Spektrum Iran

Abstract Nearly five decades after Hubert Dreyfus underscored the importance of accounting for the social character of intelligence in the development of artificial intelligence, practical implementations have progressed more rapidly than corresponding theoretical inquiry. This remains the case notwithstanding artificial intelligence’s consolidation as an actant within the news media. Because the capacity for meaning-making within a social institution presupposes the socialization of a cognitive system, the socialization of artificial intelligence may be examined along a trajectory comparable to that of human forms of natural intelligence. On this basis, the present article investigates the processes through which AI becomes socialized so as to assume a meaning-making role within a social institution such as the news media, addressing the central question: What constitutes socialized artificial intelligence? To this end, the study integrates the Dual-spacization of Intelligence with representation theory within a socio-organizational framework and adopts a retroductive theoretical approach to address the research question. Within this analysis, social order is understood as a function of AI’s socialization process. The dual-spacization of the world consequently gives rise to a dual-spatial social order. The study’s findings suggest that AI may either be engineered to replicate existing forms of knowledge and entrenched social stereotypes in a manner analogous to human cognition, or be subject to social regulation that fosters an algorithmic rationality oriented toward the common good and toward a sustainable and just social order. Such an order depends on opening representational practices through reflexive engagement with social stereotypes, enabling transformations in representation and supporting increased diversity of identities. The contribution of this article lies in proposing an integrated model for understanding the mechanisms of AI socialization across meaning-producing social institutions. Furthermore, the model offers a comprehensive perspective on the socialization of both natural and artificial cognitive systems within the evolving structures of dual-spatial institutional social orders.

Abstract This study investigates how Persian-speaking users on the social media platform X engage with generative artificial intelligence as a sociotechnical and discursive phenomenon. Drawing on a dataset of 24,215 Persian-language posts, we employ a multi-label topic modeling framework and affective profiling to analyze public discourse surrounding AI tools, their perceived implications, and normative judgments about their use. Rather than treating sentiment as a static indicator of opinion, we interpret affective expression as a communicative act shaped by platform incentives and cultural context. Our findings show that AI is positioned not only as a technical artifact but as a boundary object entangled with debates over expertise, ethics, and institutional legitimacy. The discourse is anchored in practical concerns—especially labor, education, and tool comparisons—but frequently extends into culturally specific narratives about risk, fairness, and epistemic authority. Emotionally, the conversation is marked by pragmatic positivity, critical intensity, and a sizable neutral band reflecting orientation rather than evaluation. This study contributes to ongoing debates in communication, AI ethics, and platform studies by offering a non-Anglophone, culturally grounded analysis of how publics perform vernacular governance over emerging technologies. Emotionally, the conversation is marked by pragmatic positivity, critical intensity, and a sizable neutral band reflecting orientation rather than evaluation. This study contributes to ongoing debates in communication, AI ethics, and platform studies by offering a non-Anglophone, culturally grounded analysis of how publics perform vernacular governance over emerging technologies. Drawing on a dataset of 24,215 Persian-language posts, we employ a multi-label topic modeling framework and affective profiling to analyze public discourse surrounding AI tools, their perceived implications, and normative judgments about their use.

Abstract This study examines the multifaceted impact of artificial intelligence (AI) on interpersonal relationships within Iranian society, highlighting the cultural, social, and psychological challenges emerging from the rapid adoption of AI technologies. As tools such as virtual assistants, social media algorithms, and AI-driven communication platforms become embedded in daily life, they are reshaping patterns of interaction, emotional engagement, and cultural norms. Drawing on twelve semi-structured interviews analyzed through a qualitative-dominant mixed-methods approach, including thematic analysis, intercoder reliability checks, and cross-case comparison, the research identifies a dual narrative: AI enhances communication, productivity, and daily convenience, yet simultaneously undermines face-to-face engagement, emotional bonds, and traditional social practices central to Iranian culture. Findings reveal growing concerns about weakened family and community ties, reduced social skills, dependency on intelligent systems, and generational gaps in digital adaptation. Participants also noted broader cultural shifts, including the rise of virtual lifestyles, threats to cultural identity, and increased social inequality driven by uneven access to AI tools. The study further identifies psychological risks such as loneliness, superficial online connections, diminished empathy, and the perceived decline of emotional intelligence as individuals increasingly interact with algorithmic systems. At the societal level, privacy, data governance, and ethical challenges create additional pressures that shape public trust and relational dynamics. The study contributes to national and international debates on human–AI interaction by demonstrating how global technologies interact with local cultural contexts. It argues that balancing technological innovation with the preservation of Iranian social values is essential to ensuring that AI strengthens rather than erodes the foundations of meaningful human relationships.

