Search Results (196)

The number of studies suggesting that social networking services (SNSs) use poses a risk to children’s body image continue to expand, but most studies have focused on adolescents. The study aimed to examine the associations between SNS use and body image among elementary school children in Japan. This study examined the relationship between SNSs use and body size perception and preference, body size misperception, and ideal body image among 1261 preadolescents (611 boys and 650 girls), aged 8–12 years (mean age = 9.64; SD =1.15; 52% girls), separately by sex. Using hierarchical multivariate linear regression analyses and logistic regression analyses, we examined body image factors and SNS use as the dependent and independent variables, respectively. Findings indicate that children who use SNSs do not significantly differ from nonusers regarding body dissatisfaction. However, SNS use is positively associated with body size misperception in girls. Additionally, for boys and girls, SNS use seems to increase the likelihood of admiring the body image of media figures rather than friends or classmates. Understanding how SNS use influences body image remains important for promoting healthy development in children. Full article

For the last decade, social networking services (SNS), such as X, Facebook, and Instagram, have become mainstream media for advertising and marketing. In SNS marketing, word-of-mouth among users can spread posted advertising information, which is known as viral marketing. In this study, we first analyzed the time series of user reactions to Instagram posts to clarify the characteristics of user behavior. Second, we modeled these variations using statistical distributions to predict the information diffusion of future posts and to provide some insights into the factors that affect users’ reactions on Instagram using the estimated parameters of the modeling. Our results demonstrate that user reactions have a peak value immediately after posting and decrease drastically and exponentially as time elapses. In addition, modeling with the Weibull distribution is the most suitable for user reactions, and the estimated parameters help identify key factors that influence user reactions. Full article

Considering the rapid increase in mobile payment usage, numerous big tech companies have added mobile payment to the primary services that their platforms offer. However, extant research predominantly treats this added service as a standalone offering and investigates user adoption and behavior for this service independent of the primary services. Recognizing this gap in the literature, this study considers the added service as part of an extended ecosystem and examines different motivations for using the primary service. Therefore, this study examines how different motivations for using social networking services (SNSs) shape trust in the extended payment service and ultimately influence behavioral intentions. Drawing on the schema congruity theory, we conceptualize trust as a multidimensional construct—distinguished between cognitive and emotional trust—and explore the impact of trust in the primary service on the use of an added service. Specifically, we analyze survey data of 478 users of South Korea’s leading SNS. The results reveal that both hedonic and utilitarian motivations positively influence emotional and cognitive trust, which, in turn, drive behavioral intention. However, hedonic (utilitarian) motivation exerts a stronger effect on emotional (cognitive) trust. Overall, the findings enhance the knowledge regarding trust formation in extended service ecosystems and offer insights for tech firms integrating financial services into their platforms. Full article

Background/Objectives: The severity of autism spectrum disorder (ASD) is clinically assessed through a comprehensive evaluation of social communication deficits, restricted interests, repetitive behaviors, and the level of support required (ranging from level 1 to level 3) according to DSM-5 criteria. Along with its varied clinical manifestations, the neuroanatomy of ASD is characterized by heterogeneous abnormalities. Notably, brain MRI of children with ASD often reveals an increased number of perivascular spaces (PVSs) compared to typically developing children. Our recent findings indicate that enlarged PVSs (ePVSs) are more common in younger male patients with severe ASD and that specific ePVS locations are significantly associated with ASD symptoms. Methods: In this study, we mapped ePVSs across key regions of three major cognitive networks—the Default Mode Network (DMN), the combined Central Executive/Frontoparietal Network (CEN/FPN), and the Salience Network (SN)—in 36 individuals with different symptom severities and rehabilitation needs due to ASD. We explored how the number, size, and location of PVSs in these networks are related to specific ASD symptoms and the overall need for rehabilitation and support. Results: Our results suggest that ePVSs in the DMN, CEN/FPN, and SN are strongly correlated with the severity of certain ASD symptoms, including verbal deficits, stereotypies, and sensory disturbances. We found a mild association between ePVSs and the level of support needed for daily living and quality of life. Conclusions: Dysfunction in cognitive networks associated with the presence of ePVSs has a significant impact on the severity of ASD symptoms. However, the need for assistance may also be influenced by other comorbid conditions and dysfunctions in smaller, overlapping brain networks. Full article

