques as benchmarks, one based on statistical analysis of Discrete Cosine Transform (DCT) coefficients and one employing a neural network model based on ResNet, and we compare their performance against the state of the art. Experimental results show that the proposed approaches exhibit superior performance in accurately classifying the originating user profiles.

How does social media knowledge help in combating fake news? Testing a structural equation model

To counter the spread of fake news among social media users in Nigeria, this study modelled the moderating function of social media knowledge. To collect responses from participants throughout Nigeria's geopolitical zones, an online poll was run. The data were analysed using Smart PLS 3.6′s structural equation modelling (SEM). We discovered that among Nigerian social media users, instant news sharing, self-promotion, self-expression, and altruism are factors that contribute to the spread of false news. Particularly, altruism and self-promotion had a stronger impact on the dissemination of fake news. We discovered that among people with greater social media knowledge, the correlations between instant news sharing, self-promotion, self-expression, altruism, and false news sharing are less. This study's conclusion section highlighted how this result adds to both theory and practice.

The making of the boy who cried wolf: fake news and media skepticism

Abstract Deceiving citizens is typically considered the main political motive behind the spread of fake news. Accordingly, strategies to debunk fake news, such as fact-checking, have been suggested to combat it. However, the spread of fake news persists despite these debunking strategies. We propose an alternative but underexplored motive behind the spread of fake news: Fake news aims not only to deceive citizens but also to induce media skepticism. To support our claim, we present a stylized formal model of media skepticism and demonstrate that the incentive to spread fake news persists even if citizens are not deceived by disinformation coming from fake news. Our model highlights the dilemma embedded in fact-checking.

The Dangers of Hoaxes and Efforts to Overcome them in the 2024 Indonesian Presidential Election

Hoaxes are often the dominant element in every electoral process, whether in regional or presidential elections. The spread of fake news or false information through hoaxes can be done for a variety of purposes, ranging from jokes to serious political purposes. Incomprehension of political education and misuse of social media have become fertile fields for spreading hoaxes, especially in every election. In maintaining vigilance and facing the wave of hoaxes in the 2024 presidential election, research methods that can be used are content analysis and social network analysis. The purpose of this study is to detail the causes and consequences of the spread of hoaxes in the General Election, as well as present recommendations related to effective political education strategies to prevent and overcome the spread of hoaxes in the context of elections. This research is expected to contribute conceptually and practically to minimize the negative impact of spreading hoaxes on elections and increase public understanding of elections through the application of political education. To achieve this goal, this research will involve literature analysis and case studies of the spread of hoaxes in elections.

Identifying Fake News Using Long-Short Term Memory Model

Designed to deceive readers and manipulate public opinion, fake news can be created for a variety of reasons ranging from political propaganda to generating revenue through clickbait. Another significant challenge in combating fake news is the difficult balance between curbing misinformation and preserving free speech, though some argue for stricter regulations to control the spread of fake news. Thus, the purpose of this study is to identify fake news using Long-Short Term Memory (LSTM). LSTM models are often used to analyze the linguistic features of news articles or social media posts. The dataset we used comes from a dataset of fake news on Kaggle's website. The proposed method can identify fake news with average precision, recall, accuracy, and f-measure values of 0.94, 0.96, 0.94, and 0.95. The results showed that LSTM provides superior performance compared to the Support Vector Classifier, Logistic Regression, and Multinomial Naive Bayes methods.

Architecture of a three-dimensional convolutional neural network for detecting the fact of falsification of a video sequence

