Social Network Content Research Articles

МІЖНАРОДНИЙ РИНОК ПРАЦІ ТА СОЦІАЛЬНО-ТРУДОВІ ВІДНОСИНИ В УМОВАХ ЦИФРОВІЗАЦІЇ ЕКОНОМІКИ

The article is devoted to the study of the international labor market and social and labor relations in the conditions of the digitalization of the economy. The role of digital technologies in the process of modern transformation of the sphere of labor and social and labor relations of the world economy is determined. It was revealed that this transformation is manifested in a decrease in the economic activity of the population, the spread of flexible forms of employment, which allow more effective adaptation to the stricter demands of demand in the rapidly changing labor market. The formation of the platform infrastructure of the labor market, which contributes to better access to vacancies, is indicated. At the same time, there is an increase in requirements for applicants for these vacancies, in particular, today, in addition to traditional professional requirements, the possession of technical skills and competences in content and audience management in social networks are added. It is emphasized that in modern conditions of digitalization of the economy, the responsibility of employees for the stability of their own employment and income is increasing. The labor market places increased demands not only on professional and social skills, but also on the fundamental qualities of the individual - systematic thinking, logical thinking, reflection, creativity, leadership qualities, the ability to cooperate, etc. In world practice, life-long learning, constant improvement of hard, soft and meta-skills is becoming the norm. Large companies develop special training programs for their employees using modern information, computer and psychological technologies. The urgent task of the state remains the reform of the education system, the focus of which will be the formation of meta-skills of future employees. The necessity of combining the efforts of employers, state and international organizations and institutions in ensuring favorable conditions for the formation of a more adaptive workforce is substantiated.

A Spatio-Temporal Network Model of Chinese Ethnic Culture Transmission Paths Cultural

Abstract The current social network analysis method is essential in dealing with massive data on network relationships. By treating individuals, organizations, or countries as nodes and their relationships as connecting lines, the network diagrams provided a new perspective for studying social structure and communication paths. When exploring the path of Chinese national culture, the spatio-temporal network model can better analyze cultural diffusion’s dynamic characteristics and efficiency, and then optimize the diffusion strategy to promote the global diffusion of Chinese culture. In this study, the efficiency and characteristics of cultural diffusion are analyzed in depth using social network analysis, with indicators such as point degree centrality, mediated centrality, proximity centrality, and network flatness. The effectiveness of cultural diffusion is influenced by the identification of critical nodes and the trust propagation mechanism in the normalized model. Estimated by the generalized least squares method, the model demonstrates the increased efficiency of cultural dissemination under specific dissemination attributes and narrative attributes, in which the average number of plays of Chinese cultural content on the TikTok platform reaches 13,585,700, the number of likes reaches 355,123, and the number of comments is 31,398. Enhancing the global communication efficiency of Chinese national culture can be achieved by optimizing the social network structure and content strategy. This study provides a scientific basis and practical guidance for spreading Chinese culture globally, and emphasizes the importance of social platforms in cultural dissemination.

PERSONAL BRANDING AS A TOOL FOR PROFESSIONAL DEVELOPMENT OF A TEACHER

The article examines the essence and main aspects of the formation of personal branding in the context of the teacher's professional development. It was revealed that in the difficult conditions of the functioning of the educational environment, in which the concept of partner marketing and comprehensive focus on consumers is dominant today, the marketing subsystem occupies an important place as a component of the general management system of the organization. Marketing communications as an element of the market promotion strategy by ensuring presence and expanding awareness play a key role in shaping the competitive advantages of educational institutions. It was determined that the development of the organization's image indicators will serve to build trust in the educational institution and create a positive reputation. Accordingly, the key vector in forming a positive image is personal branding as a tool for the teacher's professional development. The key advantages of the development of a personal brand under the modern conditions of the educational sphere are analyzed. The algorithm of forming a personal brand is substantiated by identifying successive stages that will serve as step-by-step instructions for creating a brand. The practical aspects of using the main tools of personal brand development through the prism of the features of the teacher's image component are considered. Systematization of the basic rules and requirements for the creation of appropriate content in social networks, which will serve as a means of forming a teacher's personal brand in order to ensure the integration of the educational, educational and developmental educational process, has been carried out. The obtained results deepen the basic principles of developing a positive image of the educational institution in general, and the development of the theory of the formation of the teacher's own brand as an integrating element of the effective activity of the educational institution. Further research should be aimed at forming a practical toolkit for the development of a teacher's personal brand, which will provide not only the opportunity to create an individual competitive offer on the labor market, but also serve as a tool for the teacher's professional development.

