Amjad A. Alsuwaylimi^1,*, Zaid S. Alenezi²

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3165-3185, 2025, DOI:10.32604/cmc.2025.061674 - 16 April 2025

Abstract Cyberbullying is a remarkable issue in the Arabic-speaking world, affecting children, organizations, and businesses. Various efforts have been made to combat this problem through proposed models using machine learning (ML) and deep learning (DL) approaches utilizing natural language processing (NLP) methods and by proposing relevant datasets. However, most of these endeavors focused predominantly on the English language, leaving a substantial gap in addressing Arabic cyberbullying. Given the complexities of the Arabic language, transfer learning techniques and transformers present a promising approach to enhance the detection and classification of abusive content by leveraging large and pretrained… More >

Wan Ying Gan^1,#,*, Wei-Leng Chin^2,3,#, Shih-Wei Huang^4,5, Serene En Hui Tung⁶, Ling Jun Lee¹, Wai Chuen Poon⁷, Yan Li Siaw⁸, Kerry S. O’Brien⁹, Iqbal Pramukti¹⁰, Kamolthip Ruckwongpatr¹¹, Jung-Sheng Chen¹², Mark D. Griffiths¹³, Chung-Ying Lin^{10,11,14,15,*}

International Journal of Mental Health Promotion, Vol.27, No.3, pp. 319-331, 2025, DOI:10.32604/ijmhp.2025.060049 - 31 March 2025

Abstract Background: Weight-related self-stigma (WRSS) is prevalent among individuals with different types of weight status and is associated with a range of negative health outcomes. Social support and coping models explain how individuals may use different coping methods to deal with their mental health needs. Psychological distress (e.g., depression and stress) could lead to overuse of social media and smartphones. When using social media or smartphones, individuals are likely to be exposed to negative comments regarding weight/shape/size posted on the social media. Consequently, individuals who experience problematic social media use (PSMU) or problematic smartphone use (PSPU)… More >

Farah Mohammad^1,2,*, Saad Al-Ahmadi³, Jalal Al-Muhtadi^1,3

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1327-1345, 2025, DOI:10.32604/cmc.2025.060228 - 26 March 2025

Abstract Software defect prediction is a critical component in maintaining software quality, enabling early identification and resolution of issues that could lead to system failures and significant financial losses. With the increasing reliance on user-generated content, social media reviews have emerged as a valuable source of real-time feedback, offering insights into potential software defects that traditional testing methods may overlook. However, existing models face challenges like handling imbalanced data, high computational complexity, and insufficient integration of contextual information from these reviews. To overcome these limitations, this paper introduces the SESDP (Sentiment Analysis-Based Early Software Defect Prediction)… More >

Jianlong Wang^1,#, Xiumei Chen^1,2,#, Muqi Huang³, Rui Liu³, I-Hua Chen^4,5,*, Gordon L. Flett^6,*

International Journal of Mental Health Promotion, Vol.27, No.2, pp. 241-256, 2025, DOI:10.32604/ijmhp.2025.058503 - 03 March 2025

Abstract Background: This study addresses the pressing need to understand the nuanced relationship between ‘mattering’—the perception of being significant to others—and problematic internet use (PIU) among university students. Unlike previous research that has primarily employed variable-centered approaches, this study first adopts a person-centered approach using Latent Profile Analysis (LPA) to identify distinct mattering profiles. Subsequently, through variable-centered analyses, these profiles are examined in relation to different types of PIU—specifically problematic social media use (PSMU) and problematic gaming (PG)—as well as adaptability. Methods: Data were collected from 3587 university students across 19 universities in China. Participants completed… More >

Xiumei Chen^1,2, Xiaobing Lu^3,*, Yufu Ning¹, Lifeng Wang¹, Jeffrey H. Gamble⁴, Xianhe Chen⁵, Xingyong Jiang⁶, I-Hua Chen^7,*, Peijin Lin⁸

International Journal of Mental Health Promotion, Vol.27, No.1, pp. 51-63, 2025, DOI:10.32604/ijmhp.2024.057750 - 31 January 2025

Abstract Background: Resilience is crucial for medical college students to thrive in the highly stressful environment of medical education. However, the prevalence of problematic internet use (PIU) in this population may negatively impact their resilience. This study investigated the influence of problematic online gaming (PG) and problematic social media use (PSMU) on the resilience of medical college students in China. Methods: A sample of 5075 first-year medical college students from four Chinese universities was studied. PG served as the independent variable, resilience as the dependent variable, fatigue as the mediator, and PSMU as the moderator. Structural… More >

