Social Online Research Articles

Problematic social media use and inhibitory control among post-secondary students.

Problematic social media use (PSMU) parallels traditional characteristics of substance use disorders, including salience, functional impairment, and tolerance. An excessive focus on social reward and engaging in negative behaviors on social media may decrease certain executive functions. However, the role of how inhibitory control processes manifest in PSMU are not well understood. The current study aims to explore both the presence of inhibitory control impairments and harmful online behaviors, such as negative social comparison, in problematic social media use. A population of emerging adults (undergraduate university students; N=503) completed several online questionnaires assessing problematic social media use, trait impulsivity, and negative social comparison on social media. An online Go-Nogo and Iowa Gambling Task were administered to further evaluate inhibitory control. Higher problematic social media use was significantly positively correlated with trait impulsivity, but not Go-Nogo or Iowa Gambling task performance. Negative social comparison on social media was a stronger predictor of PSMU, compared to inhibitory control measures. However, social comparison did not significantly interact with inhibitory control measures when predicting PSMU. When exploring subtypes of impulsivity, the strongest correlation with PSMU was observed with cognitive instability and attentional impulsivity. Our results suggest that aspects of impaired inhibitory control-namely, cognitive instability and attentional impulsivity-are present in problematic social media use. Understanding the interplay between features of addiction and the unique context of online socialization, such as negative social comparison, is central to defining problematic social media use as a potential behavioral addiction.

Online and Offline Social Sensitivity as Mediator Between Online Vigilance and Depression, Anxiety, and Stress Among Algerian Female Students.

The purpose of this study is to investigate the mediating role of online and offline social sensitivity in the association between online vigilance and mental health outcomes-specifically depression, anxiety, and stress-among Algerian female university students. A cross-sectional study was conducted with 438 Algerian female university students. Validated scales were utilized to measure online vigilance, social sensitivity (both online and offline), and psychological distress. Data was analyzed using correlation and mediation analyses to explore the relationships among these variables and to assess the mediating effect of social sensitivity. The findings indicate a significant positive relationship between online vigilance, social sensitivity, and mental health problems. Online and offline social sensitivity fully mediated the influence of online vigilance on stress, anxiety, and depression. This suggests that higher engagement in online vigilance increases social sensitivity, which in turn heightens vulnerability to psychological distress. This study contributes to the understanding of the complex interplay between digital behaviors, social perceptions, and mental health among Algerian female university students. By highlighting the mediating role of social sensitivity, the research underscores the necessity for interventions that address online habits and enhance social coping skills to improve mental well-being in this population.

CEFR updates (2020)-based next-gen immersive learning in 5 steps

The Common European Framework of Reference for Languages (CEFR) can be defined as an action-oriented framework that systematically employs “can do” descriptors to structure the processes of foreign language teaching and learning. After a comprehensive literature review, it was evident that there was no descriptive content analysis study on the CEFR (2020) in terms of immersive learning technologies. Based on the existing shortcomings, the focus of this study was to identify the updates in the CEFR and the key elements that formed the connection points of immersive learning. This study, due to its scope and content, was conducted within the framework of qualitative research methodologies, employing document analysis and descriptive content analysis. Based on the research results, it is possible to assert that the situational teaching method based on the digital/human digital twins; cognitive immersive language learning (CILL) approach rooted cognitive immersive rooms; interactive conversational agents capable of code switching; adaptive gamification approaches combining gamification techniques and educational data mining methods, accent-robuts automated speech recognition systems grounded in the sociolinguistic approach, and online socialization metaverse networks woven with social virtual reality (SVR) and collaborative virtual environment (CVE) concepts built upon the phenomenon of heterotopia stood out as the “paradigm identifiers” for the next-gen of foreign language teaching forms. As far as the research implications are concerned, the results affirmed the components and pedagogical approaches that would form the backbone for the integration of immersive technologies into CEFR-based language learning. Theoretically speaking, the CEFR-based immersive learning method was proposed for future studies in the age of AI-driven technologies.

Evaluation of Changes in Students’ Perceptions of Scientific Article Writing Skills through a Cohort Survey

Purpose of the study: To evaluate the development of students’ perceptions of their ability to write scientific articles and to identify the parts of scientific articles that are most understood and most difficult to understand by students. Methodology: This cohort survey research was conducted by distributing questionnaires twice, namely in December 2024 and March 2025. The first survey was distributed via Microsoft Form during an online socialization event that focused on the writing of scientific articles from undergraduate theses. There were 67 students present at the event. The second survey was conducted in person during a socialization event that introduced scientific journals affiliated with the Biology Education Study Program at Faculty of Teacher Training and Education, Universitas Syiah Kuala. The event was attended by 68 students. The data obtained were subsequently analyzed using descriptive statistics. Main Findings: There has been a positive development in students’ perception of their ability to write scientific articles. In addition, this research successfully identified the parts of scientific articles that are most understood and those that are still difficult for students to comprehend. Novelty/Originality of this study: This research uses a longitudinal survey, specifically a cohort survey, to obtain a comprehensive evaluation of the development of students’ perceptions in writing scientific articles.

