درخت حوزه‌های تخصصی

Background: Misinformation has undergone significant transformations over the past few decades, evolving from relatively simple text-based fake news articles to highly sophisticate AI-driven content such as deepfakes and other forms of manipulated media.Aims: This paper traces the historical development of misinformation, its increasing reliance on Artificial Intelligence (AI), and the potential future trajectories of disinformation as AI technologies advance.Methodology: We begin by examining the shift from traditional text-based disinformation campaigns, often propagated via social media platforms, to more immersive and persuasive forms of AI-generated media.Discussions: We discuss how AI techniques, such as Generative Adversarial Networks and Natural Language Processing, have revolutionized the landscape of false information, allowing for the automation of misinformation production and its widespread dissemination at an unprecedented scale. Furthermore, this paper investigates the role of social media algorithms in amplifying disinformation, demonstrating how these platforms, originally designed to prioritize user engagement, inadvertently aid in the spread of false information by promoting sensationalized or emotionally charged content. Through an in-depth analysis of case studies, including the COVID-19 pandemic and the 2020 U.S. elections, this paper highlights the dangers posed by AI-generated misinformation, particularly deepfakes, which are becoming increasingly difficult to detect, even by advanced AI systems. The implications of this shift for democratic processes, public trust, and societal cohesion are profound. This paper also explores the ethical dilemmas posed by AI-driven misinformation and presents potential solutions through the lens of AI-enhanced detection technologies and policy interventions. Lastly, this paper emphasizes the urgent need for interdisciplinary cooperation between policymakers, technologists, and media organizations to mitigate the harmful impacts of AI-driven misinformation while preserving the integrity of information in the digital age.Conclusions: By exploring both technological and regulatory approaches, a comprehensive framework for understanding the evolving threat of AI-driven disinformation is essential and pathways for future research in this critical area is suggested.

Background: The 2024 Iranian presidential election intensified political polarization on Persian Twitter (X), where ideological factions engaged in networked contestation. This study employs social network analysis to examine polarization mechanics, mapping key actors, echo chambers, and discursive strategies. Findings illuminate digital factionalism, algorithmic amplification, and the role of influencers in political mobilization.Aims: This study explores the mechanisms driving political polarization on Persian Twitter (X) during the 2024 Iranian presidential election.Methodology: Utilizing computational social science methodologies, the research combines social network analysis (SNA) and thematic analysis to examine over 133,000 active users and numerous election-related hashtags. Data collection spanned June 1–16, 2024, leveraging Twitter's API to identify clusters, user interactions, and thematic trends. Key tools included the Louvain algorithm for community detection and centrality measures for network analysis, visualized through Gephi software.Findings: Findings reveal a fragmented political landscape characterized by ideological divides, echo chambers, and limited interaction between opposing factions. Analysis identified six major clusters, each aligned with distinct political affiliations, including reformists, conservatives, and opposition groups. Thematic analysis further highlighted the rhetoric surrounding leading candidates Masoud Pezeshkian and Saeed Jalili, revealing polarized sentiments and distinct narratives among their supporters and detractors. The study also underscores the role of platform algorithms, influencer strategies, and group identities in deepening polarization.Conclusions: This research contributes to the understanding of digital polarization within Iran's socio-political context. It emphasizes the dual role of social media as both a space for public discourse and a driver of ideological segregation. Recommendations include strategies for promoting digital literacy, fostering inclusive discussions, and enhancing algorithmic transparency to mitigate polarization in Iran.

Personality as an individual factor is one way of describing human characteristics, usually referred to as a number of relatively stable behavioral traits or characteristics. This systematic review reviewed all related Iranian studies in international databases, including Google Scholar, PubMed, PsycINFO, Web of Science (ISI), Scopus, and national databases, including the Scientific Information Database (SID) and Magiran which was published between 2002 and 2024. The following keywords and combinations of them were used to search the databases: “personality characteristics” “life expectancy,” “quality of work life,” and “work-life balance”. Personality characteristics significantly influence various aspects of life, including life expectancy, quality of work life, and work-life balance. Research indicates that traits such as conscientiousness, neuroticism, and overall personality profiles can contribute to both individual and organizational outcomes. Personality traits, such as conscientiousness and emotional stability, significantly impact life expectancy, job satisfaction, and work-life balance by promoting healthier lifestyles and well-being. Understanding these influences can inform interventions to improve health outcomes and workplace dynamics.

Conventional gasoline price can affect the government and society as a strategic commodity in the community. Conventional gasoline price fluctuations have economic, political, social, cultural, and environmental effects. Thus, the prediction of its volatility is essential but there is not any study to examine the price fluctuations. This study aims to hybridize and propose different Garch models based on two distributions and various algorithms in machine learning, such as random forest, ridge regression, Support Vector Regression (SVR), and elastic-net for predicting weekly gasoline price volatility. The results depict Garch and GJRgarch models based on t-student distribution can predict volatility. The combination of ridge regression and GJRgarch model can better predict volatility for the seven-step-ahead. The RMSE scale has been used to compare results that the scale value is 0.01475 in the hybrid method. In fact, combining the ridge regression with t-student-GJRgarch model has the slightest error prediction or the most accuracy among different Garch models and machine learning algorithms

Enterprise risk management (ERM) represents a new paradigm that supports organizations in identifying, evaluating, and managing risks. Several factors encourage different organizations, especially banks, to design and apply ERM; among them, the possibility of financial problems and related costs, financial performance decline, growth opportunities, and independence of the board of directors can be pointed out. In addition, applying a suitable risk management strategy is a competitive advantage for supporting companies. Following the previous comprehensive studies, the present study was done by integrating a meta-composition approach and multivariate fuzzy network analysis. In this research, to identify effective factors on ERM based on empirical evidence and selection of studies, description and classification of selected articles, analysis of the content of selected research articles, and finally, the importance and refinement of the identified factors based on the Delphi expert opinion polling technique, and multivariate fuzzy network analysis was discussed. The study aimed to identify and rank the factors affecting the effectiveness of ERM of the firms accepted in selected banks in Iran. While reviewing studies, a semi-structured interview was conducted using an Exploratory-Descriptive Qualitative (EDQ) research design to determine the factors that affect ERM effectiveness. The interviewed experts were comprised of 20 university professors, CEOs, financial analysts of investment and brokerage companies, and senior auditors with accounting and management education. The literature review and the results of the interviews indicate five main themes that classify the factors affecting ERM effectiveness in firms. In this study, key factors were identified, and then the fuzzy Delphi technique was employed to rank and find the weight of the factors. The results showed that corporate governance, financial indicators, environmental indicators, company characteristics, and management indicators effectively enhance ERM's effectiveness. Accordingly, capital market analysts and investment companies should take a broader perspective and make decisions based on companies' financial risks instead of paying attention to companies' profitability and stock price changes. The results demonstrated that five factors determine ERM effectiveness: 1. corporate governance indicators (monitoring the board of directors and ownership structure), 2. Financial indicators (return on assets (ROA), earnings volatility, merger and acquisition (M&A) activities, financial deficiency, and capital opacity), 3. Environmental indicators (performance excellence, industry competition, audit firm credibility, environmental uncertainty, and industry), 4. Firm characteristics (financial leverage, firm size, and growth opportunities), and 5. Management indicators (management career and business diversity).