درخت حوزه‌های تخصصی

INTRODUCTION: Women's empowerment has been introduced as a mechanism to improve the quality of women's work and personal life, one of the basic principles of protecting human rights and eradicating poverty. This study aimed to investigate the effect of social capital, social support, and Internet use on the empowerment of working women with a mediating role in socioeconomic status. METHODS: In this descriptive survey study, 4,000 women were studied working in the RCS of Tehran province, Iran. About 384 samples were selected due to Cochran's formula by a simple random method. The required data were collected using some questionnaires, namely the Social Capital (Hoff and Heisman, 2009), subjective assessment of the socioeconomic status (Nabavi et al., 2007), Social Support (Sherburne and Stewart, 1991), the researcher-made questionnaire of Internet Use and Women’s Empowerment. FINDINGS: The results showed that social capital, social support, and the use of the Internet had a direct impact on the empowerment of women, and socioeconomic status played a mediating role in the relationship between social capital and empowerment. It was also found that socioeconomic status played a mediating role in the relationship between social support and the empowerment of working women. CONCLUSION: According to the results, attention to the components of social capital, social support, and the use of the Internet and cyberspace provided the basis for the empowerment of working women, while in this process the key role of women's socioeconomic status should not be overlooked. However, managers' attention to strengthening organizational trust and women's participation in decision-making, providing conditions for good communication, training, and promoting women's media literacy, would increase the socioeconomic status proportionately and improve women's empowerment.

INTRODUCTION: Improving the social capital of the Red Crescent Society can play an effective role in crisis management also control and increase productivity in responding to incidents. This research aimed to explain the role of mass and social media in the development of the social capital of the Iranian Red Crescent Society. METHODS: This practical research was conducted using a mixture of qualitative and quantitative research methods. The statistical population of the research in the qualitative part included 21 executive managers and pundits, who were selected using a purposeful and snowball sampling method. In the quantitative part, a total of 2,815 volunteer members of the Red Crescent Society formed the population, who were chosen by stratified random sampling method. The statistical data of the qualitative part were collected by holding semi-structured interviews, while those of the quantitative part were gathered using a questionnaire having content validity as well as the study of organizational documents. Data analysis in the qualitative part was performed through the thematic analysis method and in the quantitative part through the frequency and comparison of statistical data. FINDINGS: The findings showed that the effective role of the media should not be neglected. It was revealed that mass and social media played a role in the development of the social capital of the Red Crescent Society through three important components, namely news and information, mass education, and accreditation. CONCLUSION: According to the results of the current research, the Red Crescent Society can boost the productivity and effectiveness of humanitarian and public benefits programs and activities by understanding the very effective role of communication media and improving the level of cooperation and altruism culture in society with the help of mass media members, activists, and celebrities of social networks.

Internet-based social channels have turned into an important information repository for many people to get an idea about current trends and events happening around the world. As a result of Abundance of raw information on these social media platforms, it has become a crucial platform for businesses and individuals to make decisions based on social media analytics. The ever-expanding volume of online data available on the global network necessitates the use of specialized techniques and methods to effectively analyse and utilize this vast amount of information. This study's objective is to comprehend the textual information at the Lexical and Semantic level and to extract sentiments from this information in the most accurate way possible. To achieve this, the paper proposes to cluster semantically related words by evaluating their lexical similarity with respect to feature and sequence vectors. The proposed method utilizes Natural Language Processing, semantic and lexical clustering and hybrid C4.5 algorithm to extract six subcategories of emotions over three classes of sentiments based on word-based analysis of text. The proposed approach has yielded superior results with seven existing approaches in terms of parametric values, with an accuracy of 0.96, precision of 0.92, sensitivity of 0.94, and an f1-score of 0.92.