This study empirically tests explanatory factors for cyberstalking on social networking services (SNS), especially focusing on gender differences in the effects of risk, control, and opportunity factors. In this study, we used lack of attachment and denial of victim as risk factors, morality and self-control as control factors, and anonymity as an opportunity factor. We predicted that the main risk effect for cyberstalking and the interaction effect between risk and control factors and between risk and opportunity factors can be differentiated by gender. It is hypothesized that the effects of lack of attachment and denial of victim as risk factors for cyberstalking would differ by gender. Furthermore, in the context of risk factors, we predicted that the moderating effect of the control factor would be greater for women, and the effect of the opportunity factor, such as anonymity, would be greater for men. The results of the analysis of cross-sectional data from 270 SNS using college students in Seoul, South Korea, generally supported the hypotheses. As a risk factor, the influence of lack of attachment was greater for men, while denial of victim was greater for women. The moderating effects of the control factors were greater for women in such a way that the interaction between denial of victim and morality was significant for women; while the moderating effect of the opportunity factor was greater for men in such a way that the interaction between lack of attachment and anonymity was more significant for men. This study finds that the risk factors of cyberstalking and the respective moderating effects of control and opportunity factors can be differentiated according to gender. Full article

The complexity of large-scale group decision-making (LSGDM) in the digital society is becoming increasingly prominent. How to achieve efficient consensus through social networks (SNs) has become a core challenge in improving the decision quality. First, conventional clustering methods often rely on a single-distance metric, neglecting both numerical assessments and preference rankings. Second, ensuring the decision authenticity requires considering diverse behaviors, such as trust propagations, risk preferences, and minority opinion expressions, for scientific decision-making in SNs. To address these challenges, a consensus-reaching process (CRP) method based on an enhanced K-means++ clustering is proposed. The above method not only focuses on minority opinion handling (MOH), but also incorporates decision indicator sets (DISs) to analyze the degree of opinion divergences within groups. First, the Hamacher aggregation operator with a decay factor completes trust matrices, improving the trust representation. Second, a personalized distance metric that combines cardinal distances with ordinal distances is incorporated into the enhanced K-means++ clustering, enabling more precise clustering. Third, weights for decision-makers (DMs) and subgroups are determined based on trust levels and degree centrality indices. Fourth, minority opinions are appropriately handled via considering the diverse backgrounds and expertise of DMs, leveraging a difference-oriented DIS to detect and adjust these opinions via weight modifications until a consensus is reached. Fifth, the alternative ranking is objectively generated via DIS scores derived from multigranulation rough approximations. Finally, the feasibility of the proposed method is validated via a case study on unmanned aerial vehicle (UAV) selection using online reviews, supported by a sensitivity analysis and comparative experiments demonstrating superior performances over existing methods. The result shows that the proposed model can enhance clustering accuracies with hybrid distances, objectively measure the consensus via DISs, handle minority opinions effectively, and improve LSGDM’s overall efficiencies. Full article

This work introduces a novel and practical metaheuristic algorithm, the Gaslike Social Motility (GSM) algorithm, designed for optimization and image thresholding segmentation. Inspired by a deterministic model that replicates social behaviors using gaslike particles, GSM is characterized by its simplicity, minimal parameter requirements, and emergent social dynamics. These dynamics include: (1) attraction between similar particles, (2) formation of stable particle clusters, (3) division of groups upon reaching a critical size, (4) inter-group interactions that influence particle distribution during the search process, and (5) internal state changes in particles driven by local interactions. The model’s versatility, including cross-group monitoring and adaptability to environmental interactions, makes it a powerful tool for exploring diverse scenarios. GSM is rigorously evaluated against established and recent metaheuristic algorithms, including Particle Swarm Optimization (PSO), Differential Evolution (DE), Bat Algorithm (BA), Artificial Bee Colony (ABC), Artificial Hummingbird Algorithm (AHA), AHA with Aquila Optimization (AHA-AO), Colliding Bodies Optimization (CBO), Enhanced CBO (ECBO), and Social Network Search (SNS). Performance is assessed using 22 benchmark functions, demonstrating GSM’s competitiveness. Additionally, GSM’s efficiency in image thresholding segmentation is highlighted, as it achieves high-quality results with fewer iterations and particles compared to other methods. Full article

The impact of social media use on student academic achievement is complex and varies across studies, likely due to diverse usage motives and mediating factors. This study investigates the mediating role of daily time usage on the relationship between social media use motives and GPA among 301 Vietnamese university students, guided by the uses and gratifications theory. Following a rigorous validation process, we confirmed the applicability of the Social Networking Usage questionnaire within the Vietnamese context. Our findings revealed a complex relationship between social networking motives and GPA. While entertainment motives demonstrated a directly positive influence on GPA, information-seeking motives showed no significant relationship, either directly or indirectly. Critically, our results suggest that daily time spent on social networking acts as a key mediator in the interplay between academic and socialization motives and GPA. Specifically, we observed opposing effects: while academic motives indirectly benefitted GPA, socialization motives negatively impacted GPA. However, these opposing effects were channeled through daily time usage, suggesting that increasing time spent on social networking, regardless of the initial motive, ultimately detracts from academic performance. Our findings suggest that students need to be mindful of how their SNS usage motives influence their time allocation and, consequently, their academic performance. Universities may employ time management training and encourage academic uses of SNSs while advising students to limit non-academic SNS use, particularly during study periods, to minimize distractions and maximize learning time. Full article

