چکیده:
The 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.
خلاصه ماشینی:
In recent years, to help online users identify correct information, extensive research has been conducted towards creating an effective and automated framework for detecting online fake news.
news mainly uses supervised machine learning methods that require high-quality datasets and training labels; therefore, the issues and problems of detecting fake news encourage researchers and stakeholders to create more systematic, compatible, automated, and comprehensive prediction models and approaches.
3. Automated verification of fake news Given that creators of fake news often use advanced tools and technologies to mislead public opinion, laws related to information protection should be designed in such a way that, in addition to preventing privacy violations, they also enable the tracking and identification of the sources of fake news as well; for example, creating transparency in media performance and obligating them to publish news sources can help reduce the spread of fake news.
Offline classification can, based on types of online information, address the detection of fake opinions, insults, deceptions, political news, and some other related fields, such as rumor detection, clickbait detection, spammer identification, and bot detection (Shu et al.
In recent years, many researchers have been working on detecting unusual patterns in social information that can help online users discover different, unusual, or unexpected data; however, detecting suspicious distribution patterns for fake news is complex and challenging due to the heterogeneous and dynamic nature of online social behaviors (Yang et al.
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