Social Network Users Research Articles

The Relationship Between Screen Time, Social Network Usage, and Physical Activity with Depression in Students at Qazvin University

Background: Depression and its related variables are especially interesting and important to study in the context of the virtual world. Objectives: The purpose of the present study was to investigate the relationship between screen time, the amount of social network usage, and physical activity with depression in students. Methods: The research method was a descriptive correlation study. One hundred sixty undergraduate students from Qazvin Universities were selected as a sample in the spring of 2022 using multi-stage cluster sampling. After dropping some subjects, the sample size was reduced to 146. The participants completed questionnaires about screen time, social network usage, physical activity, and depression. The obtained data were analyzed using Pearson's correlation coefficient and simultaneous regression. Results: The results showed a significant relationship between screen time and depression (r = 0.35, P < 0.01). There was also a significant relationship between the use of social networks and depression (r = 0.17, P < 0.05), but no relationship was observed between physical activity and depression (r = 0.08, P > 0.05). Additionally, it was found that the predictor variables could explain 13% of the variance in the depression variable. Conclusions: The contribution of the screen time variable in explaining depression symptoms was higher than that of the other variables.

Identifying Dominant Actors of Ferdy Sambo's Case Network on Social Media X/Twitter Using Social Network Analysis for Public Relations Strategy

The Indonesian National Police (Polri) has experienced ups and downs in building a positive image in interacting with the public. This decline in trust is caused by the emergence of various issues that show the low performance of the police. In the Ferdy Sambo case study, the performance and integrity of the police is at stake and the sensitivity of the police to meet public expectations. One solution to im-prove the image is through an effective public relations strategy. However, to develop it, a deep un-derstanding of the characteristics and interaction patterns between social media through social net-work analysis is required. This research aims to identify influential X/Twitter actors in the case study of Inspector General Ferdy Sambo by applying the centrality method in Social Network User Analysis. The results of centrality analysis on the network show a wide variety of centrality levels. The @Zaindamai account dominates with the highest Degree Centrality value of 0.426829, indicating the number of connections in the network. The main role in disseminating information is held by @Zaindamai with the highest Betweenness Centrality value of 0.325748, indicating its role in connect-ing various networks. @Rizkynu46127931 stands out in Closeness Centrality with a high value of 0.497791, signifying quick and efficient access to all parts of the social network. In addition, @Rizkynu46127931 has significant influence in the network based on the highest Eigenvector Cen-trality of 0.245625. This centrality value forms the basis for formulating a more focused public relations strategy, improving the efficiency of communication with stakeholders, and designing a more concrete public relations plan.

Prediction of influential nodes in social networks based on local communities and users’ reaction information

Identifying influential nodes is one of the basic issues in managing large social networks. Identifying influence nodes in social networks and other networks, including transportation, can be effective in applications such as identifying the sources of spreading rumors, making advertisements more effective, predicting traffic, predicting diseases, etc. Therefore, it will be important to identify these people and nodes in social networks from different aspects. In this article, a new method is presented to identify influential nodes in the social network. The proposed method utilizes the combination of users’ social characteristics and their reaction information to identify influential users. Since the identification of these users in the large social network is a complex process and requires high processing power and time, clustering and identifying communities have been used in the proposed method to reduce the complexity of the problem. In the proposed method, the structure of the social network is divided into its constituent communities and thus the problem of identifying influential nodes (in the entire network) turns into several problems of identifying an influential node (in each community). The suggested method for predicting the nodes first predicts the links that may be created in the future and then identifies the influential nodes based on an iterative strategy. The proposed algorithm uses the criteria of centrality and influence domain to identify this category of users and performs the identification process both at the community and network levels. The efficiency of the method has been evaluated using real databases and the results have been compared with previous works. The results demonstrate that the proposed method provides a more suitable performance in detecting the influential nodes and is superior in terms of accuracy, recall and processing time.