Nonlinear Association Between Smartphone Usage Duration and Online Social Anxiety: An Empirical Study Based on Chinese College Students

This study aimed to investigate the relationship between smartphone use duration and online social anxiety among Chinese college students, with particular focus on potential nonlinear characteristics. Using a cross-sectional design with questionnaire survey, data were collected from a sample of college students. Daily smartphone use duration was assessed through self-report measures, while online social anxiety was measured using the General Online Social Anxiety Inventory Scale (GAAIS). Descriptive statistics revealed that participants used smartphones for an average of 6.83 hours daily (SD = 2.76) with a multimodal distribution, and had a mean online social anxiety score of 30.31 (SD = 10.14). As the data did not meet normality assumptions, Spearman's rank correlation analysis was employed to examine the relationship between variables. Results indicated a weak positive correlation between smartphone use duration and online social anxiety (ρ = 0.192, p < .001), with a notable nonlinear pattern: in the low to moderate usage range (1-6 hours), anxiety levels increased with usage time, while in the high usage range (>6 hours), this association plateaued, demonstrating a "saturation effect." These findings challenge the simple linear assumption that "more usage leads to higher anxiety" and support a more complex relationship model. The results provide important implications for developing smartphone use guidelines and targeted mental health interventions for college students. Future research should explore the differential impact of various usage purposes and patterns on social anxiety, as well as potential mediating or moderating mechanisms in this relationship.

Explaining Opinion Dynamics in Online Social Media: A Comparison of Attitudes Toward Climate Change Across U.S. States

This study explores the factors that may influence public perceptions of climate change by linking various datasets from the United States. It aims to identify patterns among climate change deniers within social media data. The outcome variable in this project is the percentage of climate change denial (people denying that climate change is man-made) in each state on Twitter. Explanatory variables include GDP per capita, gender, and local temperature. The study utilises Python and R programming languages for descriptive analyses and statistical modelling based on large-scale digital trace data. Additionally, the analysis involves constructing both an ordinary least squares regression model and one-way and two-way fixed-effects models. Secondary datasets are collected to measure the variables, and these datasets are merged at the state level. The findings suggest that states with higher GDP per capita tend to have more people acknowledging on social media that climate change is influenced by human activity. However, the effect of GDP per capita on climate change denial is extremely small, and it does not correlate with the unmeasured fixed effects that influence the proportion of denial-related tweets. Moreover, gender shows no statistically significant correlation with climate change perceptions based on our data-driven research. On the other hand, higher local median temperatures in a state are associated with a greater likelihood of citizens forming personal beliefs that humans contribute significantly to climate change. This research can facilitate social media platform moderation and assist the government in identifying the geographic distribution of climate change denial, thereby fostering a more united community to combat climate change.

Digital Phenotyping Measurement of Smartphone Social Behavior is Associated with Illness Progression Risk Scores in Young People at Clinical High Risk for Psychosis.

Young people at clinical high risk for psychosis (CHR-p) commonly experience social impairment, which contributes to functional decline and predicts transition to psychotic illness. Although the use of smart phone technology and social media platforms for social interaction is widespread among today's youth, it is unclear whether aberrant digital social interactions contribute to risk for conversion and functional impairment in CHR-p. The current study sought to characterize the nature of social smartphone and social media use in a CHR-p sample and determine its association with clinical symptoms and risk for conversion to psychosis. CHR-p (n = 132) and HC (n = 61) participants completed clinical interviews and 6 days of digital phenotyping that monitored total smartphone use, ratio of outgoing to incoming text messages and phone calls, social media use, and ecological momentary assessment surveys focused on in-person and electronic social interactions. Study Results: CHR-p did not differ from HC in total smartphone use for social communication or active social media use. However, CHR-p participants reported significantly less daily passive social media use compared to HC peers, and decreased text message reciprocity predicted 1- and 2-year conversion risk. Results demonstrate a nuanced digital social landscape with divergent relationships from in-person social behavior and suggest online socialization has implications for high-precision identification and intervention strategies among the CHR-p population.

