news reporters are a special group in the jurisprudence and hadith of Shia Islam, whose way of thinking has cast a shadow over the people and seminaries for years. The founder of this sect was Mohammad Amin Esterabadi, who, by residing in Medina and writing the book "Fawaed al-Madaniyah, " spread his beliefs in Iraq and Iran. He considered deduction and ijtihad to be "innovation" and rejected them, believing that only the apparent meaning of hadith should be adhered to and that only the infallible Imams (AS) could interpret the Quran and hadith, which is beyond the capacity of ordinary people. He also disregarded reason and consensus. His opinions were similar to Sunni Ahl al-Hadith to some extent, although he differed from them in terms of belief. The thought of Esterabadi news reporting led to more attention being paid to hadith books, but it resulted in harmful effects such as regression, sectarianism, and superficiality, which hindered rational thinking. Of course, the thought of Esterabadi news reporting was limited and restricted by the efforts of scholars, but its effects can still be seen to some extent: the emergence of modern news reporting in today's world from Egypt to Iraq, Syria, Saudi Arabia, Afghanistan, etc., and cultural news reporting among elites and people are examples of this category.

Fake news presents a complex regulatory challenge in the increasingly democratized and intermediated on-line information ecosystem. Inaccurate information is readily created by actors with varying goals, rapidly distributed by platforms motivated more by financial incentives than by journalistic norms or the public interest, and eagerly consumed by users who wish to reinforce existing beliefs. Yet even as awareness of the problem grew after the 2016 U. S. presidential election, the meaning of the term “Fake news” has become increasingly disputed and diffused. This Article first addresses that definitional challenge, offering a useful taxonomy that classifies species of Fake news based on two variables: their creators’ motivation and intent to deceive. In particular, it differentiates four key categories of Fake news: satire, hoax, propaganda, and trolling. This analytical framework can provide greater rigor to debates over the issue. Next, the Article identifies key structural problems that make each type of Fake news difficult to address, albeit for different reasons. These include the ease with which authors can produce user generated content online and the financial stakes that platforms have in highlighting and disseminating that material. Authors often have a mixture of motives in creating content, making it less likely that a single solution will be effective. Consumers of Fake news have limited incentives to invest in challenging or verifying its content, particularly when the material reinforces their existing beliefs and perspectives. Finally, Fake news rarely appears alone: it is frequently mingled with more accurate stories, such that it becomes harder to categorically reject a source as irredeemably flawed. Then, the Article classifies existing and proposed interventions based upon the four regulatory modalities catalogued by Larry Lessig: law, architecture (code), social norms, and markets. It assesses the potential and shortcomings of extant solutions. Finally—and perhaps most importantly—the Article offers a set of model interventions, classified under the four regulatory modalities, that can reduce the harmful effects of Fake news while protecting interests such as free expression, open debate, and cultural creativity. It closes by assessing these proposed interventions based upon data from the 2020 election cycle.

Objective: This paper aims to provide a detailed overview of the most recent methods and strategies used for detecting Fake news, especially in the context of rapid advancements in artificial intelligence and machine learning. With the widespread reach of Fake news across social media and other digital platforms, this review focuses on identifying and evaluating effective approaches that can help tackle this growing problem.Methods: Given the importance of detecting Fake news, this paper reviews and compares various approaches utilized in this field. To this end, by studying articles published in online libraries and document repositories such as IEEE, Scopus, Elsevier, and others, we first explore different methods for detecting Fake news. Then, we compare the various approaches of human-based detection with those of automated detection.Results: The review shows that while conventional techniques like feature extraction and rule-based systems offer a good starting point, they often fall short when dealing with the complexity of modern disinformation. Deep learning models trained on large datasets have demonstrated promising results in detecting Fake news, yet they still struggle with the subtlety of human-generated content and real-time applications. This highlights the need for more comprehensive solutions that can address these challenges.Conclusions: The findings suggest that an integrated approach—one that combines language analysis, machine learning, and network-based methods—is essential for building effective Fake news detection systems. As the field progresses, future research should focus on improving hybrid models, refining data quality, and incorporating user-centric insights to combat the spread of disinformation better. Combining large language models (LLMs) with context-aware systems offers a promising path for achieving higher precision in detecting both machine-generated and human-created Fake news.

