درخت حوزه‌های تخصصی

Background: Social identity theory suggests that individuals define themselves partly through their membership in social groups, and these identities shape attitudes and behaviors, including consumption choices. Brands increasingly function as social symbols, allowing users to express group belonging, self-concept, and shared values through brand affiliation. Consequently, users’ social identities can significantly influence their intention to purchase a brand by strengthening emotional attachment, perceived congruence, and loyalty toward brands that represent their in-group. Aims: Consumers like to associate themselves with different brands to represent their social identity. This study suggests a social identity perspective of customer-brand relationship and integrates brand identity with social media usage, electronic WOM (eWOM) and brand loyalty in predicting brand purchase intention in an online environment. Methodology: Data were collected through a questionnaire-based survey among a sample of higher education students in a public university in Iran. A sample of 423 respondents was selected through a convenience sampling technique. Data were then analyzed through Structural Equation Modeling (SEM) to provide a prediction-oriented model assessment by LISREL 8.54. Sobel mediation tests were performed to test the mediating effects of social media use, eWOM and brand loyalty on the relationship between social identity and brand purchase intention. Findings: The findings indicated that social identity (affective, cognitive, and evaluative) of users/consumers has a significant impact on: (a) social media use, (b) eWOM about brand, and (c) brand loyalty. Social media use and brand loyalty were found to be the determinants of eWOM about brand. Finally, this research found that all three variables of: (a) social media use, (b) eWOM about brand, and (c) brand loyalty are the main determinants of intention to purchase a certain brand. Conclusion: This research suggests that social identity perspective can be integrated with other perspectives to model the consumer's psychological path to brand purchase intentions. This study confirms the critical role of social identification of users in the reaction towards a given brand and stresses the mediation effects of social media use, brand loyalty and eWOM on the effects of social identity on the path to brand purchase intention.

Background: Self-development microcelebrities constitute a distinct and rapidly expanding subtype within the broader culture of fame. These figures primarily operate within the digital self-help domain, offering audiences simplified psychological and lifestyle concepts that promise personal transformation through adherence to their guidance. Contemporary self-help discourse is rooted in the positive psychology movement that gained traction in the United States during the late 20th century and is heavily influenced by neoliberal ideologies that emphasize individual agency and responsibility. Aims: In this research, Instagram pages of Iranian self-development microcelebrities were studied to understand what they share with their audience and the various methods they employ to engage with them. Methodology: This study analyzes the Instagram accounts of Iranian self-development microcelebrities with follower counts exceeding 300,000, using a thematic analysis approach. Initially, each account was analyzed individually, followed by a cross-case analysis to identify recurring thematic patterns. Findings: The analysis revealed three overarching themes—pseudoscience, the primacy of luck and the inner self, and self-made rules—comprising seven organizing themes and 31 basic themes. The findings indicate that these microcelebrities promote a form of hyper-individualism aligned with neoliberal values. Conclusion: This model of subjectivity emphasizes psychological self-regulation and inner transformation while detaching individuals from social and political engagement.

Background: Social media has enabled both intentional and incidental user exposure to diverse cultures and subcultures. This exposure fosters the socialization of similar cultural elements or produces changes in original cultural patterns through cyber-acculturation, which arises from continuous contact with culturally distinct groups. Aims: This study aimed to develop models of social media users’ cyber-acculturation in their exposure to cyber-subcultures. Methodology: Using a Q methodology research design, the study employed convenience snowball sampling and an online questionnaire to collect data from 52 participants, who freely rated 61 statements derived from interviews. Exploratory Factor Analysis, treating participants as variables, was conducted to identify distinct cyber-acculturation models. To further examine the characteristics of each factor, network analysis was applied. Discussions: Users demonstrated varying patterns of cyber-acculturation. Comparable to the model proposed by Sam and Berry (2006)— which outlines the strategies of assimilation, separation, integration, and marginalization— this study identified five distinct models of social media users’ acculturation in exposure to cyber-subcultures: 1) Conservative Socialization Adopters, 2) Media-Literate Non-Adopters, 3) Media-Literate Acculturation Adopters, 4) Change-Aspirant Semi Non-Adopters, and 5) Risk-Taking Socialization Adopters. Each of these categories, to some extent, reflects the integration strategy, particularly motivated by social mobility, self-actualization, well-being, and personal advancement. Conclusion: Understanding the variations in how users adopt cultural elements enables governance bodies, policymakers, and professionals in media, culture, and education to design and implement more effective measures with greater impact across different user types.