Boredom Proneness and Social Media Usage of College Students

This descriptive-predictive study aimed to determine the correlation between the social media usage of college students and their boredom proneness in a University in Davao region. A total of 350 participants were chosen to participate in this study using simple random sampling techniques. A 22-item multidimensional state boredom questionnaire and 21-item social networking usage served as an instrument wherein the Jamovi was used for data analysis. The mean and standard deviation were utilized to test the level of both variables, and results showed that the level of social media usage and boredom proneness among students is high. The Shapiro-Wilk test was utilized to test the normality; it revealed that the data is not normally distributed. The Spearman rho correlation coefficient test revealed a significant relationship between social media usage and students’ boredom proneness, with a p-value of .001, indicating a strong positive correlation. Meanwhile, the result of Linear Regression showed 71% of the variance is explained by the three significant predictors contributing to the students’ social media usage which are Time Perception, Inattention and Disengagement. The study’s findings indicate that increased use of social media is associated with higher levels of boredom. In this case, school administration, local health authorities, and school counseling and guidance offices should enhance the implementation of policies regarding the responsible use of social media, integrate the findings into various programs, and increase public awareness of the effects of excessive social media use on people’s mental health and academic performance. Lastly, researchers should consider extending this topic with a quantitative approach and investigate the possible mediating factors between boredom and social media usage.

How to Keep Millennial Social Network Users Active? Millennial Social Network Usage on Mobile Devices Across Cultures

ABSTRACT Despite the continuous upsurge in the use of social networks by consumers and marketers, the empirical research investigating their economic value in different cultures still lags. This study develops an extended Theory of Reasoned Action (TRA) model that includes trust in the social network community, perceived enjoyment, and Hofstede’s cultural factors (masculinity/femininity, power distance, individualism/collectivism, and long-term objectives). Using survey methodology across three countries (Kuwait, Tunisia, and Italy), data is analyzed using a structural equation model. The results of this study empirically extended the TRA model by including the significant impact of perceived enjoyment and attitude on the use of Facebook across different cultures. Trust in the social network community and its impact on the use of Facebook was only significant in Arab culture and not European culture. Hofstede’s factors moderate the relationship between subjective norms and usage of Facebook, and their impact is varies across different cultures. The study highlights the differential impact of cultures on social network use and the importance of understanding the type of culture the business is targeting. Commercial organizations should guide their marketing efforts to manage their marketing differently across cultures to enhance their consumer engagement.

COVID-19’s myths, facts, concerning and obstinate posts on social network, and the mental health status of social network users in Bangladesh

Myths, misinformation, facts like posts spread by social media during COVID-19 pandemic had an enormous effect on psychological health. This study aimed to investigate social media based COVID-19’s posts and the psychological health status of participants. A cross-sectional, online survey-based study was conducted in between April to October 2021 using a structured and semi-structured questionnaire, predominantly involving 1200 active social network users in Bangladesh. Depression, anxiety, and stress were assessed using the Depression, Anxiety, and Stress Scale (DASS-21), while the Insomnia Severity Index (ISI) measured insomnia severity for selected participants. Internal reliabilities were calculated with Cronbach’s alpha coefficients (cut-off point 0.70). Unrelated multivariate logistic regression explored correlations among outcome errors, with the model assessing the impact of selected independent variables on mental health. The findings demonstrated that 27.8% individuals spread facts whereas 7.4% spread myths and misinformation about COVID-19 on social networks. Furthermore, 28.1% and 36.7% shared obstinate and concerning posts respectively. The prevalence of depression, anxiety and stress symptoms, ranging from mild to extremely severe, were 43.9%, 30.9%, and 23.8% respectively. However, 2.8% had severe level of insomnia. Facts, myths, tour attending, and no mask group photos were significantly associated with anxiety, and less likelihood of experiencing anxiety. Interestingly, circulating such activities on social networks had no significant association with depression, stress, or insomnia. The spread of misinformation on social media undermines any efforts to contain COVID-19 infection. The findings hugely recommend of using fact checking facilities and adaptation to the pandemic situations to maintain lower prevalence of depression, anxiety, stress and insomnia.

Hoax News As An Ethical Violation Impact Of Information Technology Advances

The increasingly rapid development of technology must also be accompanied by the mindset of social network users to have harmony when facing changing times. Rephrase: Adjust the reception and dissemination of information using technology according to reality. It aims to describe the phenomenon of fake news (hoax) on social networks and online media and how to prevent fake news (hoax). It is used to visualize and describe data and facts about hoaxes and their spread through social media or online media. The study results show that easy and cheap technological advances are determining factors for information accessibility. Besides that, social media activists must also be more imaginative in utilizing information, especially an educated understanding of the media, which is often considered a source of truth, as well as the importance of the government's role in controlling the spread of fake news (hoax) that determines political decisions. Legal policies are regulated in the ITE Law.