Abstract In the contemporary landscape of crisis management, decision-makers are increasingly overwhelmed by the sheer volume, velocity, and variety of media data generated during emergencies. Traditional manual analytical methods are often insufficient to process this influx effectively, necessitating a paradigm shift toward advanced computational approaches. The primary goal of this study is to bridge the gap between technical data science and practical crisis communication by establishing a clear analytical link between specific machine learning (ML) paradigms and their operational capabilities. This article utilizes a narrative review methodology, underpinned by a theoretical framework grounded in machine learning. The study systematically synthesizes existing literature to categorize and analyze how distinct ML architectures—specifically supervised, unsupervised, and deep learning—are applied within the domain of media data analysis to support decision-making processes during crises. The analysis confirms that artificial intelligence significantly enhances crisis management effectiveness by automating media monitoring and generating actionable real-time insights. The findings delineate specific roles for different algorithms: supervised learning serves as the theoretical foundation for rapid misinformation detection and precise crisis classification. Conversely, unsupervised learning and deep learning are identified as critical tools for detecting data anomalies and recognizing emerging patterns, which are essential for the functionality of proactive early warning systems. While AI offers transformative potential, this study provides a critical reflection on significant implementation challenges. It highlights the “black box” problem—characterized by a lack of algorithmic interpretability—and inherent data biases as major ethical hurdles that can compromise accountability and fairness in crisis response. The present study contributes a structured framework for understanding AI’s role through a theoretical lens. It concludes that future implementation must prioritize explainable AI to balance computational efficiency with ethical responsibility.

Abstract This study examines the relationship between data bias, algorithmic ethics, and the social consequences of digital hermeneutics. As artificial intelligence increasingly influences interpretive domains—particularly religious and philosophical texts—the question of data neutrality and algorithmic objectivity has become a fundamental concern. Using an analytical-explanatory approach, the study demonstrate that training data, contrary to common assumptions, are not neutral. Instead, they embody cultural values and presuppositions that are reproduced within algorithmic processes. This reproduction can result in semantic simplification, the reduction of interpretive diversity, and even the distortion of sacred texts. Drawing on a hermeneutical perspective, the article emphasizes the need to distinguish between “human pre-understanding” and “machine data,” showing that the absence of awareness, critical reflexivity, and lived experience in algorithms prevents the attainment of authentic understanding. Moreover, the study indicates that the social implications of this condition extend beyond textual interpretation, posing risks to privacy, intensifying social inequalities, and undermining cultural diversity. Ultimately, the article argues that digital hermeneutics can be constructive only when the technical capacities of artificial intelligence are accompanied by ethical principles, religious oversight, and the preservation of interpretive traditions.

Abstract Artificial intelligence (AI) is increasingly used as a key tool to redefine digital branding and customer engagement. It encompasses techniques and methods that businesses leverage to create brand value, enhance the effectiveness of customer interactions, and improve marketing strategies. The analysis of interviews indicates that AI applications—through data analysis, advanced algorithms, modeling, and other techniques—bring significant transformations in digital branding processes, while also presenting specific challenges and opportunities. Accordingly, this study focuses on identifying AI techniques, persuasive effects, transformations, and challenges associated with AI implementation in digital marketing and customer engagement. To address the research questions, this study employed a qualitative, field-based approach. Seventeen experts in AI and digital branding were purposefully selected and interviewed using semi-structured format. Participant selection focused on expertise, professional experience, and practical familiarity with AI applications in digital branding. The interviews aimed to explore experts’ experiences, perceptions, and insights regarding AI’s role and functions in branding processes. The interview data were analyzed using thematic analysis. Initially, codes were extracted from the interview transcripts. These codes were then categorized and aggregated to identify sub-themes and, ultimately, the main research themes. The findings indicate that AI applications in digital branding are primarily built on advanced computational and learning-based techniques, including scalable algorithms, machine and reinforcement learning, search and recommender systems, automation, data processing, human–computer interaction, and AI-enabled platforms. These capabilities drive major transformations to digital branding, such as more dynamic and personalized marketing activities, changes in distribution and pricing mechanisms, adaptive business strategies, enhanced decision-making and cybersecurity, improved customer experience, stronger brand positioning, the emergence of digital business models, brand globalization, and value creation. This research contributes to the limited qualitative literature examining AI functions and outcomes in digital branding by drawing on experts' lived experiences, and providing rich and practical insights for researchers and practitioners in the field.