Background/Objectives: Interest in diversity is growing worldwide. Today, an understanding and social acceptance of diverse people is becoming increasingly important. Therefore, in this study, we aimed to clarify the relationship between an individual’s gray matter volume (GMV), which is thought to reflect brain health, and their understanding of diversity (gender, sexuality (LGBTQ), and origin). Methods: GMV was determined as the value of the Gray Matter Brain Healthcare Quotient (GM-BHQ) based on MRI image analysis. Meanwhile, participants’ understanding and acceptance of diversity was calculated based on their answers to the psychological questions included in the World Values Survey Wave 7 (WVS7). Results: Our analysis indicated that, in the group of participants with the highest understanding of diversity (PHUD. n = 11), not only the GMV at the whole brain level (t = 2.587, p = 0.027, Cohen’s d = 0.780) but also the GMV of the central executive network (CEN: t = 2.700, p= 0.022, Cohen’s d = 0.814) and saliency network (SN: t = 3.100, p = 0.011, Cohen’s d = 0.935) were shown to be significantly higher than the theoretical value estimated from sex, age, and BMI at the 5% level. In addition, the GMV of the default mode network (DMN: t = 2.063, p = 0.066, Cohen’s d = 0.622) was also higher than the theoretical value at the 10% level. Meanwhile, in the group of others (n = 10), there was no significant difference from the theoretical value. These differences between PHUD and others were also observed when comparing the two with and without controlling for educational and occupational covariates at the 5% or 10% levels. Conclusions: These results suggest that understanding diversity requires a healthy brain, centered on three networks that govern rational judgment, emotion regulation, other-awareness, self-awareness, and the valuing of actions. This is the first study to show that brain structure is related to an understanding and acceptance of the diversity of people. Full article

The widespread adoption of social networking sites (SNSs) has brought social-engineering victimisation (SEV) to the forefront as a significant concern in recent years. Common examples of social-engineering attacks include phishing websites, fake user accounts, fraudulent messages, impersonation of close friends, and malicious links shared through comments or posts on SNS platforms. The increasing number of SNS users is closely linked to a rise in SEV incidents. Consequently, it is essential to explore relevant theories, frameworks, and contributing factors to better understand this phenomenon. This study systematises and analyses 47 scholarly works on SEV in SNSs, examining theories, frameworks, and influencing factors. A total of 90 independent variables were identified and grouped into seven perspectives: socio-demographics, personality traits, socio-emotional factors, habitual factors, perceptual/cognitive factors, message characteristics, and sender characteristics; these were considered alongside mediating variables. The correlations between these variables and victimisation outcomes were evaluated, uncovering factors that increase vulnerability and highlighting contradictory findings in existing studies. This systematised analysis emphasises the limitations in current research and identifies future research directions in order to deepen the understanding of the factors influencing SEV. By addressing these gaps, this study aims to advance mitigation strategies and provide actionable insights to reduce SEV in SNS contexts. Full article

This paper investigates the effectiveness of centrality measures for the influence maximization problem in competitive social networks (SNs). We consider a framework, which we call “I-Game” (Influence Game), to conceptualize the adoption of competing products as a strategic game. Firms, as players, aim to maximize the adoption of their products, considering the possible rational choice of their competitors under a competitive diffusion model. They independently and simultaneously select their seeds (initial adopters) using an algorithm from a finite strategy space of algorithms. Since strategies may agree to select similar seeds, it is necessary to include an initial seed tie-breaking rule into the game model of the I-Game. We perform an empirical study in a two-player game under the competitive independent cascade model with three different seed-tie-breaking rules using four real-world SNs. The objective is to compare the performance of centrality-based strategies with some state-of-the-art algorithms used in the non-competitive influence maximization problem. The experimental results show that Nash equilibria vary according to the SN, seed-tie-breaking rules, and budgets. Moreover, they reveal that classical centrality measures outperform the most effective propagation-based algorithms in a competitive diffusion setting in three graphs. We attempt to explain these results by introducing a novel metric, the Early Influence Diffusion (EID) index, which measures the early influence diffusion of a strategy in a non-competitive setting. The EID index may be considered a valuable metric for predicting the effectiveness of a strategy in a competitive influence diffusion setting. Full article