Unraveling influential factors shaping employee cybersecurity behaviors: an empirical investigation of public servants in Vietnam

Purpose Government employees must comply with policies on information security regulations, online security practices, social networking usage, internet addiction, online cyberthreats and other related habits. These activities are considered cybersecurity behaviors. Government social media (GSM) accounts are increasingly used to educate employees about cybersecurity risks. To support the effectiveness of cybersecurity practices in government organizations, the purpose of this study is to investigate the impacts of GSM and organizational policy compliance on employees’ cybersecurity awareness, motivation and behaviors. Design/methodology/approach Data were obtained by administering a questionnaire survey to public personnel in Vietnam. A total of 330 valid responses were obtained, and the research hypotheses were tested using partial least squares–structural equation modeling. Findings First, cybersecurity awareness enhances information protection motivation and employee protective behavior. Second, GSM has positive impacts on cybersecurity knowledge and information protection motivation. Third, there is a strong positive association between information protection motivation and employee protective behavior. Finally, while organizational compliance significantly increases cybersecurity awareness, its impact on employee protective behavior is ind irect. Originality/value This research enhances the literature on the behavioral dimension of cybersecurity. The primary objective of this study is to assess the influence of cybersecurity awareness on protective behaviors rather than intents and attitudes alone. Furthermore, this research integrates protection motivation theory and cultivation theory to provide a more thorough assessment of cybersecurity awareness and protective behavior. By investigating the impact of GSM on the level of cybersecurity awareness among employees within government organizations, this study provides valuable insights into the efficacy of recent governmental initiatives aimed at fostering cybersecurity.

Suspicious behaviour detection in multilayer social networks using PF-KMA and SS-GAE techniques

Multilayer Social networks are an important part of human life to interact on different networks at the same time. Due to the openness of such networks, they become a platform for spammers to spread malicious behaviors. Hence, there is an urgent need for effective detection of malicious behaviors; thereby, enabling the networks to take mitigation actions to decrease the possibility to reward such activities. Detection of suspicious behaviors in previous works is challenging due to the problems of community detection, the large amount of feature corruption, and memory requirements. Thus, to deal with such problems, in this paper, an efficient clustering-based detection of malicious users in multilayer social networks is proposed. Initially, the input dataset is pre-processed and used for Exponential Distribution based Erdős–Rényi based graph construction. From the graph structure, two types of data, such as user representations and graph features are extracted for graph encoding using the Soft Sign activated Graph Auto Encoder model. Then, the decoding is done to predict the information diffusion level, thereby, ranking the users using the Laplace Regularization technique. Then, the ranked users are clustered into different groups using the Pareto Front based K-Means Clustering Algorithm technique. Finally, the experimental results were analyzed to demonstrate the efficacy of the proposed model to detect malicious users in multilayer social networks.

Detecting coordinated and bot-like behavior in Twitter: the Jürgen Conings case

Social media platforms can play a pivotal role in shaping public opinion during times of crisis and controversy. The COVID-19 pandemic resulted in a large amount of dubious information being shared online. In Belgium, a crisis emerged during the pandemic when a soldier (Jürgen Conings) went missing with stolen weaponry after threatening politicians and virologists. This case created further division and polarization in online discussions. In this paper, we develop a methodology to study the potential of coordinated spread of incorrect information online. We combine network science and content analysis to infer and study the social network of users discussing the case, the news websites shared by those users, and their narratives. Additionally, we examined indications of bots or coordinated behavior among the users. Our findings reveal the presence of distinct communities within the discourse. Major news outlets, conspiracy theory websites, and anti-vax platforms were identified as the primary sources of (dis)information sharing. We also detected potential coordinated behavior and bot activity, indicating possible attempts to manipulate the discourse. We used the rapid semantic similarity network for the analysis of text, but our approach can be extended to the analysis of images, videos, and other types of content. These results provide insights into the role of social media in shaping public opinion during times of crisis and underscore the need for improved strategies to detect and mitigate disinformation campaigns and online discourse manipulation. Our research can aid intelligence community members in identifying and disrupting networks that spread extremist ideologies and false information, thereby promoting a more informed and resilient society.