In recent years, confronting and encountering with Fake news has become one of the main specialties of the official media, including the Islamic Republic of Iran Radio and Television. In this regard, using Quranic themes can be a way forward, because the Holy Quran is a successful model in encountering with such news and information. The current research has identified the way of this exposure and how to use it in the national media. This research was conducted with a qualitative approach and thematic analysis method, and the data of this research is of library type. The findings of the research show that the Qur'an confronts Fake news in the cognitive field through "explanation", "denial", "controversy" and "hostility", in the emotional field through "warning", "humiliation", "rebuke", "threat". and "encouraging" and in the field of behavior through "readiness and ability", "modelling", "explanation", "changing the subject", "reprimand", "restoration" and "similarity". The results of the research and interpretation of the findings indicate that the national media should use "explanation", "denial", "education/promotion of media literacy", "use of persuasive media attractions", "proper management of news coverage", "demanding and making demands". , "Using various formats of programming", "Creating a special Fake news unit" and "Creating a digital platform" to encountering with Fake news.

Today, social networks have become a prominent source of news, significantly altering the way people obtain news from traditional media sources to social media. Alternatively, social media platforms have been plagued by unauthenticated and Fake news in recent years. However, the rise of Fake news on these platforms has become a challenging issue. Fake news dissemination, especially through visual content, poses a significant threat as people tend to share information in image format. Consequently, detecting and combating Fake news has become crucial in the realm of social media. In this paper, we propose an approach to address the detection of Fake image news. Our method incorporates the error level analysis (ELA) technique and the explicit convolutional neural network of the EfficientNet model. By converting the original image into an ELA image, it is possible to effectively highlight any manipulations or discrepancies within the image. The ELA image is further processed by the EfficientNet model, which captures distinctive features used to detect Fake image news. Visual features extracted from the model are passed through a dense layer and a sigmoid function to predict the image type. To evaluate the efficacy of the proposed method, we conducted experiments using the CASIA 2. 0 dataset, a widely adopted benchmark dataset for Fake image detection. The experimental results demonstrate an accuracy rate of 96. 11% for the CASIA dataset. The results outperform in terms of accuracy and computational efficiency, with a 6% increase in accuracy and a 5. 2% improvement in the F-score compared with other similar methods

This article aims to explore the various legal strategies employed to combat Fake news and misinformation, examining the complexities and challenges inherent in these approaches. It seeks to identify effective measures and provide a comprehensive understanding of how law and policy can evolve to address the pervasive issue of misinformation in the digital age. Employing a qualitative research design, this study relies on semi-structured interviews with a purposive sample of 23 participants, including legal experts, policymakers, and digital platform operators. The interviews were designed to achieve theoretical saturation, ensuring a robust exploration of the subject. Data were analyzed thematically, focusing on identifying key themes, categories, and concepts related to legal strategies against Fake news. Four main themes emerged from the analysis: Legal Frameworks, Challenges in Legal Enforcement, Role of Digital Platforms, and Impacts of Legal Strategies. Each theme encompasses several categories with associated concepts, revealing a complex landscape of strategies ranging from constitutional considerations and digital regulation to the ethical challenges of enforcement and the societal impacts of legal measures against misinformation. The study concludes that combating Fake news and misinformation requires a multifaceted approach that goes beyond traditional legal remedies. Effective strategies must include enhancing public cognitive resilience, promoting critical media literacy, and ensuring digital platforms' accountability and transparency. Collaboration among stakeholders across legal, educational, and technological domains is crucial for developing resilient legal frameworks that can adapt to the evolving nature of misinformation.

Fake news in Iran: A Mixed Method StudyWith the rise and spread of social media, the issue of Fake news has increasingly garnered the attention of policymakers, journalists, and even the general public. Iran's unique situation and complex structure make the issue of Fake news particularly significant. This research employed a sequential mixed method approach. First, a survey was conducted with 137 journalists and social media activists, who responded to questions regarding their perspectives on the proliferation of Fake news in Iran and globally, and the potential roles of governments and decision-making bodies in combating Fake news. After analyzing these responses, interviews were designed in which 48 media experts answered both closed and open-ended questions. Finally, by comparing the results from both phases of the research, certain findings and recommendations were formulated and presented.

The new term "Fake news", with extensive changes in communications infrastructures and the emergence of new media and subsequent changes in the structures and institutions of human societies, including politics and economics has become one of the controversial issues of public and academic fields related to journalism, media, communication, and culture in recent years. This Scientific term is used in public discussions to explain different phenomena which are not necessarily mean Fake news. The lack of clear definition of the term-Fake news-and cases that are typically confused with this scientific term makes it difficult to understand the harm and consequences of it and the possibility of adopting solutions to combat it. This research has identified the similar types with Fake news by a comprehensive review of available scientific sources: Satire, Parody, Fabrication, Manipulation, Propaganda, Advertising, Hoax, Rumor, so to obtain indicators to compare and conceptually organize these media phenomena. 18 experts of news and media were interviewed. These indicators are "author", "editorial", "content" and "gender relationship". At the end of this study, based on the obtained indicators, the term Fake news is explained and a complete table is made to clearly express the differences and similarities of all similar types with Fake news. .