The article reflects the use of neural network technologies to determine the facts of falsification of the contents of video sequences. In the modern world, new technologies have become an integral part of the multimedia environment, but their proliferation has also created a new threat - the possibility of misuse to falsify the contents of video sequences. This leads to serious problems, such as the spread of fake news and misinformation of society. The scientific article examines this problem and determines the need to use neural networks to solve it. In comparison with other existing models and approaches, neural networks have high efficiency and accuracy in detecting video data falsification due to their ability to extract complex features and learn from large amounts of source data, which is especially important when reducing the resolution of the analyzed video sequence. Within the framework of this work, a mathematical model for identifying the falsification of audio and video sequences in video recordings is presented, as well as a model based on a three-dimensional convolutional neural network to determine the fact of falsification of a video sequence by analyzing the contents of individual frames. Within the framework of this work, it was proposed to consider the problem of identifying falsifications in video recordings as a joint solution to two problems: identification of falsification of audio and video sequences, and the resulting problem itself was transformed into a classical classification problem. Any video recording can be assigned to one of the four groups described in the work. Only the videos belonging to the first group are considered authentic, and all the others are fabricated. To increase the flexibility of the model, probabilistic classifiers have been added, which allows to take into account the degree of confidence in the predictions. The peculiarity of the resulting solution is the ability to adjust the threshold values, which allows to adapt the model to different levels of rigor depending on the task. The architecture of a three-dimensional convolutional neural network, including a preprocessing layer and a neural network layer, is proposed to determine fabricated photoreceads. The resulting model has a sufficient degree of accuracy in determining falsified video sequences, taking into account a significant decrease in frame resolution. Testing of the model on a training dataset showed the proportion of correct detection of video sequence falsification above 70%, which is noticeably better than guessing. Despite the sufficient accuracy, the model can be refined to more significantly increase the proportion of correct predictions.

The Extracurricular Role of Hizbul Wathan in Shaping the Nationalist Character of Students at Ma Muhammadiyah 1 Medan

This research aims to determine the role of Hizbul Wathan extracurriculars in shaping the nationalist character of students at MA Muhammadiyah 1 Medan. This research was conducted using qualitative research methods. Data collection techniques are carried out through observation, interviews and documentation. This research was conducted at the Hizbul Wathan (HW) MA Muhammadiyah 1 Medan cohesive organization, the research subjects were the supervisors, administrators and members of HW. The findings in this study show that; 1) The HW scout leaders and members do not yet have a complete understanding of the concept of nationalism, but implicitly in running the organization the management and members have actually demonstrated the seeds of the spirit of nationalism in every planning and implementation of activities. 2) Strengthening the organization with a foundation of nationalist spirit based on state ideology and Muhammadiyah values. Through strong organizational commitment, the spirit of nationalism will flow within the HW scout members. 3) HW scout members will become nationalists. This was demonstrated in HW's scouting activities at MA Muhammadiyah 1 Medan through respecting the flag, avoiding the spread of fake news, and not being involved in negative activities. To encourage the development of national values ??and a sense of nationalism in students, HW coaches, Civics teachers, and Social Sciences teachers use varied methods supported by flag ceremonies, sports competitions, and local arts events.

Pengenalan dan Pembelajaran Literasi Berbasis Media Digital Mengenai Beretika Media Sosial di SDN Pasir Luhur

In all-digital era like this, digital literacy is something that must be done in order to keep abreast of the fast technological movements but also to avoid all things that are not desirable in the vast digital media. This journal article aims to be able to find out and provide an understanding of digital literacy regarding the use of Microsoft Word and how to be ethical on social media aimed at elementary school children who are auspices of SDN Pasirluhur to be able to avoid the threat of crime that is rife in the digital world, For example, the spread of fake news on social media and many others. The implementation method used in this article is the stage of monitoring and situation analysis, survey and location determination, learning and socialization with material presentation, as well as the evaluation stage. In activities regarding digital literacy there are several main discussions such as the meaning of social media, the functions and uses of the positive and negative impacts of social media, as well as how to be ethical and deal with fake news on social media.

