Social Networks

The outbreak of Coronavirus disease, 2019 (COVID-19), started in late 2019 and developed into a pandemic by March 2020 and has become a global problem. Following the global outbreak and coronavirus spreading around the world, the WHO reported a statement on January 11, 2020, announcing the new Coronavirus outbreak as the sixth significant public health emergency in the world. In the stressful situation caused by the coronavirus epidemic, many jokes and Humor about this disease were distributed on social networks. In these circumstances, the question arises: Why do some people continue to make jokes about it, despite the mass perception of the coronavirus epidemic? The present research method was qualitative and Strauss and Corbin's version of the grounded theory was used. Participants were included the Telegram Social Network Comic Channel “https://t.me/s/jokcom” Members, which had more than 2879 members and those on Instagram and Twitter members who liked the corona content to the jokes about the covid-19 pandemic inside Iran. Based on the result, we found the effects and consequences of corona jokes. There was several factors involved in shaping the phenomenon of covids jokes. Joke and Humor are like a double-edged sword; in some situation, can be both harmful and helpful.

The main aim of this study was to identify and analyze the effect of social capital on entrepreneurial activities of Fars province specialized companies in Iran to improve their status. This descriptive research was accomplished using survey and required data was collected through questionnaire. Stratified random sampling was used and sample size was estimated to be 380 rural women. The validity and reliability of the questionnaire was confirmed by the viewpoints of professors as well as conducting a pilot study, calculating the Cronbach's alpha coefficient, respectively. Based on the results of path analysis, social capital and social networks activities had a direct and significant effect on entrepreneurial activities. Business plan writing skill, creativity, entrepreneurial motivation, family communication, supportive policies, educational-counseling policies, and business environments also had a direct and significant effect on social capital. Creativity, entrepreneurial motivation, family communication and educational policies had a direct and significant effect on social networking activities, as well. Enhancing the ability of entrepreneurs to start and continue entrepreneurial activities, paying attention to the role of social networks and their interacting as well as considering social capital as a link between business networks by identifying entrepreneurial opportunities and providing resources and facilities are essential in this regard.

A social network consists of individuals and the relationships between them, which often influence each other. This influence can propagate behaviors or ideas through the network, a phenomenon known as influence propagation. This concept is crucial in applications like advertising, marketing, and public health. The influence maximization (IM) problem aims to identify key individuals in a social network who, when influenced, can maximize the spread of a behavior or idea. Given the NP-hard nature of IM, non-exact algorithms, especially metaheuristics, are commonly used. However, traditional metaheuristics like the variable neighborhood search (VNS) struggle with large networks due to vast solution spaces. This paper introduces DQVNS (Deep Q-learning Variable Neighborhood Search), which integrates VNS with deep reinforcement learning (DRL) to enhance neighborhood structure determination in VNS. By using DQVNS, we aim to achieve performance similar to population-based algorithms and utilize the information created step by step during the algorithm's execution. This adaptive approach helps the VNS algorithm choose the most suitable neighborhood structure for each situation and find better solutions for the IM problem. Our method significantly outperforms existing metaheuristics and IM-specific algorithms. DQVNS achieves a 63% improvement over population-based algorithms on various datasets. The results of implementation on different real-world social networks of varying sizes demonstrate the superiority of this algorithm compared to existing metaheuristic, IM-specific algorithms, and network-specific measures.

Detecting fake news on social media platforms remains a significant challenge due to the dynamic nature of these networks, evolving user-news relationships, the difficulty in distinguishing real from fake information, and the use of advanced generative models to create fake content. In this study, we propose a novel approach, the Dynamic Graph Attention Network (DynGAT), for effective fake news detection. The DynGAT model utilizes the dynamic graph structure of social networks to capture the evolving interactions between users and news sources. It includes a graph construction module that updates the graph based on temporal data and a graph attention module that assigns importance to nodes and edges within the graph. The model applies attention mechanisms to prioritize critical interactions and uses deep learning techniques to classify news articles as real or fake. Experimental results on the TweepFake dataset (20,712 samples) show that DynGAT achieves 95% accuracy, outperforming existing methods such as Static GNN (87%), Transformer-based models (91%), and Hybrid models (89%). The model also demonstrates improvements in precision, recall, and F1 score. This work contributes to the ongoing efforts to combat misinformation and promote reliable information on social media platforms.

Background: A key claim and fundamental assumption of feminist thought is that many traits commonly perceived as female are not inherent or genetically determined, but are instead products of historical and social conditioning. This perspective argues that even in contemporary societies, persistent anti-female stereotypes limit women’s ability to fully exercise their rights. For instance, the historical exclusion of women from political life, potentially driven by male power dynamics, has fostered the misconception that women lack interest in political engagement.Aims: This research seeks to investigate this hypothesis: does Internet-based social networks help to increase women's political participation?Methodology: Employing a qualitative approach, this study synthesizes data from diverse sources– including case study of Clubhouse, and peer-reviewed academic literature– to explore to what extent are women interested in political action under free and equal conditions? To this end, we selected the Clubhouse platform. We then monitored and analyzed women’s behavior before and after a sensitive national political event, the 2021 (1400 AH) presidential election.Findings: The findings indicated that, contrary to our initial hypothesis, the Clubhouse platform’s open environment did not significantly enhance women’s political participation or stimulate greater interest in political topics. Women largely remained engaged with content related to entertainment, family life, cooking, and music.Conclusions: This viewpoint aligns with the tendency to view gender-related issues as stemming from inherent, natural differences rather than socio-historical constructs.

