With the increasing influence of mass media and cyberspace in today's life, the need to address the legal aspect of this issue has become more apparent. One of the benefits and characteristics of today's media is the fast and free circulation of information and the mass production of news by the media custodians and even the users themselves. Therefore, identifying the rights of the audience and users of these media and, on the other hand, specifying the duty of the users and the custodians of the media seem to be important and necessary. One of these rights is the right to benefit from correct information. On the other hand, a phenomenon that can disrupt the realization of this right is a global and destructive phenomenon known as "fake news". Dealing with such news and information and prohibiting their production and publication is a reciprocal duty of the right to benefit from correct information. The main question of this article is to identify this right and obligation in the context of various types of mass media in Iran's legal system, using library data and documentary methods with a descriptive-analytical approach and sometimes with a comparative view of the actions taken in Other countries have investigated this issue. And finally, identifying such rights and obligations and examining its various dimensions in the mirror of the laws and regulations of Iran's legal system is the main finding of the article.

2The rise of virtual media has significantly impacted how people communicate in recent years. However, the spread of misleading and suspicious information on social networks poses serious challenges to society. For this reason, detecting false information on these platforms is of great importance. Social networks allow for the rapid production and dissemination of large volumes of diverse content, making it difficult to assess the accuracy and reliability of the information shared. This task requires collaboration between humans and computers, as it cannot be achieved solely through automated systems. At the same time, the right to free access to information is recognized as a fundamental human right in both national and international legal frameworks. Every citizen is entitled to accurate domestic and global news and developments, using the press and other media to express and exchange ideas. This article provides a thorough examination of the methods used to detect online misinformation, commonly referred to as "fake news." Many of these methods focus on analyzing the characteristics of users, the content, and the platforms where the news is published. By exploring these approaches, the research aims to offer recommendations on how to ensure citizens have proper access to accurate information, in line with their right to free access to information.

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.

In each journal, the editorial board receives many articles but more than 70% of them are rejected. This happens because there is no real correlation among the variables in these articles or the variables and perceived relations are fake, which means playing with the variables nonexistent in reality. This rejection occurs mainly as a result of the researchers' misinterpretation of the interdisciplinary studies, their having no expertise in the subject, their paying no attention to co-authorship in different disciplines, and their being fond of the statistical outputs. Furthermore, these researchers have no real acceptable and sensible idea and no valid variable, think in isolate space, have few partners, lack the necessary motivation for research, and have hesitation in delivering their paper, and so on. To conclude, the researchers must concentrate more on the recognition of the differences between aim, necessity and profit, and the importance of prioritization.

Introduction: Evaluation of hospital information system (HIS) is a complex endeavor, in which all human, technical and organizational aspects should be considered. This study aimed to develop indicators for HIS evaluation.Methods: Present qualitative study was carried out through a cross-sectional method in 2012 in Kerman province, using Delphi technique. Given the objectives of this study, three independent phases were performed including literature review, providing draft indicators for HIS evaluation and reaching consensus. Required data were obtained through interviews and designed forms. Twenty-three experts composed the study population in interview and reaching consensus phases. Validity and reliability were confirmed through content validity and test-retest method, respectively. Data were analyzed using descriptive statistics.Results: Final set of indicators for HIS evaluation consisted of ninety-one indicators under 8 main topics, i.e. technical quality, software quality, architecture and interface quality, vendor quality, after-sale services quality, workflow support quality, support department, outcome quality and HIS cost.Conclusion: Given the complexity of information system evaluation, all human, technical and organizational aspects have to be taken into account in any evaluation. Proposed indicators provide the possibility of comprehensive evaluation of HIS.

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.