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 >

Menghan Zhang^1,2, Fangfang Shan^1,2,*, Mengyao Liu^1,2, Zhenyu Wang^1,2

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1565-1580, 2024, DOI:10.32604/cmc.2024.056008 - 15 October 2024

Abstract With the emergence and development of social networks, people can stay in touch with friends, family, and colleagues more quickly and conveniently, regardless of their location. This ubiquitous digital internet environment has also led to large-scale disclosure of personal privacy. Due to the complexity and subtlety of sensitive information, traditional sensitive information identification technologies cannot thoroughly address the characteristics of each piece of data, thus weakening the deep connections between text and images. In this context, this paper adopts the CLIP model as a modality discriminator. By using comparative learning between sensitive image descriptions and… More >

Yali Si^1,2, Feng Li^1,*, Shan Zhong^1,2, Chenghang Huo³, Jing Chen⁴, Jinglian Liu^1,2

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 685-706, 2024, DOI:10.32604/cmc.2024.055141 - 15 October 2024

Abstract Point-of-interest (POI) recommendations in location-based social networks (LBSNs) have developed rapidly by incorporating feature information and deep learning methods. However, most studies have failed to accurately reflect different users’ preferences, in particular, the short-term preferences of inactive users. To better learn user preferences, in this study, we propose a long-short-term-preference-based adaptive successive POI recommendation (LSTP-ASR) method by combining trajectory sequence processing, long short-term preference learning, and spatiotemporal context. First, the check-in trajectory sequences are adaptively divided into recent and historical sequences according to a dynamic time window. Subsequently, an adaptive filling strategy is used to… More >

Xiuyang Meng^1,2, Chunling Wang^1,2,*, Jingran Yang^1,2, Mairui Li^1,2, Yue Zhang^1,2, Luo Wang^1,2

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4259-4281, 2024, DOI:10.32604/cmc.2024.050325 - 20 June 2024

Abstract Suicide has become a critical concern, necessitating the development of effective preventative strategies. Social media platforms offer a valuable resource for identifying signs of suicidal ideation. Despite progress in detecting suicidal ideation on social media, accurately identifying individuals who express suicidal thoughts less openly or infrequently poses a significant challenge. To tackle this, we have developed a dataset focused on Chinese suicide narratives from Weibo’s Tree Hole feature and introduced an ensemble model named Text Convolutional Neural Network based on Social Network relationships (TCNN-SN). This model enhances predictive performance by leveraging social network relationship features More >

Divya P. Vijayan¹, Tokani Ghuhato¹, Eslavath Rajkumar^2,*, Allen Joshua George³, Romate John¹, John Abraham⁴

International Journal of Mental Health Promotion, Vol.26, No.5, pp. 389-398, 2024, DOI:10.32604/ijmhp.2024.023032 - 30 May 2024

Abstract ‘Selfie’ taking was introduced to the common people by smartphones and has become a common practice across the globe in no time. With technological advancement and the popularity of smartphones, selfie-taking has grown rapidly within a short time. In light of the new trend set by the generation, this study aimed to explore reasons for selfie-taking and selfie-posting on social media and their effects on the social and psychological lives of young adults. A purposive sampling method was adopted to select 20 Indian citizens, between 18 and 24 years. The data were collected through semi-structured More >

Hao Jiang¹, Yuerong Liao¹, Dongdong Zhao², Wenjian Luo³, Xingyi Zhang^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1045-1075, 2024, DOI:10.32604/cmes.2024.048653 - 16 April 2024

Abstract Due to the presence of a large amount of personal sensitive information in social networks, privacy preservation issues in social networks have attracted the attention of many scholars. Inspired by the self-nonself discrimination paradigm in the biological immune system, the negative representation of information indicates features such as simplicity and efficiency, which is very suitable for preserving social network privacy. Therefore, we suggest a method to preserve the topology privacy and node attribute privacy of attribute social networks, called AttNetNRI. Specifically, a negative survey-based method is developed to disturb the relationship between nodes in the… More >

Fangfang Shan^1,2,*, Fuyang Li¹, Zhenyu Wang¹, Peiyu Ji¹, Mengyi Wang¹, Huifang Sun¹

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1029-1044, 2024, DOI:10.32604/cmes.2024.047665 - 16 April 2024

Abstract A deep learning access control model based on user preferences is proposed to address the issue of personal privacy leakage in social networks. Firstly, social users and social data entities are extracted from the social network and used to construct homogeneous and heterogeneous graphs. Secondly, a graph neural network model is designed based on user daily social behavior and daily social data to simulate the dissemination and changes of user social preferences and user personal preferences in the social network. Then, high-order neighbor nodes, hidden neighbor nodes, displayed neighbor nodes, and social data nodes are… More >