Background: Afghans are the main group of migrants in Iran, and in recent years, their presence has been met with negative reactions from the Iranian public. For example, recently on Twitter, hashtags such as #Expel_Afghans_a_National_Demand (in Persian: # اخراج_افغانی_مطالبه_ملی ) have been widely used by Iranian users. Aims: This study aims to examine Iranian users' perspectives on Afghans by analyzing tweets written with this hashtag about Afghans living in Iran. Methodology: Using thematic analysis, it investigates the negative views expressed by Iranian users. This analysis is based on Bennett’s six-stage model of intercultural sensitivity. The extracted themes from these tweets include five main categories: dissatisfaction with the lifestyle and behaviors of Afghans living in Iran, financial-related dissatisfaction, nationalism-related dissatisfaction, xenophobia-related dissatisfaction, and dissatisfaction due to internal factors. Finding: The dissatisfaction of Iranians toward Afghans aligns with the Defense stage of Bennett’s model, as this stage is characterized by individuals distinguishing between themselves and others, perceiving their own culture as superior, and viewing other cultures as inferior. Conclusion: Based on the themes present in the tweets, it seems that some targeted actions can help reduce xenophobia and counter dominant narratives.

Background: Following the Islamic Revolution and the expansion of access to visual media, the use of this media in Shia rituals, such as religious gatherings ( hey’ats ), significantly increased. In recent years, a new form of Shia ritual, referred to as “public communities”, has emerged which organized by cultural institutions. These communities differ markedly from traditional Shia-Iranian rituals and have established a distinct communicative domain. Aims: This study aims to critically examine these events by identifying their constitutive components and analyzing the nature of their mediatization. Methodology: Three events held in the Iranian calendar year 1401 were analyzed using a qualitative content analysis approach. These Shia-revolutionary performative rituals are categorized into two main domains: the “Public construction of the ritual” and the “construction of the presence and positioning of the subject”. Their media representation is also shaped by two principal categories: the “transitionalization of the communication mode” and “ideological representation”. Findings: Our findings indicate that the organization and media representation of these events produce a spectacular pseudo-ritual that significantly diverges from traditional Shia rituals in Iran. Conclusion: These events have been organized and represented in ways that prioritize spectacle, visibility, and media populism, thus enabling a transformation in ritual structures and indicating a growing tendency toward mediatization in Iran.

Background: Social media platforms such as Facebook, Instagram, TikTok, and X have redefined the norms of sociality, identity performance, and participation in the public sphere. Aims: This study examines how Persian-language users on X negotiate the visibility of children in online spaces through affective discourse and vernacular governance. Methodology: Analyzing 2,392 posts, we identify seven thematic formations—ranging from family blogging and sharenting to screenshot-based mockery, celebrity child cultures, and rights-based critiques. Using a hybrid methodological approach combining high-recall data retrieval, supervised multi-label topic modeling, and sentiment–intensity analysis, we map how practices like quote-tweeting and screenshotting structure public debates around parental branding, childhood agency, privacy, and consent. Central to this ecosystem is the culturally specific figure of “Arat’s father”, a discursive shorthand for the commodification of childhood under platform economies. Findings: The findings reveal a layered affective landscape where humor, outrage, and pedagogical neutrality coexist, enabling users to police age norms and negotiate ethical boundaries in real time. Conclusion: This study reveals how ordinary users in Iran and Persian-speaking contexts regulate childhood visibility through platform affordances, emotional repertoires, and normative claims. It also proposes a reproducible pipeline for analyzing culturally specific digital publics with methodological transparency and ethical sensitivity.

