Deema Mohammed Alsekait^1,*, Asif Nawaz², Ayman Nabil³, Mehwish Bukhari², Diaa Salama AbdElminaam^3,4,5,6,*

Computer Systems Science and Engineering, Vol.48, No.4, pp. 1031-1054, 2024, DOI:10.32604/csse.2024.051847

Abstract The article describes a novel method for sentiment analysis and requirement elicitation from social media feedback, leveraging advanced machine learning techniques. This innovative approach automates the extraction and classification of user requirements by analyzing sentiment in data gathered from social media platforms such as Twitter and Facebook. Utilizing APIs (Application Programming Interface) for data collection and Graph-based Neural Networks (GNN) for feature extraction, the proposed model efficiently processes and analyzes large volumes of unstructured user-generated content. The preprocessing pipeline includes data cleaning, normalization, and tokenization, ensuring high-quality input for the sentiment analysis model. By classifying… More >

Meng Zhang^1,2, Xiangyang Luo^1,2,*, Ningbo Huang²

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2513-2532, 2024, DOI:10.32604/cmes.2024.050517

Abstract Geolocating social media users aims to discover the real geographical locations of users from their publicly available data, which can support online location-based applications such as disaster alerts and local content recommendations. Social relationship-based methods represent a classical approach for geolocating social media. However, geographically proximate relationships are sparse and challenging to discern within social networks, thereby affecting the accuracy of user geolocation. To address this challenge, we propose user geolocation methods that integrate neighborhood geographical distribution and social structure influence (NGSI) to improve geolocation accuracy. Firstly, we propose a method for evaluating the homophily… 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

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 >

Yijie Ye¹, Han Wang², Liujiang Ye¹, Hao Gao^1,*

International Journal of Mental Health Promotion, Vol.26, No.5, pp. 361-376, 2024, DOI:10.32604/ijmhp.2024.049606

Abstract Sleep quality is closely linked to people’s health, and during the COVID-19 pandemic, the sleep patterns of residents in China were notably poor. The lockdown in China led to an increase in social media use, prompting questions about its impact on sleep. Therefore, this study investigates the association between social media use and sleep quality among Chinese residents during the COVID-19 outbreak, highlighting the potential mediating role of social media addiction. Data were collected via questionnaires through a cross-sectional survey with 779 valid responses. Variance analysis was used to test for differences in social media… 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

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 >

Pakorn Santakij¹, Samai Srisuay^2,*, Pongporn Punpeng¹

Computer Systems Science and Engineering, Vol.48, No.3, pp. 665-689, 2024, DOI:10.32604/csse.2024.045066

Abstract Social media has revolutionized the dissemination of real-life information, serving as a robust platform for sharing life events. Twitter, characterized by its brevity and continuous flow of posts, has emerged as a crucial source for public health surveillance, offering valuable insights into public reactions during the COVID-19 pandemic. This study aims to leverage a range of machine learning techniques to extract pivotal themes and facilitate text classification on a dataset of COVID-19 outbreak-related tweets. Diverse topic modeling approaches have been employed to extract pertinent themes and subsequently form a dataset for training text classification models.… More >

Weijian Fan^1,*, Ziwei Shi²

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2723-2741, 2024, DOI:10.32604/cmc.2024.050344

Abstract With the explosive growth of false information on social media platforms, the automatic detection of multimodal false information has received increasing attention. Recent research has significantly contributed to multimodal information exchange and fusion, with many methods attempting to integrate unimodal features to generate multimodal news representations. However, they still need to fully explore the hierarchical and complex semantic correlations between different modal contents, severely limiting their performance detecting multimodal false information. This work proposes a two-stage detection framework for multimodal false information detection, called ASMFD, which is based on image aesthetic similarity to segment and… More >

Zohaib Ahmad Khan¹, Yuanqing Xia^1,*, Ahmed Khan², Muhammad Sadiq², Mahmood Alam³, Fuad A. Awwad⁴, Emad A. A. Ismail⁴

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2771-2793, 2024, DOI:10.32604/cmc.2024.046897

Abstract Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significant source of user-generated content. The development of sentiment lexicons that can support languages other than English is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existing sentiment analysis systems focus on English, leaving a significant research gap in other languages due to limited resources and tools. This research aims to address this gap by building a sentiment lexicon for local languages, which is then used with a machine learning algorithm for efficient sentiment analysis.… More >

Kamolthip Ruckwongpatr^1,#, Chirawat Paratthakonkun^2,#, Usanut Sangtongdee^3,4,*, Iqbal Pramukti⁵, Ira Nurmala⁶, Kanokwan Angkasith⁷, Weena Thanachaisakul⁷, Jatuphum Ketchatturat⁸, Mark D. Griffiths⁹, Yi-Kai Kao^10,*, Chung-Ying Lin^1,5,11,12

International Journal of Mental Health Promotion, Vol.26, No.4, pp. 293-302, 2024, DOI:10.32604/ijmhp.2024.047023

Abstract Background: In recent years, there has been increased research interest in both smartphone addiction and social media addiction as well as the development of psychometric instruments to assess these constructs. However, there is a lack of psychometric evaluation for instruments assessing smartphone addiction and social media addiction in Thailand. The present study evaluated the psychometric properties and gender measurement invariance of the Thai version of the Smartphone Application-Based Addiction Scale (SABAS) and Bergen Social Media Addiction Scale (BSMAS). Method: A total of 801 Thai university students participated in an online survey from January 2022 to More >

Yasmine M. Ibrahim^1,2, Reem Essameldin³, Saad M. Darwish^1,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 243-262, 2024, DOI:10.32604/cmc.2024.047840

Abstract Detecting hate speech automatically in social media forensics has emerged as a highly challenging task due to the complex nature of language used in such platforms. Currently, several methods exist for classifying hate speech, but they still suffer from ambiguity when differentiating between hateful and offensive content and they also lack accuracy. The work suggested in this paper uses a combination of the Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) to adjust the weights of two Multi-Layer Perceptron (MLPs) for neutrosophic sets classification. During the training process of the MLP, the WOA is More >