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The purpose of this research is to obtain an overview of the communication and interaction patterns occurring within the Converse fan group, Converse Head Indonesia, using the critical-constructivism paradigm. This study employs a digital ethnography strategy with a qualitative approach, utilizing in-depth interviews and observations of the group's activities on their Facebook fan page. Through interviews with six informants selected purposively and using the snowball technique, it was found that social interactions among CHI members are grouped into nine dimensional categories based on interview questions. The results reveal that members join the network to socialize and gain access to the latest Converse products, including benefits, price information, and where to obtain them. From the interactions among CHI members, exclusivity, value standards, appearance standards, and ethics were also identified. Lastly, it was found that the interactions within the CHI group predominantly emphasize economic values. [ABSTRACT FROM AUTHOR]

Predicting what becomes popular on social media is crucial because it helps us understand future topics and public interests based on massive social data. Previous studies mainly focused on picking specific features and checking past statistic numbers, ignoring the hidden impact of messages passing along the complex relationships among different entities. People talk and connect with others on social media; thus, it is essential to consider how information spreads when studying social media networks. This work proposes a multi-layer temporal graph neural network (GNN) framework for predicting what will be popular on social media networks. This framework takes into account the way information spreads among different entities. The proposed method involves multi-layer relations and temporal information within a sequence of social media network snapshots. It learns the temporal representations of target entities in each snapshot and predicts how the popularity of a particular entity will change in future snapshots. The proposed method is evaluated with real-world data across four popularity trend prediction tasks. The experimental results prove that the proposed method performs better than various baselines, including traditional machine learning regression approaches, prior methods for popularity trend prediction, and other GNN models. [ABSTRACT FROM AUTHOR]

Abstract Predicting what becomes popular on social media is crucial because it helps us understand future topics and public interests based on massive social data. Previous studies mainly focused on picking specific features and checking past statistic numbers, ignoring the hidden impact of messages passing along the complex relationships among different entities. People talk and connect with others on social media; thus, it is essential to consider how information spreads when studying social media networks. This work proposes a multi-layer temporal graph neural network (GNN) framework for predicting what will be popular on social media networks. This framework takes into account the way information spreads among different entities. The proposed method involves multi-layer relations and temporal information within a sequence of social media network snapshots. It learns the temporal representations of target entities in each snapshot and predicts how the popularity of a particular entity will change in future snapshots. The proposed method is evaluated with real-world data across four popularity trend prediction tasks. The experimental results prove that the proposed method performs better than various baselines, including traditional machine learning regression approaches, prior methods for popularity trend prediction, and other GNN models.

Wireless sensor networks (WSNs) have attracted a lot of interest due to their enormous potential for both military and civilian uses. Worm attacks can quickly target WSNs because of the network's weak security. The worm can spread throughout the network by interacting with a single unsafe node. Moreover, the analysis of worm spread in WSNs can benefit from the use of mathematical epidemic models. We suggest a five-compartment model to characterize the mechanisms of worm proliferation with respect to time in WSN. Taking into account the ZZ transform convoluted with the Atangana-Baleanu-Caputo (ABC) fractional derivative operator, we employ it to analyze the characteristics and applications of the ZZ transformation using the Mittag-Leffler kernel. Moreover, we construct a new algorithm for the homotopy perturbation method (HPM) in conjunction with the ZZ transform technique to generate analytical solutions for the worm transmission model. Also, we address the qualitative aspects such as positivity, boundness, worm-free state, endemic state, basic reproduction number (R0) and worm-free equilibrium stability. Furthermore, we prove that the virus rate in sensor nodes is extinct if R01. In addition, we develop analytical findings to evaluate the series of solutions. Furthermore, a detailed statistical analysis is conducted to verify the nonlinear dynamics of the system by verifying the 0−1 test to determine whether uncertainty exists using approximation entropy and the C0 data. An extensive analysis of the vaccination class with respect to the transmitting class as well as the susceptible class is being used to investigate the effects of stepping up precautions on WP in WSN. Moreover, the modeling of the WSN revealed that reducing the fractional-order from 1 requires that the recommended approach be implemented at the highest rate so that there is no long-lasting immunization; instead, nodes remain briefly defensive before becoming vulnerable to future worm attacks.

Work-related social media networks (SMNs) like LinkedIn introduce novel networking opportunities and features that promise to help individuals establish, extend, and maintain social capital (SC). Typically, work-related SMNs offer access to advanced networking features exclusively to premium users in order to encourage basic users to become paying members. Yet little is known about whether access to these advanced networking features has a causal impact on the accumulation of SC. To close this research gap, we conducted a randomized field experiment and recruited 215 freelancers in a freemium, work-related SMN. Of these recruited participants, more than 70 received a randomly assigned voucher for a free 12- month premium membership. We observe that individuals do not necessarily accumulate more SC from their ability to access advanced networking features, as the treated freelancers did not automatically change their online networking engagement. Those features only reveal their full utility if individuals are motivated to proactively engage in networking. We found that freelancers who had access to advanced networking features increased their SC by 4.609% for each unit increase on the strategic networking behavior scale. We confirmed this finding in another study utilizing a second, individual-level panel dataset covering 52,392 freelancers. We also investigated the dynamics that active vs. passive features play in SC accumulation. Based on these findings, we introduce the "theory of purposeful feature utilization": essentially, individuals must not only possess an efficacious "networking weapon"--they also need the intent to "shoot" it. [ABSTRACT FROM AUTHOR]