Analisis Karakter Bahasa Alay Kaum Remaja Di Kota Makassar

In daily life, human being can never escape from language. There are two types of languages; the standard language and the non-standard languages. In Indonesia, the standard language is Bahasa Indonesia, while the non-standard languages are the languages of vernacular (local languages) and sub-cultural languages (slang, jargon, etc). Nowadays, most of teenagers use the nonstandard language to communicate among their friends, which is called slang. There is a newslang in Indonesia which is called Alay language. The Alay language is popular among teenagers since 2008 until now. The purpose of this study were (1) to know the patterns used to modify Alay Language, (2) to know the effect of using Alay language on the standard form of Indonesian language, and (3) to find out the purpose of Alay Language used by teenagers in Makassar. This research uses descriptive analysis method. The research data were taken from the conversations that contained of Alay's words, which were previously made on the internet by Facebook social network users. Other supporting data were the questionnaires. The results of this study indicated that (1) There were no standard rules in modifying Alay Language, but the most common process was Substitution Process with a frequency of 38%. (2) The use of Alay Language had both the positive and negative effects on the existence of standard language, Indonesian language. (3) One of the reasons of using the Alay language was to be called “cool”.

Does so-called “healthy” content on instagram display balanced recipes? A pilot study in relation to the risk of unhealthy eating patterns in social network users

Objectivesocial networks (SN) including Instagram have increased in popularity. However, SN-mediated content may influence eating behaviors in a negative way. This study analyzed whether Instagram content claimed as “healthy” complies with nutritional guidelines. Methodsrecipes posted in French on Instagram with the caption #healthy or similar ones were analyzed, once from February to May 2023 and again in April 2024. Health authorities’ guidelines and food pyramid inclusion criteria were used for the quantitative and qualitative analysis, respectively. Recipes were then classified as balanced, partially unbalanced or unbalanced, with the two subgroups “restrictive” and “excessive”, and according to the main protein source. Resultswe coded a total of 114 courses (2 datasets of 57 courses each). Among these, 3 were classified as balanced main courses, 45 as partially unbalanced main courses and 66 as unbalanced main courses (21 were deemed as restrictive, 21 as excessive and 24 were otherwise inadequate), with a majority of hypocaloric courses. Approximately half of the recipes were vegetarian or vegan. Discussionthese results suggest that food recipes published on Instagram as #healthy may, at times, be far from nutritional guidelines and could rather promote unbalanced eating patterns. This suggest that food-related content on SN might be insufficiently moderated and that recipes referenced as #healthy should perhaps be accompanied by warnings and preventive measures. This observation, in addition to other detrimental behaviors displayed on SN (e.g. extreme physical activity or body image pressure) may contribute to the increased incidence of eating disorders (ED) associated with problematic SN use. Alerts on this risk and accessible tools for the prevention and early detection of ED risk in SN users are urgently needed.

Classification of SPAM mail utilizing machine learning and deep learning techniques

Abstract: The Internet and social media networks usage has increased nowadays and become a prominent medium of communicating. Email is one of the professional reliable methods of communication. Automatic classifications of spam emails have become an area of interest. In order to detect spam emails, this study utilizes a dataset, including spam and non-spam emails. Various techniques are applied to obtain higher accuracy using machine learning techniques. NLP is also utilized for improvising accuracy using embeddings. For that, this work utilizes the BERT model, to achieve satisfactory detection of spam emails. Further, the results are compared with state-of-the-art methods, including, KNN, LSTM and Bi-LSTM. The results obtained by Bi-LSTM and LSTM were 97.94% and 86.02%, respectively. The presented methodology is promising in detecting spam emails due to the higher accuracy achieved.