Background: Understanding how humans perceive and estimate distances in built environments is critical not only for advancing perceptual psychology but also for informing the design of computational models in computer vision, robotics, and architectural design. Aims: This study investigates the mechanisms and limitations of human distance estimation within a controlled architectural environment. Methodology: While some previous experiments focused on estimating distances in virtual settings, the current study examines real-world estimation accuracy across a series of predefined points within an unobstructed corridor. Participants were asked to visually estimate the distance between their position and seven distinct target locations, ranging from near to far without the aid of physical reference cues. The core objective was not simply to measure accuracy, but to identify the perceptual threshold beyond which estimation errors significantly increase. A one-way ANOVA model was employed to assess the influence of variables such as actual distance and participant age on perceptual accuracy. Results: Results revealed a consistent estimation performance up to approximately 2 m, beyond which the margin of error grew increasingly pronounced. Notably, a critical threshold was identified at 7.476 m, where estimation errors sharply escalated. The maximum observed discrepancy occurred at a distance of 10.186 m, suggesting a cognitive boundary in spatial awareness. Conclusion: These findings contribute to the understanding of visual-spatial perception mechanisms and offer theoretical insights relevant to applications in robotics, image processing, virtual reality, and navigation system design.

Background: Network security and penetration pose a significant challenge in the extensive IoT research of recent years. System security and user privacy demand security solutions that are carefully planned and diligently maintained. Aims: This paper introduces a novel three-stage hybrid IDS, IoT-APOSCA, leveraging machine learning and meta-heuristics for attack detection; stages include pre-processing, feature selection, and attack detection. The pre-processing steps are: cleaning, visualization, feature engineering, and vectorization. Methodology: Networks use Intrusion Detection Systems (IDSs) to monitor and detect malicious activities as a key security feature. The Arctic Puffin Optimization (APO) and Sine-Cosine Algorithm (SCA) are used in the feature selection stage, while a changed Random Neural Network (RNN) is employed in the attack detection stage. Results: The proposed technique is assessed using the DS2OS dataset, and the outcomes show that the approach, integrating multiple learning models, led to an accuracy enhancement to 99.66%. Also, the values Recall and False Alarm Rate obtained are equal to 0.9926 and 0.003, respectively. Conclusion: Intrusion detection system efficacy is directly tied to the quality of its classification method. Enhanced neural network performance is achievable through adjustments to parameters, such as network weights.

Background: Cryptocurrencies have a variety of unique qualities, from cutting-edge technology to highly secure architecture. Additionally, the ability to invest in cryptocurrency, as an asset or a function of its prosperity has made crypto-currencies attractive to venture capitalists, computer scientists, and statisticians. Aims: In this study, we concentrated on a collection of documents web-scrapped from the market section of CNBC, where each document is associated with a response variable. Methodology: These documents contain preprocessed words/terms of day-to-day reportage on cryptocurrency (Bitcoin). The corresponding response variables are the daily opening and closing price of Bitcoin prices. The Supervised Latent Dirichlet Allocation(sLDA), a statistical model of labeled documents, was used to analyze the textual data alongside their corresponding response variables, since our study aims to predict the response variable for unlabeled new documents. Results: Hidden Topics with their unique terms from the preprocessed articles were exposed through a Natural language processor. Mean absolute error (MAE), Mean absolute percentage error (MAPE), and Root mean square error (RMSE) graphs were constructed for the sLDA models with ‘k = 3,10,20,30,50,75,100 and 200 Topics’ values where the model with the best evaluation metric, was selected for prediction purpose. Conclusion: It was discovered that the sLDA model with k = 20. A posterior covariance matrix which shows the proportion of terms from the documents, making up a Topic. Coefficient values were generated in other to graphically visualize how important the discovered topics are and how they affect the market trend. Finally, the prediction of new labels (numeric-decoded closing prices) for the unlabeled documents was done and comparisons were made; the predicted labels follow a similar pattern to that of the time series closing price trend.