This study investigates the provincial variations in self-expression among millions of Sina Weibo users within China, alongside the influence of ecological factors. Online social networking (OSN) platforms enable individuals to express themselves publicly through the use of personal Bios sections. In order to explore regional differences in self-expression, we constructed a self-expression index, grounded in the percentage of users who provided self-descriptions. To do this, we analyzed the Bios information of over 13 million users, obtaining provincial self-expression scores. Our results demonstrated that the self-expression index has face, convergent, and discriminant validity. Moreover, we discovered that several ecological factors exhibited a considerable impact on self-expression among Chinese Sina Weibo users. This research offers valuable insights into the regional variations of self-expression in China and the role of ecological factors in shaping such tendencies.

Copyright of Journal of Agricultural Economics & Social Sciences is the property of Egyptian National Agricultural Library (ENAL) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: There are many ways to raise public health awareness and recently, Social media networks (SMN) have played a big role in it. The study aims to assess SMN use for acquiring health care information and publics' perception of virtual encounters with pharmacists for health- related purposes; particularly in times of a pandemic crisis. Methods: A bi-phasic cross-sectional survey was distributed on 2017 and again during COVID-19 pandemic. both phases (phase 1) and (phase 2) respectively, were compared and analyzed. Results: On phase 1 and phase 2 we collected 566 and 409 respondents respectively. Most used SMNs in both phases were Twitter, Snapchat and Instagram. In both phases respondents preferred acquiring health care information by going physically to the hospital or pharmacy, however virtual interactions were only accepted by (14%) in phase 1 compared to (36%) in phase 2. Additionally, while only 15% said they would “definitely” reach a pharmacist virtually in phase 1, 50% said they would in phase 2. In phase 2, 90% follow the Saudi Ministry of Health website, while 41% follow verified doctors for acquiring medical health-care information. Virtual contact with a pharmacist was mostly (76%) for medical consultation. Conclusion: This study is the first to shed light on society's acceptance and perception of an innovative educational tool taken by the pharmacist through social media and virtual portals among the Saudi population in Jeddah. There's an opportunity for Medication Therapy Management (MTM), CMR, medication refill, and disease follow up that the pharmacist can take the lead in, if properly implemented. Future studies should look into safe and reliable ways to make use of SMNs as well as virtual tools to expand public health awareness especially in a highly technology dependent society.

To discover and identify the influential nodes in any complex network has been an important issue. It is a significant factor in order to control over the network. Through control on a network, any information can be spread and stopped in a short span of time. Both targets can be achieved, since network of information can be extended and as well destroyed. So, information spread and community formation have become one of the most crucial issues in the world of SNA (Social Network Analysis). In this work, the complex network of twitter social network has been formalized and results are analyzed. For this purpose, different network metrics have been utilized. Visualization of the network is provided in its original form and then filter out (different percentages) from the network to eliminate the less impacting nodes and edges for better analysis. This network is analyzed according to different centrality measures, like edge-betweenness, betweenness centrality, closeness centrality and eigenvector centrality. Influential nodes are detected and their impact is observed on the network. The communities are analyzed in terms of network coverage considering the Minimum Spanning Tree, shortest path distribution and network diameter. It is found that these are the very effective ways to find influential and central nodes from such big social networks like Facebook, Instagram, Twitter, LinkedIn, etc. [ABSTRACT FROM AUTHOR]

In the world of social media, people are free to choose names based on their preferences, which may potentially reflect certain levels of uniqueness. In this study, we have attempted to explore the possibility of applying the ecological theory of individualism/collectivism in the context of social media. We, thus, examined provincial variations in the uniqueness of nicknames among more than 13 million Sina Weibo users. Initially, the nickname uniqueness indicator was set at the provincial level. It was found that the uniqueness of nicknames was the highest in provinces with temperate climates, for example Guangdong, and the lowest in provinces with demanding climate, such as Ningxia. Regression analysis results partially supported that inhabitants in provinces with temperate climate were more likely to use unique nicknames on social media compared to those from harsh climate. This finding is significant in terms of ecology.

