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  • Open Access

    ARTICLE

    Predicting Users’ Latent Suicidal Risk in Social Media: An Ensemble Model Based on Social Network Relationships

    Xiuyang Meng1,2, Chunling Wang1,2,*, Jingran Yang1,2, Mairui Li1,2, Yue Zhang1,2, Luo Wang1,2

    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 >

  • Open Access

    ARTICLE

    Exploring the Reasons for Selfie-Taking and Selfie-Posting on Social Media with Its Effect on Psychological and Social Lives: A Study among Indian Youths

    Divya P. Vijayan1, Tokani Ghuhato1, Eslavath Rajkumar2,*, 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 >

  • Open Access

    ARTICLE

    A Privacy Preservation Method for Attributed Social Network Based on Negative Representation of Information

    Hao Jiang1, Yuerong Liao1, Dongdong Zhao2, Wenjian Luo3, Xingyi Zhang1,*

    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 >

  • Open Access

    ARTICLE

    Deep Learning Social Network Access Control Model Based on User Preferences

    Fangfang Shan1,2,*, 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 >

  • Open Access

    ARTICLE

    Fake News Detection Based on Text-Modal Dominance and Fusing Multiple Multi-Model Clues

    Lifang Fu1, Huanxin Peng2,*, Changjin Ma2, Yuhan Liu2

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4399-4416, 2024, DOI:10.32604/cmc.2024.047053 - 26 March 2024

    Abstract In recent years, how to efficiently and accurately identify multi-model fake news has become more challenging. First, multi-model data provides more evidence but not all are equally important. Secondly, social structure information has proven to be effective in fake news detection and how to combine it while reducing the noise information is critical. Unfortunately, existing approaches fail to handle these problems. This paper proposes a multi-model fake news detection framework based on Tex-modal Dominance and fusing Multiple Multi-model Cues (TD-MMC), which utilizes three valuable multi-model clues: text-model importance, text-image complementary, and text-image inconsistency. TD-MMC is… More >

  • Open Access

    ARTICLE

    The Social Networking Addiction Scale: Translation and Validation Study among Chinese College Students

    Siyuan Bi1, Junfeng Yuan1,2, Lin Luo1,2,3,*

    International Journal of Mental Health Promotion, Vol.26, No.1, pp. 51-60, 2024, DOI:10.32604/ijmhp.2023.041614 - 05 February 2024

    Abstract Purpose: The core component theory of addiction behavior provides a multidimensional theoretical model for measuring social networking addiction. Based on this theoretical model, the Social Networking Addiction Scale (SNAS) was developed. The aim of this study was to test the psychometric properties of the Chinese version of the SNAS (SNAS-C). Methods: This study used a sample of 3383 Chinese university students to conduct confirmatory factor analysis (CFA) to explore the structural validity of the SNAS-C. This study examined the Pearson correlations between the six subscales of the SNAS-C (i.e., salience, mood modification, tolerance, withdrawal symptoms,… More >

  • Open Access

    ARTICLE

    A Weighted Multi-Layer Analytics Based Model for Emoji Recommendation

    Amira M. Idrees1,*, Abdul Lateef Marzouq Al-Solami2

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1115-1133, 2024, DOI:10.32604/cmc.2023.046457 - 30 January 2024

    Abstract The developed system for eye and face detection using Convolutional Neural Networks (CNN) models, followed by eye classification and voice-based assistance, has shown promising potential in enhancing accessibility for individuals with visual impairments. The modular approach implemented in this research allows for a seamless flow of information and assistance between the different components of the system. This research significantly contributes to the field of accessibility technology by integrating computer vision, natural language processing, and voice technologies. By leveraging these advancements, the developed system offers a practical and efficient solution for assisting blind individuals. The modular… More >

  • Open Access

    ARTICLE

    A Large-Scale Group Decision Making Model Based on Trust Relationship and Social Network Updating

    Rongrong Ren1,2, Luyang Su1,2, Xinyu Meng1,2, Jianfang Wang3, Meng Zhao1,2,4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 429-458, 2024, DOI:10.32604/cmes.2023.027310 - 22 September 2023

    Abstract With the development of big data and social computing, large-scale group decision making (LGDM) is now merging with social networks. Using social network analysis (SNA), this study proposes an LGDM consensus model that considers the trust relationship among decision makers (DMs). In the process of consensus measurement: the social network is constructed according to the social relationship among DMs, and the Louvain method is introduced to classify social networks to form subgroups. In this study, the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights. In the… More >

  • Open Access

    ARTICLE

    Real-Time Spammers Detection Based on Metadata Features with Machine Learning

    Adnan Ali1, Jinlong Li1, Huanhuan Chen1, Uzair Aslam Bhatti2, Asad Khan3,*

    Intelligent Automation & Soft Computing, Vol.38, No.3, pp. 241-258, 2023, DOI:10.32604/iasc.2023.041645 - 27 February 2024

    Abstract Spammer detection is to identify and block malicious activities performing users. Such users should be identified and terminated from social media to keep the social media process organic and to maintain the integrity of online social spaces. Previous research aimed to find spammers based on hybrid approaches of graph mining, posted content, and metadata, using small and manually labeled datasets. However, such hybrid approaches are unscalable, not robust, particular dataset dependent, and require numerous parameters, complex graphs, and natural language processing (NLP) resources to make decisions, which makes spammer detection impractical for real-time detection. For… More >

  • Open Access

    ARTICLE

    Maximizing Influence in Temporal Social Networks: A Node Feature-Aware Voting Algorithm

    Wenlong Zhu1,2,*, Yu Miao1, Shuangshuang Yang3, Zuozheng Lian1,2, Lianhe Cui1

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3095-3117, 2023, DOI:10.32604/cmc.2023.045646 - 26 December 2023

    Abstract Influence Maximization (IM) aims to select a seed set of size k in a social network so that information can be spread most widely under a specific information propagation model through this set of nodes. However, most existing studies on the IM problem focus on static social network features, while neglecting the features of temporal social networks. To bridge this gap, we focus on node features reflected by their historical interaction behavior in temporal social networks, i.e., interaction attributes and self-similarity, and incorporate them into the influence maximization algorithm and information propagation model. Firstly, we propose… More >

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