Asal Jameel Khudhair^#, Amenah Dahim Abbood^#,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-31, 2026, DOI:10.32604/cmc.2025.068553 - 10 November 2025

Abstract Community detection is one of the most fundamental applications in understanding the structure of complicated networks. Furthermore, it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships. Networking structures are highly sensitive in social networks, requiring advanced techniques to accurately identify the structure of these communities. Most conventional algorithms for detecting communities perform inadequately with complicated networks. In addition, they miss out on accurately identifying clusters. Since single-objective optimization cannot always generate accurate and comprehensive results, as multi-objective optimization can. Therefore, we utilized two objective functions… More >

Hiba Sameer Saeed^#, Amenah Dahim Abbood^#,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4375-4405, 2025, DOI:10.32604/cmc.2025.067783 - 23 October 2025

Abstract In recent years, the evolution of the community structure in social networks has gained significant attention. Due to the rapid and continuous evolution of real-world networks over time. This makes the process of identifying communities and tracking their topology changes challenging. To tackle these challenges, it is necessary to find efficient methodologies for analyzing the behavior patterns of dynamic communities. Several previous reviews have introduced algorithms and models for community detection. However, these methods have not been very accurate in identifying communities. Moreover, none of the reviewed papers made an apparent effort to link algorithms… More >

Nareshkumar Raveendhran, Nimala Krishnan^*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3253-3265, 2025, DOI:10.32604/cmc.2025.067490 - 23 September 2025

Abstract Social media outlets deliver customers a medium for communication, exchange, and expression of their thoughts with others. The advent of social networks and the fast escalation of the quantity of data have created opportunities for textual evaluation. Utilising the user corpus, characteristics of social platform users, and other data, academic research may accurately discern the personality traits of users. This research examines the traits of consumer personalities. Usually, personality tests administered by psychological experts via interviews or self-report questionnaires are costly, time-consuming, complex, and labour-intensive. Currently, academics in computational linguistics are increasingly focused on predicting… More >

Jialong Zhang¹, Meijuan Yin^2,*, Yang Pei², Fenlin Liu², Chenyu Wang²

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 2095-2115, 2025, DOI:10.32604/cmc.2025.065679 - 29 August 2025

Abstract Identifying influential users in social networks is of great significance in areas such as public opinion monitoring and commercial promotion. Existing identification methods based on Graph Neural Networks (GNNs) often lead to yield inaccurate features of influential users due to neighborhood aggregation, and require a large substantial amount of labeled data for training, making them difficult and challenging to apply in practice. To address this issue, we propose a semi-supervised contrastive learning method for identifying influential users. First, the proposed method constructs positive and negative samples for contrastive learning based on multiple node centrality metrics… More >

Lin Luo^1,2,*, Junfeng Yuan¹, Yanling Wang¹, Rui Zhu¹, Huilin Xu¹, Siyuan Bi¹, Zhongge Zhang¹

International Journal of Mental Health Promotion, Vol.27, No.6, pp. 863-876, 2025, DOI:10.32604/ijmhp.2025.064385 - 30 June 2025

Abstract Objectives: Social Network Addiction (SNA) is becoming increasingly prevalent among college students; however, there remains a lack of consensus regarding the measurement tools and their optimal cutoff score. This study aims to validate the 21-item Social Network Addiction Scale-Chinese (SNAS-C) in its Chinese version and to determine its optimal cutoff score for identifying potential SNA cases within the college student population. Methods: A cross-sectional survey was conducted, recruiting 3387 college students. Latent profile analysis (LPA) and receiver operating characteristic (ROC) curve analysis were employed to establish the optimal cutoff score for the validated 21-item SNAS-C. Results:… More >

Sangmin Kim¹, Byeongcheon Lee¹, Muazzam Maqsood², Jihoon Moon^3,*, Seungmin Rho^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2079-2108, 2025, DOI:10.32604/cmes.2025.061653 - 30 May 2025

Abstract The increased accessibility of social networking services (SNSs) has facilitated communication and information sharing among users. However, it has also heightened concerns about digital safety, particularly for children and adolescents who are increasingly exposed to online grooming crimes. Early and accurate identification of grooming conversations is crucial in preventing long-term harm to victims. However, research on grooming detection in South Korea remains limited, as existing models trained primarily on English text and fail to reflect the unique linguistic features of SNS conversations, leading to inaccurate classifications. To address these issues, this study proposes a novel… More >

Yahya Tashtoush^1,*, Areen Banysalim¹, Majdi Maabreh², Shorouq Al-Eidi³, Ola Karajeh⁴, Plamen Zahariev⁵

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3113-3134, 2025, DOI:10.32604/cmc.2025.062724 - 16 April 2025

Abstract Social media has emerged as one of the most transformative developments on the internet, revolutionizing the way people communicate and interact. However, alongside its benefits, social media has also given rise to significant challenges, one of the most pressing being cyberbullying. This issue has become a major concern in modern society, particularly due to its profound negative impacts on the mental health and well-being of its victims. In the Arab world, where social media usage is exceptionally high, cyberbullying has become increasingly prevalent, necessitating urgent attention. Early detection of harmful online behavior is critical to… More >

Xiaowen Wan¹, Wenbin Sheng¹, Rong Huang¹, Cheng Zeng¹, Xu Zhou^2,*, Yuan Wu³, Xiaohui Cao¹, Xiaoke Chen¹

International Journal of Mental Health Promotion, Vol.27, No.2, pp. 215-229, 2025, DOI:10.32604/ijmhp.2025.059074 - 03 March 2025

Abstract Background: As the digital age progresses, fear of missing out (FoMO) is becoming increasingly common, and the impact factor of FOMO needs to be further investigated. This study aims to explore the relationship between psychological security (PS) and FoMO by analyzing the mediating role of social networking addiction (SNA) and the moderating role of social self-efficacy (SSE). Methods: We collected a sample of 1181 college students (with a mean age of 19.67 ± 1.38 years) from five universities in a province of mainland China through cluster sampling. Data were gathered using the psychological security questionnaire… More >

Yuanjing Hao, Xuemin Wang, Liang Chang^*, Long Li, Mingmeng Zhang

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3131-3159, 2025, DOI:10.32604/cmc.2024.059201 - 17 February 2025

Abstract Dynamic publishing of social network graphs offers insights into user behavior but brings privacy risks, notably re-identification attacks on evolving data snapshots. Existing methods based on -anonymity can mitigate these attacks but are cumbersome, neglect dynamic protection of community structure, and lack precise utility measures. To address these challenges, we present a dynamic social network graph anonymity scheme with community structure protection (DSNGA-CSP), which achieves the dynamic anonymization process by incorporating community detection. First, DSNGA-CSP categorizes communities of the original graph into three types at each timestamp, and only partitions community subgraphs for a specific… More >

Saad M. Darwish^1,*, Abdul Rahman M. Sabri², Dhafar Hamed Abd², Adel A. Elzoghabi¹

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1595-1624, 2024, DOI:10.32604/csse.2024.054615 - 22 November 2024

Abstract The number of blogs and other forms of opinionated online content has increased dramatically in recent years. Many fields, including academia and national security, place an emphasis on automated political article orientation detection. Political articles (especially in the Arab world) are different from other articles due to their subjectivity, in which the author’s beliefs and political affiliation might have a significant influence on a political article. With categories representing the main political ideologies, this problem may be thought of as a subset of the text categorization (classification). In general, the performance of machine learning models… More >