Sosialisasi Panduan Penulisan Artikel Ilmiah Menggunakan Metode Penelitian Kuantitatif

This study aims to enhance students' understanding of writing scientific articles based on quantitative research methods. The socialization was conducted online via Zoom Meeting, supported by Google Classroom for further discussions. The event involved 80 participants from diverse academic backgrounds. The materials covered fundamental concepts of quantitative research, sampling techniques, data collection and analysis using statistical tools such as SPSS, and ethical considerations in scientific writing. Evaluation was carried out through an online survey to assess participants' comprehension of the presented material. The results indicate that this socialization provided broader insights into scientific article writing, particularly in adhering to academic standards and publication ethics. With continued mentoring, students are expected to apply quantitative methods in their research and produce high-quality publications that meet the standards of scientific journals. Keywords: quantitative research, scientific writing, data analysis, academic publication, online socialization.

Disagree or Remain Silent? Strategies of Political Communication Between Russian Citizens and State officials on VK Online Social Media (Case of Leningrad Oblast)

In 2020, state authorities in Russia began actively engaging with citizens through social media, creating new channels for e-participation. While numerous foreign studies explore how social media can transform e-participation systems, research on the quality of social media dialogue between society and government, particularly in countries with weak deliberative traditions, remains limited. One key issue for Russian-speaking internet users, especially amid global transformations, is the dynamics of agreement and disagreement between citizens and the government. This study aims to analyze and describe how Russian citizens and government officials express agreement and disagreement in online discussions on social issues on the official platform of the Leningrad Oblast Governor on VK online social media. Using a modified discourse analysis methodology, we examined 1286 comments on an ecology-related topic. We analyzed the frequency, orientation, and typology of agreement and disagreement, focusing on argumentation and politeness in online communication. The findings revealed that citizens were more active and expressive in both agreeing and disagreeing with each other and authorities. In contrast, authorities tended to remain silent or react mildly to disagreements. This study highlights the potential for social media to foster public dialogue but also underscores the need for more research on how to improve the quality of these interactions.

Locked among inequalities: A study of children’s digital experiences and digital divide during the COVID-19 pandemic

This article examines children’s digital experiences during the COVID-19 pandemic as a specific aspect of digital divide. Utilizing a survey of 2004 English parents aged 20–55 years, the study explores how various factors – including household living conditions, parents’ sociodemographic status and sociotechnical variables such as children’s usage frequency and intensity, expenditure on technology and parents’ digital skills – affected different dimensions of children’s digital experiences during the lockdown. These dimensions include academic performance, connectivity issues, social interaction, feelings of isolation, problematic use and social support. The findings reveal distinct age-related trends, with older children more frequently engaging in online socialization. In addition, the study highlights a correlation between more favourable household socioeconomic conditions and improved digital experiences for children.

Monetized socialization on the front end: exchanging money as social activities through Red Packet and Transfer on WeChat

ABSTRACT Research on the monetization of sociality in social media often emphasizes the back-end processes, where people’s online connections and relationships are commodified for platforms’ financial gain. This paper argues for expanding existing studies on monetized socialization by paying attention to the front-end, where users’ online connections occur in relation to the experiences of money. Focusing on the digitalization of the Chinese cultural tradition of hongbao on WeChat, I show how WeChat extends the possibilities of online connections in a monetized way through money-exchanging services such as WeChat Red Packet and money Transfer. Exploring the heterogeneous social activities in relation to WeChat hongbao in everyday life, I suggest that WeChat shapes the configuration of sociality where money is an essential mechanism through which digital sociality is produced and experienced. By showing how back-end systems are implicated in users’ front-end monetary connections, I argue that such reconfiguration of digital sociality is integral to the platform economy, advancing the understanding of the dual layers of monetization at play in online sociality.

Adolescents' Risky Online Behaviors and Social Desirability Bias in Vietnam: Privacy Disclosure and Pornography Use.

This study quantified the prevalence of two risky online behaviors among adolescents in Vietnam: online privacy disclosure and online pornography use. We conducted a field experiment with 1,313 junior high school students aged 13-15 years in Hoa Binh city. In addition to conventional direct questions, we employed list experiments to address social desirability bias in the students' responses. The results indicated that 49.9% of the adolescents engaged in online privacy disclosure and that 58.5% were involved in online pornography use. This study revealed significant underreporting among adolescents (35.6 and 43.3 percentage points for privacy disclosure and pornography use, respectively). The heterogeneous analyses revealed that recent smartphone ownership and active smartphone and Facebook use were associated with a greater prevalence of these behaviors. Notably, urban adolescents showed greater engagement in pornography use than did their rural counterparts. This study represents a pioneering effort to empirically investigate sensitive online behaviors among adolescents utilizing an experimental approach to address measurement bias. Our findings suggest that list experiments are a robust method for assessing sensitive issues and emphasize the critical need for educational interventions to mitigate online risks faced by young people.