There are many ways to raise public health awareness and recently, Social media networks (SMN) have played a big role in it. The study aims to assess SMN use for acquiring health care information and publics' perception of virtual encounters with pharmacists for health- related purposes; particularly in times of a pandemic crisis. A bi-phasic cross-sectional survey was distributed on 2017 and again during COVID-19 pandemic. both phases (phase 1) and (phase 2) respectively, were compared and analyzed. On phase 1 and phase 2 we collected 566 and 409 respondents respectively. Most used SMNs in both phases were Twitter, Snapchat and Instagram. In both phases respondents preferred acquiring health care information by going physically to the hospital or pharmacy, however virtual interactions were only accepted by (14%) in phase 1 compared to (36%) in phase 2. Additionally, while only 15% said they would "definitely" reach a pharmacist virtually in phase 1, 50% said they would in phase 2. In phase 2, 90% follow the Saudi Ministry of Health website, while 41% follow verified doctors for acquiring medical health-care information. Virtual contact with a pharmacist was mostly (76%) for medical consultation. This study is the first to shed light on society's acceptance and perception of an innovative educational tool taken by the pharmacist through social media and virtual portals among the Saudi population in Jeddah. There's an opportunity for Medication Therapy Management (MTM), CMR, medication refill, and disease follow up that the pharmacist can take the lead in, if properly implemented. Future studies should look into safe and reliable ways to make use of SMNs as well as virtual tools to expand public health awareness especially in a highly technology dependent society. [ABSTRACT FROM AUTHOR]

In the world of social media, people are free to choose names based on their preferences, which may potentially reflect certain levels of uniqueness. In this study, we have attempted to explore the possibility of applying the ecological theory of individualism/collectivism in the context of social media. We, thus, examined provincial variations in the uniqueness of nicknames among more than 13 million Sina Weibo users. Initially, the nickname uniqueness indicator was set at the provincial level. It was found that the uniqueness of nicknames was the highest in provinces with temperate climates, for example Guangdong, and the lowest in provinces with demanding climate, such as Ningxia. Regression analysis results partially supported that inhabitants in provinces with temperate climate were more likely to use unique nicknames on social media compared to those from harsh climate. This finding is significant in terms of ecology. [ABSTRACT FROM AUTHOR]

This study focused on how students of the Faculty of Mass Communication use the social media networks, the differences between male and female students and the differences between the study levels in terms of using social media in academic communication and for the arrangement of various activities and events in their academic environment. The study aimed to identify the motives behind the students' use of social media, the academic activities they follow and arrange for, as well as the way they present themselves through these media. The survey method was adopted, using a questionnaire as the data collection tool. The study revealed that facebook was ranked first among the respondents, followed by the WhatsApp application. The students’ motives for using social media varied; the first motive was to communicate with their classmates in the college. The most frequently discussed topic was to know the time and date of classes and exams, followed by issues of interest to the whole batch. The results also revealed partial acceptance of the two hypotheses; there were statistically significant differences between the use of social media networks and the variable of study level. In the second hypothesis, there were also statistically significant differences between the use of social media networks and the gender variable.

Project communication and collaboration is a crucial activity that makes or breaks a project. And thus the territorial development today cannot do without a new generation of projects that embody the real needs of residents and visitors. Excellent technical skills and intuition may be insufficient to drive performance if not accompanied by appropriate levels of interpersonal interaction within project groups. Evidently that Global participation platforms and social networks like Facebook, LinkedIn and others and a mass of local blogs and web communities are an important source for next generation of project managers. How these new technologies will impact organizations, however, is not entirely clear. Therefore, it represents an important field for information systems research. This paper presents a survey study of the intrusion of social media in project management. We investigate some basic questions such as the possibilities, dangers, and limits (e. g. legal or ethical) of using social media services in project management. Also we analyzed the existed approaches that can serve as a start point for the successful and efficient implementation of such services. Ours results indicate that social media analysis can serve as an improvement tool for project management of territory development to reduce the product development time and associated cost through the implementation of social network analysis

The exponential rise in internet technology and online social media networks have revitalized human-being to connect and socialize globally irrespective of geographical and any demographic boundaries. Additionally, it has revitalized business communities to reach target audiences through social media networks. However, as parallel adverse up-surge the ever-increasing presence of malicious users or spam has altered predominant intend of such social media network by propagating biased contents, malicious contents and fraud acts. Avoiding and neutralizing such malefic users on social media network has remained a critical challenge due to gigantically large size and user's diversity such as Facebook, Twitter, and LinkedIn etc. Though exploiting certain user's behavior and content types can help identifying malicious users, majority of the existing methods are limited due to confined parametric assessment, and inferior classification approaches. With intend to provide spam profile detection system in this paper a novel heterogeneous ensemble-based method is developed. The proposed model exploits user profile features, user's activity features, location features and content features to perform spam user profile detection. To ensure optimality of computational significances, we applied multi-phased feature selection method employing WilcoxonRank Sum test, Significant Predictor test, and Pearson Correlation test, which assured retaining optimal feature sets for further classification. Subsequently, applying an array of machine learning methods, including Logistic regression, decision tree, Support Vector Machine variants with Linear, Polynomial and RBF kernels, Least Square SVM with linear, polynomial and RBF kernels, ANN with different kernels, etc we constituted a robust ensemble model for spam user profile classification. Simulations revealed that the proposed ensemble classification model achieves accuracy and F-score higher than 98%, which is the highest amongst major works done so far. It affirms suitability and robustness of the proposed model for real time spam profile detection and classification on social media platforms. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Educational Psychological Studies (EPS) is the property of Refaad for Studies, Research & Development and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)