درخت حوزه‌های تخصصی

Background: The integration of artificial intelligence into psychodynamic therapy presents a transformative opportunity to enhance mental health interventions.Aims: By consolidating evidence from diverse studies, this research underscores the theoretical and practical implications of psychodynamic AI systems and their potential to reshape therapeutic practices.Methodology: This study employs a meta-analytic approach to synthesize findings from existing research, focusing on the application of psychodynamic principles—such as unconscious processes, relational dynamics, and symbolic meaning—in AI-driven systems. Despite the dominance of cognitive-behavioral frameworks in AI therapeutic tools, psychodynamic approaches offer unique potential for fostering deeper emotional engagement and interpersonal understanding.Results: The analysis highlights advancements in AI-based systems designed to emulate psychodynamic processes and explores challenges, including technical limitations and ethical concerns. The findings emphasize the need for future research to refine AI's capacity to address complex psychodynamic phenomena while adhering to ethical standards, ensuring accessibility, and enhancing emotional intelligence.Conclusions: Ultimately, this work advocates for a balanced integration of psychodynamic principles with AI technologies to achieve scalable and effective mental health solutions.

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.

Background: A key claim and fundamental assumption of feminist thought is that many traits commonly perceived as female are not inherent or genetically determined, but are instead products of historical and social conditioning. This perspective argues that even in contemporary societies, persistent anti-female stereotypes limit women’s ability to fully exercise their rights. For instance, the historical exclusion of women from political life, potentially driven by male power dynamics, has fostered the misconception that women lack interest in political engagement.Aims: This research seeks to investigate this hypothesis: does Internet-based social networks help to increase women's political participation?Methodology: Employing a qualitative approach, this study synthesizes data from diverse sources– including case study of Clubhouse, and peer-reviewed academic literature– to explore to what extent are women interested in political action under free and equal conditions? To this end, we selected the Clubhouse platform. We then monitored and analyzed women’s behavior before and after a sensitive national political event, the 2021 (1400 AH) presidential election.Findings: The findings indicated that, contrary to our initial hypothesis, the Clubhouse platform’s open environment did not significantly enhance women’s political participation or stimulate greater interest in political topics. Women largely remained engaged with content related to entertainment, family life, cooking, and music.Conclusions: This viewpoint aligns with the tendency to view gender-related issues as stemming from inherent, natural differences rather than socio-historical constructs.