Book review: Jesse W. C. Yip, Discourse of Online Social Support: A Study of Online Self-Help Groups for Anxiety and Depression

Book review: Jesse W. C. Yip, <i>Discourse of Online Social Support: A Study of Online Self-Help Groups for Anxiety and Depression</i>

Internet Gaming Addiction or Online Social Networking Addiction? Evidence from Chinese Adolescents Facing Parental Phubbing

Internet Gaming Addiction or Online Social Networking Addiction? Evidence from Chinese Adolescents Facing Parental Phubbing

Peningkatan Kepatuhan Pajak Melalui Sosialisasi Perpajakan kepada Komunitas Bisnis Multi-Level Marketing Minyak Aroma Terapi di Yogyakarta

This study aims to evaluate the effectiveness of tax socialization in improving tax compliance among the multi-level marketing (MLM) business community of aromatherapy oil in Yogyakarta. The research employs a qualitative approach using in-depth interviews, direct observation, and documentation methods. The results indicate that despite high participant enthusiasm, there is a gap in tax compliance. Out of 25 participants, 5 individuals did not possess a Taxpayer Identification Number (NPWP), while 15 out of 20 participants with an NPWP had not reported their MLM income. Limited understanding of tax administrative obligations emerged as a key factor. The tax socialization methods employed, including face-to-face and online approaches, had their respective advantages and drawbacks. Face-to-face socialization was perceived as more interactive, whereas online socialization offered flexibility. This study recommends more intensive educational approaches and follow-up mentoring programs to assist participants in comprehending and fulfilling their tax obligations correctly. These findings are expected to serve as a reference for the government and tax authorities in designing more effective tax socialization strategies to enhance tax compliance, particularly among small and medium-sized enterprise segments

Optimized Deep Learning Techniques to Identify Rumors and Fake News in Online Social Networks

The swift expansion of networking platforms has led to a significant proliferation of fake news on social media in recent years, posing a serious risk to public safety. This phenomenon carries various potential negative effects on society, including the erosion of public confidence in journalists and governmental institutions. Consequently, the identification of fake news has attracted considerable attention from researchers across various fields. As online and social media platforms have grown, it has become easier for false information to mix in with real or verified information. People who spread false information usually have some kind of political or social goal in mind when they spread their hoaxes. Because of this, it is of the utmost importance to come up with a trustworthy way to spot false information. This article describes a way to use deep learning to spot fake news. Methodology is made up of a set of input data. The information in this dataset comes from the social networking site Twitter. First, the raw data that is being used are preprocessed. Stop word removal, stemming, and tokenization are the main parts of data preprocessing. The NTLK library is used to get rid of stop words. Porter's Algorithm is used to do stemming. N-gram model is used to do tokenization. LSTM, CNN, and AdaBoost algorithms are used to build the model. Results have shown that LSTM is better than CNN and AdaBoost in terms of accuracy, specificity, and sensitivity. LSTM has achieved an accuracy of 99.24% for fake news detection. Specificity of LSTM is 99.2%. LSTM's sensitivity is 98.67%. LSTM has achieved an accuracy of 99.24% for fake news detection. Specificity of LSTM is 99.2% and sensitivity is 98.67%. Received: 1 May 2024 | Revised: 4 September 2024 | Accepted: 19 October 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The Twitter Sentiment Analysis datasets that support the findings of this study are openly available at https://www.kaggle.com/competitions/tweet-sentiment-extraction/discussion/142142. The glove Twitter data that support the findings of this study are openly available at https://www.kaggle.com/datasets/bertcarremans/glovetwitter27b100dtxt. Author Contribution Statement Abu Sarwar Zamani: Conceptualization, Writing – original draft, Project administration, Funding acquisition. Aisha Hassan Abdalla Hashim: Writing – review &amp; editing, Supervision. Sara Saadeldeen Ibrahim Mohamed: Investigation, Visualization. Md. Nasre Alam: Software, Writing – review &amp; editing, Data curation.

Build Online and Offline Social Governance Systems to Help Improve the Quality and Efficiency of Social Governance

Build Online and Offline Social Governance Systems to Help Improve the Quality and Efficiency of Social Governance

An Analysis of Online and Offline Social Characteristics and Differences among Contemporary Youth

An Analysis of Online and Offline Social Characteristics and Differences among Contemporary Youth

A Trust-Based Personalized Differential Privacy Guarantees for Online Social Networks

A Trust-Based Personalized Differential Privacy Guarantees for Online Social Networks

AI Techniques and Applications for Online Social Networks and Media: Insights From BERTopic Modeling

AI Techniques and Applications for Online Social Networks and Media: Insights From BERTopic Modeling