Zagreb Library Association social media Editorial Board

In this paper, we will present the Editorial Board of Social Networks of the Zagreb Library Association (ZLA), when it was founded, how it functions, and the challenges it faces. The article will mention which social networks the Editorial Board uses to promote ZLA, and its members, but also to attract new ones. The paper will further explain how the Editorial Board chooses content to publish and how it creates its own, original content. It will present in detail all original categories, both ongoing and finished, and a free online visual editor used by the Editorial Board.
 The second part of this paper will present the analysis of the activities on each social network where ZLA has active profiles (Facebook, Instagram, LinkedIn, Twitter (today X), YouTube, Pinterest, TikTok). These statistical data will be compared: page followers and number of posts in each year from the beginning of 2020 to the first half of the current year. It will also be analysed to what extent the number of posts and their content affects the followers’ growth on each of the social networks.
 In conclusion, the paper will argue that content is not enough, it is also important how often it is published. It will present differences in the popularity and usage of social networks, the dynamics of publishing, and the choice of format, which the Editorial Board thinks is sometimes more important than the content.
 Finally, it will show the importance of its own, original content, which sets this Editorial Board apart from the content of social networks of other associations, libraries, and book bloggers. Thanks to high-quality and inspiring content, attractive visuals for announcements, and a well-thought-out publishing schedule, the Editorial Board has reached an enviable number of followers with whom it interacts regularly

EPH68 What Are the Main Symptoms of Long COVID? Real World Medical Data Cross-Referenced with Social Network Content

EPH68 What Are the Main Symptoms of Long COVID? Real World Medical Data Cross-Referenced with Social Network Content

How has the pandemic accelerated the transformation of Analytical Chemistry education from Generation Z to Alpha?

How has the pandemic accelerated the transformation of Analytical Chemistry education from Generation Z to Alpha?

НОВОСИБИРСКИЕ СЕЛЬСКИЕ РАЙОНКИ НА РАЗЛИЧНЫХ МЕДИПЛАТФОРМАХ

The article shows how the newspapers of the municipal districts of the Novosibirsk region are represented in social networks. The relevance of the stated topic is due to the fact that expanding interaction with social networks is one of the main features and trends in the development of regional and local press markets in Russia. The novelty is determined by the lack of published scientific articles concerning the current state of affairs in such a large region as the Novosibirsk region, as well as the empirical nature of the work carried out, which made it possible to trace the current state of this segment of the media sphere. The article presents the results of the analysis of audience indicators of public newspapers and monitoring of their content both in traditional social networks, such as VKontakte and Odnoklassniki, and in the relatively new Telegram, YouTube or Yandex.Zen. It is shown that all newspapers of this type have their own pages, on average they have 1.5–3 thousand subscribers on VKontakte and Odnoklassniki. The median number of posts per week is 22.5 posts for VKontakte and 20.8 for Odnoklassniki. At the same time, new platforms, such as Telegram, are just beginning to attract the interest of editorial offices — nine newspapers launched their tg channels in the spring of 2022, but the number of subscribers there is not yet large.

Fusion-based Representation Learning Model for Multimode User-generated Social Network Content

As mobile networks and APPs are developed, user-generated content (UGC), which includes multi-source heterogeneous data like user reviews, tags, scores, images, and videos, has become an essential basis for improving the quality of personalized services. Due to the multi-source heterogeneous nature of the data, big data fusion offers both promise and drawbacks. With the rise of mobile networks and applications, UGC, which includes multi-source heterogeneous data including ratings, marks, scores, images, and videos, has gained importance. This information is very important for improving the calibre of customized services. The key to the application's success is representational learning of fusing and vectorization on the multi-source heterogeneous UGC. Multi-source text fusion and representation learning have become the key to its application. In this regard, a fusion representation learning for multi-source text and image is proposed. The convolutional fusion technique, in contrast to splicing and fusion, may take into consideration the varied data characteristics in each size. This research proposes a new data feature fusion strategy based on the convolution operation, which was inspired by the convolutional neural network. Using Doc2vec and LDA model, the vectorized representation of multi-source text is given, and the deep convolutional network is used to obtain it. Finally, the proposed algorithm is applied to Amazon's commodity dataset containing UGC content based on the classification accuracy of UGC vectorized representation items and shows the feasibility and impact of the proposed algorithm.

