جستجوی مقالات مرتبط با کلیدواژه "social networks" در نشریات گروه "فنی و مهندسی"

Therefore, project-based learning through social networks has been the theoretical background of this study. The statistical population of this research was the 10th grade students of the secondary school of Ayatollah Amoli Madrasah for boys and girls in Amol city in the academic year of 1401-1400. Based on the J-power sample size calculation software, 24 people were selected as a statistical sample by purposive sampling method and assigned. They were randomly selected (lottery) and registered on the Edmodo website with the guidance of a teacher-researcher in six small groups. After receiving the guidance and basic educational files, they were first introduced to the principles of story writing in English. Then, in two stages, they were asked to first write a short story in collaboration with their peers through the Edmodo site, and after finishing the group activity, each student was required to write a story alone. The written tests were scored by two evaluators (researchers) based on the model (Alderson & Tankó, 2010) that ranks the writings on a scale of 1 to 6. Pearson's correlation was performed to check the reliability of the scores between the raters and the results for the individual test (Pearson r = 0.81 p < .05) and for the group test (Pearson r = 0.88 p < .05) showed a significant agreement between the two raters. Comparing the scores of individual stories showed that after completing online group work through the site, students showed significant improvement in their writing skills, and this principle emphasizes the effectiveness of online collaborative activities through social media. Participants were also asked to answer an open-ended questionnaire in two stages before and after the online project. The construct validity of the questionnaire was guaranteed through expert review and exploratory factor analysis. Also, Cronbach's alpha analysis was used as a measure of internal homogeneity of the scale and the estimated reliability index of the questionnaire was obtained (α=90). According to the results, most of the students showed a positive attitude towards the online cooperative learning project.

Community structure is a common and important feature in many complex networks, including bipartite networks. In recent years, community detection has received attention in many fields and many methods have been proposed for this purpose, but the heavy consumption of time in some methods limits their use in large-scale networks. There are methods with lower time complexity, but they are mostly non-deterministic, which greatly reduces their applicability in the real world. The usual approach that is adopted to community detection in bipartite networks is to first construct a unipartite projection of the network and then communities detect in that projection using methods related to unipartite networks, but these projections inherently lose information. In this paper, based on the bipartite modularity measure that quantifies the strength of partitions in bipartite networks and using the HellRank centrality measure, a quick and deterministic method for community detection from bipartite networks directly and without need to projection, proposed. The proposed method is inspired by the voting process in election activities in the social society and simulates it.

In the modern world, social network analysis has various applications including social network management, market trend analysis, identifying influential individuals, supporters, and enhancing the performance of descriptive systems. One of the existing issues in the field of information diffusion is examining the extent of dissemination in such systems. In large-scale networks, computational complexity has increased, and one of the methods to cope with this problem is using evolutionary algorithms. In this paper, a new method is proposed to increase the accuracy of predicting information diffusion in social networks by combining the colonial competition algorithm and the centrality algorithm. In the proposed method, the centrality of nodes is first calculated for all nodes in the network. Then, based on the distance between users in the social network and the speed of information dissemination among users using the colonial competition algorithm, the optimal path for dissemination is determined, and information diffusion prediction is made accordingly. In the colonial competition algorithm, the optimal path for information dissemination in the network is identified based on how the colonies move towards the colonizer. In this paper, four different datasets were examined. The results show that the proposed method leads to better results. The algorithm's performance is evaluated based on accuracy, NMI, and error criteria, showing desirable results.

In the new paradigm of innovation, the definition of innovation has changed from emphasizing only economic goals to social goals, adding a social element to innovation has brought social innovation which considers innovation and the application of new ideas in the direction of value creation aimed at responding to social needs and is trying to attract social participation. On the other hand, in the conditions of today's world, cultural changes have made people prefer to have extensive communication with the world while being alone. With the advent of social networks and the Internet, the social communication system has faced important developments, and the tendency of people to these networks is growing all over the world with the aim of creating social relationships. The content produced by users in these networks is a suitable source for investigating social phenomena such as social innovations. Today, social networks play a significant role in sharing ideas and opinions of users, interacting on various issues and producing knowledge based on mutual communication and free circulation of innovations, and as a result, developing and supporting social innovations in order to solve social issues and problems. The present research, which has been prepared by using library and internet resources and compiled in an analytical-review form, tries to investigate the role of social networks in the development of social innovations while examining the concept of social innovation and the benefits of social networks.

Factors Influencing Unethical Behavior in Social Networks based on Theory of Planned BehaviorAbstract The expanding use of Social Networking Sites (SNS) and their flexible functionalities allow individuals to use these sites in ethical and unethical ways. The objective of this study is to identify the influential factors that have an impact on individuals’ ethical decision-making process based on Theory of Planned Behavior. For this purpose, four different scenario-based questionnaires were designed to enable the survey to investigate factors affecting individuals’ behavior in different situation. With 660 returned questionnaires in hand, the collected data were analyzed using the Partial Least Squares-Structural Equation Modeling (PLS-SEM) technique. The results showed attitude to be the most influential factor, followed by perceived behavioral control and subjective. Finally, the role of different scenarios was studied and it is shown different effects on the relationships based on different scenarios.Keywords: Unethical Behaviors, Theory of Planned Behavior, Social Networks, Ethic in Information Technology

This research was carried out with the aim of designing and explaining the effectiveness of social media advertising in the domestic garment industry. This research is practical in terms of purpose. The statistical population was a group of experts consisting of academic professors and prominent consultants in the field of marketing based on social networks, as well as domestic garment industry activists. The main data collection tool was in-depth and semi-structured interviews. 12 experts were selected by purposeful sampling. After conducting 9 interviews, the researcher reached theoretical saturation and the process of conducting interviews was stopped. In order to design the model, the qualitative method of Foundation Data Theory was used with MAXQDA software. A total of 123 initial concepts were extracted, after examining and putting them together and removing duplicate concepts, 40 final concepts were identified. The result of this process was the formation of 13 categories that formed the basis of the research conceptual model. In the following, the dimensions of the paradigm model include the main and central category (internal desire to explore about advertising), causal conditions (general interest in using social networks, producing attractive content, observing ethical principles, using the power of the brand to gain the trust of users), context (society's tendency to opt out of social networks), environmental variables (information and communication technology infrastructures in the country), strategic categories (electronic word-of-mouth advertising, making it possible for users to exchange opinions under the advertisement, using reliable and specialized media) Introduction became.

Social networks have become one of the most important decision-making factors in organizations due to the speed of publishing events and the large amount of information. For this reason, they are one of the most important factors in the decision-making process of information validity. The accuracy, reliability and value of the information are clarified by these networks. For this purpose, it is possible to check the validity of information with the features of these networks at the three levels of user, content and event. Checking the user level is the most reliable level in this field, because a valid user usually publishes valid content. Despite the importance of this topic and the various researches conducted in this field, important components in the process of evaluating the validity of social network information have received less attention. Hence, this research identifies, collects and examines the related components with the narrative method that it does on 30 important and original articles in this field. Usually, the articles in this field are comparable from three dimensions to the description of credit analysis approaches, content topic detection, feature selection methods. Therefore, these dimensions have been investigated and divided. In the end, an initial framework was presented focusing on evaluating the credibility of users as information sources. This article is a suitable guide for calculating the amount of credit of users in the decision-making process.