Political social media bot detection: Unveiling cutting-edge feature selection and engineering strategies in machine learning model development

Over time, social media bots (SMBs), specifically political SMBs, have played a crucial role in influencing and spreading misinformation, manipulating public opinion, and harassing and intimidating users of online social networks (OSNs). This article aims to study previous works on the detection and analysis of political SMB activities and address critical challenges that significantly impact the effectiveness of SMB detection models. These challenges include feature engineering, feature selection (FS), and model implementation. Over 33 features were extracted from the Twibot-20 dataset, including content, user information, network, behavior, and temporal features. Various FS techniques are explored and compared to select the optimal features, comprising basic, filter, wrapper, embedded, and hybrid. The optimal features are then employed to train multiple machine-learning algorithms. To balance the dataset, the synthetic minority oversampling technique coupled with edited nearest neighbors (Smote-ENN) is used. The results showed an improvement in model performance, from an initial Area Under the Curve (AUC) of 90.40 % and accuracy of 81.60 % using the original set to a score of 99.50 % for the test set and 100 % for the training set in all used metrics. Decision Trees, Random Forest, Gradient Boosting, Adaboost, XGB, and Extra Trees emerge as the most effective for detecting political SMBs.

Digging Communicative Intentions: The Case of Crises Events

In emergency situations users of social networks convey all sorts of what have been called communicative intentions, well-known since the work of Austin (1962) and Searle (1969) as speech acts (SA). While speech acts have been the focus of close scrutiny in the philosophical and linguistic literature (see (Portner, 2018) for extended discussion), their role has been only rarely understood and exploited in processing social media content about crisis events, our focus here. Current work on communicative intentions in social media are topic-oriented, focusing on the correlation between SA and specific topics such as crisis (e.g., earthquakes) but also politics, celebrities, cooking, travel, etc. It has been observed that people globally tend to react to natural disasters with SA distinct from those used in other contexts (e.g., celebrities, which are essentially made up of comments). Here, we explore the further hypothesis of a correlation between different SA types and urgency and propose an in depth linguistic and computational analysis of communicative intentions in tweets from an urgency-oriented perspective. Indeed, SA are mostly relevant to identify intentions, desires, plans and preferences towards action and to ultimately produce a system intended to help rescue teams. Our contribution is four-fold and consists of: (1) A two-layer annotation scheme of speech acts both at the tweet and sub-tweet levels, (2) A new French dataset of about 13K tweets annotated for both urgency and SA, targeting both expected (e.g., storms) and unexpected or sudden (e.g., building collapse, explosion) events, (3) A thorough analysis of the annotations studying in particular the correlation between SA and the urgency of the message, SA and intentions to act categories (e.g., human damages), and SA and crisis types, finally, (4) A set of deep learning experiments to detect SA in crises related corpora. Our results show a strong correlation between SA and urgency annotations at both the tweet and sub-tweet levels with a particular salient correlation in the latter case, which constitutes a first important step towards SA-aware NLP-based crisis management on social media.

Самопрезентация пользователя в социальных сетях и проблема коллапса контекста

This article discusses the social problems of communication in social networks that provide a platform and tools for the formation of a virtual network identity. The features of the behavior of users of social networks and the tactics of their self-presentation when constructing a personal network profile are described. It is argued that in the process of social communication, people present different versions of themselves depending on their audience and adapting their own self-presentation to the perceived values and preferences of the interlocutors. The difficulties of online communication are described, where, unlike real interaction, the user does not have the opportunity to correlate his/ her behavior with the situational context and carry out differentiated self-presentation. The problems of blurring the boundaries of public and private in social networks and the loss of information privacy, which is a necessary condition for personal freedom and autonomy, are raised. It is argued that the disclosure of private information is an important component of maintaining social relations. It is noted that currently there is a transformation of cultural norms regulating the degree of openness-closeness and publicity-privacy. The concept of "context collapse" is introduced – a situation caused by the mixing of heterogeneous audiences with different social values, norms and expectations into one homogeneous group. The strategies are proposed to overcome the negative consequences of this situation: dividing the audience into several segments, self-censorship, forcing to avoid discussing controversial topics and adapting the user's language style, suggesting a reduction in language variability. The work was based on the methods of symbolic interactionism and I. Hoffman's theory of roles, which he used to build the dramatic theory of social interaction and self-presentation in social interaction.