가짜뉴스의 생산과 전파, 그리고 규제

Fake news reveals the most stimulating problem of the times. Academic research is expanding as controversy increases, but it is difficult to obtain a comprehensive understanding of fake news unless a large amount of research results are examined one by one due to the complex intertwining of various sub-topics. Therefore, this paper aims to gain a macroscopic understanding of the production, dissemination, and regulation of fake news through a single research result. First, the current major discussions and issues on the concept and scope of fake news were investigated, and the causes and propagation processes of fake news were reviewed to confirm how changes in the media environment and human psychology affect the spread of fake news. It also investigated the controversial discussion on the regulation of fake news and suggested that the regulation should be minimized in terms of freedom of expression and that it is desirable to be implemented in an alternative way, such as technical regulation. It is hoped that this article can contribute to streamlining the theoretical review of the research field, especially the convergence academic field, which finds new ways to effectively manage and regulate fake news by providing a macroscopic understanding of fake news through the above discussion.

The Impact of Fake News on Social Media Users During the COVID-19 Pandemic, Health, Political and Religious Conflicts: A Deep Look

In this new era of inter-connectedness, the COVID-19 outbreak has been characterized by a rapid surge of misinformation — or “fake news” — about the disease on social media, exacerbating its negative psychological impacts and hindering public health efforts. By providing easy access to and sharing of information in real time, social media is considered the main channel for the distribution of fake news. In this viewpoint, we try to find the main impacts of fake news related to COVID-19 on social media users and propose strategies for limiting its spread. In this regard, we set out four key objectives: studying and evaluating the impact of fake news on social media audiences; analyzing fake news patterns; and recommending ways to stop its circulation. To achieve these objectives, we follow a review-based research methodology encompassing scholarly articles and reports from sources including JSTOR, Taylor & Francis, MDPI, Google Scholar, and the WHO, covering the period from 2016 onwards. We employ the Uses and Gratification model to identify three key impacts of COVID-19 related misinformation on social media users: (1) an “infodemic” of health-related misinformation; (2) an increase in social media pastime; and (3) an increase in social, political, and religious conflicts. Several actions are proposed to limit the spread of fake news, involving entities such as government and health agencies, as well as social media companies.

Containing misinformation: Modeling spatial games of fake news.

The spread of fake news on social media is a pressing issue. Here, we develop a mathematical model on social networks in which news sharing is modeled as a coordination game. We use this model to study the effect of adding designated individuals who sanction fake news sharers (representing, for example, correction of false claims or public shaming of those who share such claims). By simulating our model on synthetic square lattices and small-world networks, we demonstrate that social network structure allows fake news spreaders to form echo chambers and more than doubles fake news' resistance to distributed sanctioning efforts. We confirm our results are robust to a wide range of coordination and sanctioning payoff parameters as well as initial conditions. Using a Twitter network dataset, we show that sanctioners can help contain fake news when placed strategically. Furthermore, we analytically determine the conditions required for peer sanctioning to be effective, including prevalence and enforcement levels. Our findings have implications for developing mitigation strategies to control misinformation and preserve the integrity of public discourse.

The Role of Libraries in Preventing the Spread of Fake News in the Scientific Community

The Role of Libraries in Preventing the Spread of Fake News in the Scientific Community

New (Digital) Media in Creative Society: Ethical Issues of Content Moderation


 
 
 Digitalisation and platformisation are continuously impacting and reshaping the societies we live in. In this context, we are witnessing the rise of phenomena such as fake news, hate speech, and the sharing of any other illegal content through social media. In this paper, I propose some ethical reflections on content moderation in the context of digital (social) media, as this topic seems – to me – to already incorporate other relevant digital issues in it, such as algorithms bias, the spread of fake news, and the potential misuses of artificial intelligence. In the first section, I will provide a few hermeneutic reflections over a speech given by the Italian scholar Umberto Eco, which appears to underline the necessity of a content moderation in an era of digital (social) media. In the second section, I will analyse, through a consequentialist perspective, critical and ethical issues posed by content moderation. In particular, I suggest the idea of a ‘moderate’ (reasonable and limited) content moderation that can only be assured by humans, as they are able to contextualise the content, to take emotions and subjective elements into account, to apply critical thinking and adaptability in complex circumstances.
 