The rapid rise of social media and widespread Internet access have contributed significantly to the global popularity of e-sports. However, while popular e-sports attract considerable attention, niche e-sports remain underexplored, limiting user discovery and engagement. This paper proposes a Twitter-based recommendation system that uses advanced data management and processing techniques to address the challenge of identifying and recommending both popular and niche e-sports. The system analyzes social media metadata, including user IDs, followers, followees, engagements, and impressions, to calculate two critical metrics: popularity and satisfaction. Based on the combination of these metrics, the system calculates overall scores for each e-sports and generates two distinct rankings: one for popular and another for niche e-sports. The proposed system reflects the application of data-driven methodologies and social network analysis in creating recommendations that meet diverse user preferences, highlighting the relevance of data processing technologies in personalized content delivery. Experimental evaluations, using a dataset derived from Twitter hashtags (#) representing 30 target e-sports in 2022, demonstrate the system’s effectiveness in capturing the emerging dynamics in e-sports and providing actionable insights for diverse user preferences. This study highlights the potential of SNS-based technologies to advance data processing, analysis, and application within the e-sports ecosystem. Full article

This study examined how eating out behavior and variety-seeking tendency in food choice (VARSEEK) differ depending on social network service (SNS) use and recommended information utilization (SURU), focusing on Korean generation Z youth. To this end, participants were categorized as high, middle, or low based on their SURU score; eating out behavior, as well as VARSEEK, were then compared across the three groups. The results indicated that higher SURU scores were associated with a higher frequency of cooking, a higher frequency of eating out, a higher average cost of eating out per person, and a greater tendency to perceive oneself as gourmet. In relation to VARSEEK, the high and middle SURU score groups demonstrated significantly higher mean scores than the low group. This finding suggests that the greater the SURU level, the greater the food neophilic inclination, expressing an affinity for unique, unfamiliar, or exotic cuisine and a willingness to experiment with novel recipes. Consequently, SURU leads to more frequent eating out, resulting in consumers expanding into a food neophilic tendency to try more diverse and new foods. Based on these results, SURU can be a useful indicator for segmenting food- and restaurant-related markets; consumers with a high level of SURU are a group to pay attention to in marketing as they can be tested when introducing new foods into the market. Full article

The rapid growth of the digital economy has significantly enhanced the convenience of information transmission while reducing its costs. As a result, the participation in social networks (SNs) has surged, intensifying the mutual influence among network participants. To support objective decision-making and gather public opinions within SNs, the research on the consensus-reaching process (CRP) has become increasingly important. However, CRP faces three key challenges: first, as the number of decision-makers (DMs) increases, the efficiency of reaching consensus declines; second, minority opinions and non-cooperative behaviors affect decision outcomes; and third, the relationships among DMs complicate opinion adjustments. To address these challenges, this paper introduces an enhanced CRP mechanism. Initially, the hippopotamus optimization algorithm (HOA) is applied to update the initial community division in Leiden clustering, which accelerates the clustering process, collectively referred to as HOAL. Subsequently, a two-stage opinion adjustment method is proposed, combining minority opinion handling (MOH), non-cooperative behavior management, and dual-fine tuning (DFT) management, collectively referred to as DFT-MOH. Moreover, trust relationships between DMs are directly integrated into both the clustering and opinion management processes, resulting in the HOAL-DFT-MOH framework. The proposed method proceeds by three main steps: (1) First, the HOAL clusters DMs. (2) Then, in the initial CRP stage, DFT manages subgroup opinions with a weighted average to synthesize subgroup perspectives; and in the second stage, MOH addresses minority opinions, a non-cooperative mechanism manages uncooperative behaviors, and DFT is used when negative behaviors are absent. (3) Third, the prospect-regret theory is applied to rank decision alternatives. Finally, the approach is applied to case analyses across three different scenarios, while comparative experiments with other clustering and CRP methods highlight its superior performance. Full article

The digital transformation and proliferation of social network sites (SNSs) have created new opportunities to consider digital sources to support the development of software systems. Social network sites (SNSs), such as Twitter and Facebook, can be major sources used during the process of requirement elicitation to identify and extract users’ requirements. The primary objective of SNS-based requirement elicitation is to overcome the limitations of the traditional requirement elicitation methods. However, these valued resources for requirement elicitation are yet to be fully exploited. Software products might not fulfill users’ needs owing to the numerous challenges in processing the data effectively. This study aims to explore the actual use, benefits, and challenges of SNS-based requirement elicitation. Twenty-five practitioners in the software companies in Saudi Arabia were interviewed, and thematic analysis was conducted on the interview data. With the application of the TOE model, five critical benefits and nine challenges were identified and classified into technological, organizational, and environmental contexts. The findings of this study offer valuable implications for researchers and practitioners by providing fine-grained details about the adoption of SNS-based requirement elicitation that could eventually facilitate its implementation effectively. Full article