Since the Corona pandemic, the discussion of virtual education has been widely discussed and has entered all fields, including physical education. In the post-Corona era, this type of education has maintained and even expanded its specific audience, so that we are currently witnessing the growth of virtual sports clubs on social networks. Therefore, in order to understand the target market of this profession, sociological information from this group seems essential. Therefore, the purpose of this study is to study the demographic variables of virtual sports clubs on social networks. The statistical population of this study was the participants of a virtual women's club in one of the social networks, numbering 800 people, of which 233 were selected by simple random sampling method and their demographic information was obtained through an online form and the data received was analyzed using a descriptive-analytical method. This study showed that exercising in the virtual club was satisfactory from the participants' perspective, considering that the vast majority of them achieved their goals. Among the participants in the virtual club, the percentage of married people was much higher than single people, and young people aged 20 to 40 had the largest number of participants. The percentage of unemployed people was significantly higher than employed people. There was no significant difference in the type of online or offline use of sessions, but in a two-dimensional analysis of these variables with the variable of online or offline use of virtual sports, it was found that single people were more interested in the offline method and the flexibility of using class time than married people, people with a master's degree were more interested in other educational levels than other educational levels, and people aged 40 to 60 were more interested in the offline method and the flexibility of using class time than other age groups.

The primary issue of this research was to investigate the relationship between artificial intelligence (AI) and user behavior patterns on social networks, the impact of AI on voting behavior, and the connection between the use of online data analysis and students' decision-making in presidential elections. This question arises while considering the influence of AI on user behavior patterns and decision-making in presidential elections. The objective of this study was to evaluate the impact of AI variables and online data on user behavior and decision-making in various contexts. This re-search examined the study hypothesis to better understand how behavior and decision-making are shaped in the digital age and the role of modern technologies in these processes. To conduct this research, a survey method and available library resources were utilized. The study used existing sources and collected necessary data from the target population, namely students at universities in Tehran, and employed descriptive and inferential statistics for data analysis. The findings of this research indicate that demographic variables and AI play a significant role in user behavior and de-cision-making, and the use of online data analysis can influence students' decision-making in presi-dential elections. Additionally, AI can be effective in detecting user behavior patterns on social networks and influencing voting behavior. These results can be useful for policymakers, election officials, and social activists in designing effective strategies to monitor user behavior and decision-making in the digital age, contributing to a better understanding of AI's role in shaping behavior and decision-making in society.

Objective: The present study aimed to investigate the moderating role of interpersonal mindfulness in the relationship between social media use and marital satisfaction. Methods: This study was descriptive and correlational in terms of its objectives and the manner of data collection. The sample population consisted of married individuals who used social media and resided in Tehran, from whom 160 people were selected using a convenience sampling method. In this research, the Interpersonal Mindfulness Scale by Steven Puchter et al. (2018), the Social Networks Scale by Mojdeh et al. (2014), and the Marital Satisfaction Scale by Enrich were used. To evaluate the structural model of the study, the four-stage structural assessment model proposed by Hair et al. (2017) was utilized. Findings: The results indicated a significant positive correlation between interpersonal mindfulness and marital satisfaction, and a significant negative correlation between the extent of social media use and marital satisfaction (P = 0.05). Conclusion: Ultimately, the findings demonstrated that the moderating role of interpersonal mindfulness in the relationship between social media use and marital satisfaction is significant.

Objective: The primary aim of this study was to integrate the role of social networks and subjective norms into the theoretical framework of the investment model of relationships to predict marital stability among divorce-seeking couples in the city of Ilam. Methods: This research employed a correlational design. The statistical population included all divorce-seeking couples in Ilam during 2019–2020 who visited family courts, counseling centers, and divorce registry offices. A sample of 160 participants was selected using convenience sampling. Data collection utilized the following instruments: the Marital Instability Index (Edwards et al., 1987), the Social Networks in Marital Relationships Scale (researcher-developed, 2019), the Subjective Norms in Marital Relationships Scale (researcher-developed, 2019), and Rusbult’s Investment Model Scale (1980). Data analysis was conducted using path analysis via AMOS version 23. Findings: The findings indicated that the tested model exhibited acceptable fit. Significant positive correlations were found between marital satisfaction and marital commitment, while significant negative correlations were observed between the quality of alternative relationships and marital commitment. Additionally, relationship investment and marital commitment were significantly positively correlated. A significant relationship was identified between marital commitment and marital instability at the level of p<0.05. However, no significant relationship was observed between social networks and marital commitment or between subjective norms in marital relationships and marital commitment. Furthermore, the results demonstrated that marital commitment mediated the relationship between satisfaction with the relationship and marital instability (p≤.05), the quality of alternative relationships and marital instability (p≤.05), relationship investment and marital instability (p≤.01), and subjective norms and marital instability (p≤.05). However, marital commitment did not significantly mediate the relationship between the influence of social networks in marital relationships and marital instability. Conclusion: Based on the findings and the role of subjective norms and social networks within Rusbult’s investment model, the results of this study can be utilized in counseling centers to enhance and prevent the instability of marital relationships.