Yunyu Shi^1,2, Fanchang Kong^1,2,*, Min Zhu³

International Journal of Mental Health Promotion, Vol.26, No.12, pp. 977-985, 2024, DOI:10.32604/ijmhp.2024.057596 - 31 December 2024

Abstract Background: To reduce adolescents’ social anxiety, the study integrates external factors (social media usage) with internal factors (imaginary audience and appearance-based self-esteem) to internal mechanisms of adolescents’ social anxiety in the Internet age based on objective self-awareness theory and self-esteem importance weighting model. Methods: Utilizing the Social Media Usage Intensity Scale, Social Anxiety Scale, imaginary Audience Scale, and Physical Self Questionnaire, we surveyed 400 junior high school students from three schools in Hubei province, China. Results: A significantly positive correlation is revealed between the intensity of social media usage and both social anxiety and imaginary audience… More >

Dunya Ahmed Alkurdi^1,2,*, Mesut Cevik², Abdurrahim Akgundogdu³

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4285-4305, 2024, DOI:10.32604/cmc.2024.057029 - 19 December 2024

Abstract Deepfake has emerged as an obstinate challenge in a world dominated by light. Here, the authors introduce a new deepfake detection method based on Xception architecture. The model is tested exhaustively with millions of frames and diverse video clips; accuracy levels as high as 99.65% are reported. These are the main reasons for such high efficacy: superior feature extraction capabilities and stable training mechanisms, such as early stopping, characterizing the Xception model. The methodology applied is also more advanced when it comes to data preprocessing steps, making use of state-of-the-art techniques applied to ensure constant… More >

Muhammad Ali Ilyas¹, Abdul Rehman², Assad Abbas¹, Dongsun Kim^3,*, Muhammad Tahir Naseem^4,*, Nasro Min Allah⁵

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4525-4549, 2024, DOI:10.32604/cmc.2024.056291 - 19 December 2024

Abstract In an era dominated by information dissemination through various channels like newspapers, social media, radio, and television, the surge in content production, especially on social platforms, has amplified the challenge of distinguishing between truthful and deceptive information. Fake news, a prevalent issue, particularly on social media, complicates the assessment of news credibility. The pervasive spread of fake news not only misleads the public but also erodes trust in legitimate news sources, creating confusion and polarizing opinions. As the volume of information grows, individuals increasingly struggle to discern credible content from false narratives, leading to widespread… More >

Yu-Ting Huang^1,#, Daniel Kwasi Ahorsu^2,#, Emma Sethina Adjaottor^3,*, Frimpong-Manso Addo³, Mark D. Griffiths⁴, Amir H. Pakpour⁵, Chung-Ying Lin^1,6,7,8,*

International Journal of Mental Health Promotion, Vol.26, No.11, pp. 875-885, 2024, DOI:10.32604/ijmhp.2024.057049 - 28 November 2024

Abstract Background: The present study evaluated the psychometric properties of Problematic Internet Use (PIU) instruments and their correlation with psychological distress and time spent on Internet activities among university students in Ghana. Methods: In the present cross-sectional survey design study, 520 participants (35.96% female) were recruited with a mean age of 19.55 years (SD = 1.94) from several university departments (i.e., Behavioral Sciences, Materials Engineering, Nursing and Midwifery, and Biochemistry and Biotechnology) of Kwame Nkrumah University of Science and Technology (KNUST) between 19 July and 04 August, 2023. Participants completed a survey that included the following… More >

Stephen Afrifa^1,2,*, Vijayakumar Varadarajan^3,4,5,*, Peter Appiahene², Tao Zhang¹, Richmond Afrifa⁶

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1625-1639, 2024, DOI:10.32604/csse.2024.055084 - 22 November 2024

Abstract Users of social networks can readily express their thoughts on websites like Twitter (now X), Facebook, and Instagram. The volume of textual data flowing from users has greatly increased with the advent of social media in comparison to traditional media. For instance, using natural language processing (NLP) methods, social media can be leveraged to obtain crucial information on the present situation during disasters. In this work, tweets on the Uttarakhand flash flood are analyzed using a hybrid NLP model. This investigation employed sentiment analysis (SA) to determine the people’s expressed negative attitudes regarding the disaster. More >