 

GBCA: Graph Convolution Network and BERT combined with Co-Attention for fake news detection

Social media has evolved into a widely influential information source in contemporary society. However, the widespread use of social media also enables the rapid spread of fake news, which can pose a significant threat to national and social stability. Current fake news detection methods primarily rely on graph neural network, which analyze the dissemination patterns of news articles. Nevertheless, these approaches frequently overlook the semantic characteristics of the news content itself. To address this problem, we propose a novel Graph Convolution Network and BERT combined with Co-Attention (GBCA) model. Initially, we conduct training for a graph classification task on the propagation structure of fake news. Subsequently, we employ the BERT model to extract semantic features in fake news. Finally, we utilize co-attention mechanism to integrate the two dimensions of propagation structure and semantic features, which enhances the effectiveness of fake news detection. Our model outperforms baseline methods in terms of accuracy and training time, as demonstrated by experiments on three public benchmark datasets.

Development of a Content Framework of Artificial Intelligence Integrated Education Considering Ethical Factors

The rapid advancement of Artificial Intelligence (AI) technology has brought about significant positive changes across society. However, it has also introduced challenges like privacy breaches, data bias, and spreading fake news. In response, several countries, including South Korea, have provided ethical guidelines and policies for AI. Yet, these measures often fall short of keeping pace with the speed and diversity of AI development. To address these issues, this study developed a comprehensive approach by integrating ethical considerations into AI education, covering the entire AI technology development and application process. It involved analyzing previous research on AI and AI ethics education and creating a draft of an integrated AI education program focused on problem-solving based on computational thinking and ethical practicality. The draft was refined and supplemented through two rounds of expert Delphi surveys, ultimately leading to an “Integrated AI Education Program.” This proposed program emphasizes that the knowledge content of AI technology and ethical considerations should not be treated separately but addressed together. It aims to enhance moral and social capacities alongside the experience of thinking like a computer scientist when solving real-life problems. Through this, the program supports the ultimate goal of AI education: to foster computational thinking while providing an educational experience that considers both technology and ethics. This study is expected to advance the discussion on integrating AI ethics into AI education curricula and contribute to developing socially responsible AI developers and users.

Fake news and justice organizations: jurisprudential analysis and organizational responses

Organizations have informal and formal structures that play a crucial role in their functioning. These entities have specific services and obligations that are largely institutionalized and legitimized. Institutionalization plays a determining role in the stability of organizational behavior patterns. However, the heterogeneity and complexity of the organizational environment make it challenging to restrict the legitimate form to a single demand or a dichotomy of forms of legitimation. In the age of digital innovation, the spread of fake news represents a significant challenge. To combat this false information, it is imperative to strengthen fact-checking controls, promote media and digital literacy, and encourage a culture of shared responsibility in the dissemination of information. Understanding the social impact of fake news and adopting measures to prevent disinformation are essential. In this context, the research aimed to map the public debate around fake news from 2018 to 2023, focusing on case law related to the jurisdiction of the Superior Electoral Court (TSE). The study sought to structure the case law on fake news in the electoral process, understand the electoral justice system's position on the issue, and describe the main organizational responses manifested through the TSE's decisions. The research methodology involved a literature review, as well as a qualitative empirical approach, using methodological triangulation and content analysis.

Fake News and Cinema: film analysis in Europe and the United States

How does cinema reflect the reality of fake news? This paper answers this question. To the existing studies on cinema and journalism we want to add, under a qualitative analysis, the following point of view: those information professionals who defend professional malpractice because it is what the public demands. We understand qualitative methodology as the action of giving the film pieces analysed the category of primary source. We understand primary sources as those in which the data come directly from the population or a sample of the population (Torres, Paz and Salazar, 2014, p. 3). In this case, we are not talking about people, but about films.The viral is present in news consumers who, despite having more capacity to communicate than ever, are victims and, at the same time, part of the problem in the rapid spread of fake news. On the one hand, the audience is a victim because their right to be informed with a quality news product is being violated. Secondly, it is also part of the problem. It is not possible for malicious information to have a strong influence on society if there is no audience to consume it. That is, without a prior critical analysis of the product they are consuming. The films analysed have shown us that the population must assume certain responsibilities in order to avoid the disastrous consequences of fake news.