Socialisation of student youth in social networks

Social networks have become a part of the daily life of students, they influence the development of views, values and identity, which can have an impact on students’ social connections, so research is an urgent task that contributes to understanding this influence. The purpose of the study was to investigate the impact of social networks on various spheres of life of modern students. In order to investigate the issues of socialisation of young people in social networks, the study used generalisation and analytical method. As a result, the importance of developing strategies aimed at strengthening the positive impact of social networks on students was highlighted. It was revealed that changing social norms and creating an atmosphere of open and decent communication in an online environment can help improve students’ socialisation in the virtual world. It was noted that the protection of privacy and the development of critical thinking are important for ensuring the security and information competence of social network users. The importance of developing media literacy and skills in verifying the truthfulness of information in the process of socialisation of students was noted. Strategies that promote the development of virtual space and improve interaction in social networks were outlined. The paper analysed aspects of positive socialisation of students in the digital age that help them solve problems and cope with the challenges of the online environment. The results of scientific research can contribute to a better understanding of how social networks affect young people, and help researchers develop programmes and strategies aimed at supporting the positive aspects of this impact and reducing possible negative consequences

Illuminating the Relationship Between Social Networking Usage and Job Performance Through the Work–Life Balance of Gen-Y Employees in Corporate Sector

The use of social media, such as social networking sites continues to grow in everyday life, along with their use in the workplace. So, a concern raised about how the personal use of social networking sites like Facebook, WhatsApp, Instagram, etc., at work affects employee job performance. Moreover, the purpose of this study is to explore the relationship between social networking usage and employee job performance through the mediating role of work–life balance. The quantitative methodology was employed in this study. Data was collected using a self-administered questionnaire adapted from a previously validated survey. The target population consisted of Gen-Y employees of the corporate sector in Chandigarh, Delhi, and Haryana (UTs/states) of India. Judgemental sampling was applied to collect data from 224 employees of the corporate sector. The study results indicated that time spent, frequency of using social networking sites has significant relationship with job performance and work–life balance of employees while work–life balance and employee job performance are significantly positively related. However, social networking use has a significant negative relationship with job performance and work–life balance. Additionally, work–life balance significantly mediates the relationship between social networking usage and job performance. Therefore, the study concluded that employees struggle to set boundaries since they cannot distinguish between work and family life due to the rise in technology and social media use. Therefore, managers and HR professionals need to consider the negative aspect of online social networking at work. Further, the implications for managers and organizations are discussed.

Exploring the impact of social networking usage on attachment styles and mental health: A study among college students in Vietnam

This study aimed to explore how attachment styles, fear of missing out symptoms, social networking usage, social connectedness, and time spent on social networking relate to each other. This study involved 310 randomly selected college students in Vietnam between March and April 2023. Participants completed an online questionnaire, providing demographic information and responses from three scales: The Fear of Missing Out Scale (FOMOs), Experiences in Close Relationships - Revised - General Short Form (ECR-R-GRF), The Social Connectedness Scale, and The Social Networking Time Use Scale (SONTUS). The research sought to examine how attachment style, fear of missing out, social networking time use, social connection, and time spent on social networking interact. The study found that (a) fear of missing out significantly and positively influences attachment styles, (b) time spent on social networking sites positively impacts attachment style, and (c) social networking time use affects attachment style by serving as a mediator through fear of missing out. Additionally, the time individuals spend on social networking platforms influences the relationship between fear of missing out and attachment styles. This study contributes to the understanding of how social network usage affects mental health, clarifies social network attachment demand theory, and offers guidance for families, schools, and psychological organizations to promote social integration and well-being among students.

Analysis of Image Interaction and Social Motivation in Virtual Communities

The purpose of this paper is to explore the relationship between image interaction and social motivation in virtual communities, and take Instagram and TikTok as case studies. First, the conceptual characteristics of virtual communities and the defined forms of image interaction are analysed, and then the classification of social motivation and its impact on user behaviour are explored, as well as the role of image interaction in shaping the social ecosystem, the social atmosphere and the user's social network. In Instagram, image interaction behaviours were found to be positively correlated with user engagement, increasing users' exposure and social influence. In TikTok, on the other hand, social identity based on image interaction was closely related to user satisfaction, promoting user loyalty and stickiness to the platform. In summary, image interaction, as an important form of social behaviour in virtual communities, has a positive impact on the development of the community and the social experience of users, but it also needs to pay attention to issues such as information security and content regulation.