VERBAL AND NON-VERBAL METHODS OF INFLUENCE IN EDUCATIONAL VIDEO CONTENT

The article focuses on the place of video content on the Internet and the distribution of video content in social networks. Currently, as a result of the active development of social networks and the Internet, interest in educational video content is increasing. The format of short informative videos attracts students with accessibility and understandability. The authors prove that the choice of the habitual, easily recognized form of presentation of educational materials facilitates the perception of information and makes it possible to motivate schoolchildren to expand their knowledge on their own. The authors clarify concept of speech impact in relation to a new form of “learning through entertainment”. Frequent methods of speech impact in videos are indicated. The actualization of verbal and non-verbal methods of influencing the audience is emphasized. Attempts are made to identify the reasons, on the one hand, for the successful memorization of scientific information by schoolchildren after watching the teacher’s video, and on the other hand, the teacher’s reluctance to present educational material in a form that is understandable for the “child of digitalization” in the classroom at school. To reach a consensus between the teacher in a real school and the online blogger teacher, who is often the same person, it is important to be able to combine modern methods of explaining new scientific knowledge based on students’ clip thinking with the traditional system of sequential systematic mastering of basic subjects.

The Relationship Between Risk Communication and Willingness to Receive Booster Doses of COVID-19 Vaccines

Background: Identifying factors associated with willingness to receive a booster dose of COVID-19 vaccines and risk communication are essential to control the pandemic. Objectives: The study aimed to evaluate the relationship between risk communication and willingness to receive booster doses of COVID-19 vaccines. Methods: This cross-sectional study was conducted on 746 over 18 years old adults in Kermanshah, Iran, on October 2022 using random cluster sampling from 396,000 members of the general population who had not received a COVID-19 booster dose. Required data were collected using an online questionnaire from people who met the study requirements and analyzed via SPSS software version 22. Results: A total of 746 participants were between the ages of 40 and 59, 374.0% had a history of underlying disease, and 61.9% received two doses of the Sinopharm vaccine. About 36.3% were willing to accept booster dosages, and men, older adults, and those with underlying diseases were more likely to receive booster vaccinations. The most influential sources of information were social networks and television. There was a positive and significant correlation between risk communication and willingness to receive booster vaccine doses (P > 0.05). The average risk communication scores were higher among those more likely to receive a booster dose. Conclusions: Health policymakers should pay more attention to risk communication in designing multi-component interventions and producing appropriate and authentic content in social networks to increase the general population’s acceptance of vaccine booster doses.

Social Media Narrative and its Prosumers: A Systematic Review from 2016-2023

The general objective of this systematic review of the scientific literature was to gather scientific evidence on the treatment of social media narrative and its prosumers. The review considered original publications in journals indexed in Scopus, Scielo, and Dialnet. Two types of analysis were developed, in order to eliminate possible biases. The quantitative analysis allowed us to locate the countries, languages, categorical treatment, results, and other aspects where the most research on transmedia narrative is being conducted. In this way, the scope of the topic in the scientific publications reviewed was established. On the other hand, by means of qualitative analysis, after the screening process, the approach of different perspectives was established: where the research on social media narrative in content sharing spaces leans towards, evidencing an inclination towards Discord. The search for the characterization of narrative content in social media was also carried out, and finally, the conception or treatment of the prosumer's role in the creation of content. It is concluded that the studies where the prosumer of digital platforms is related to a social media narrative should be increased to build and sustain the transcendental role of this as a value for the loyalty of various content in social networks, i.e., be responsible for generating virtual spaces where reliable content is expected to be disseminated.

Marijuana Use and Perceptions of Employment Suitability

Abstract: The present study extends research on marijuana social acceptance and employment suitability in the United States by examining how hiring managers view substance use-related content in social networking profiles. Specifically, this research focused on the weight hiring managers placed on social networking posts containing recreational marijuana content, medicinal marijuana content, and alcohol content when assessing potential candidates. With a sample of 405 hiring managers who evaluated experimentally manipulated social networking profiles, the results demonstrated a modest negative hiring bias against recreational marijuana content, with stronger negative effects for alcohol content. At the same time, social networking content related to medicinal marijuana content did not have a significant effect on perceptions of employment suitability. These findings highlight the nuanced nature of substance use stigma in today’s evolving society.

Digital destination branding: A framework to define and assess European DMOs practices

The current tourism scenario is complex, marked by deep crises and changes due to Covid-19 pandemic impacts. Tourist destinations more than ever need to assert their brand on the global market. This paper proposes a framework for setting up destination branding choices in the digital realm and for measuring the choices made by tourismplayers such as Destination Marketing Organisations (DMOs).This study proposes a Digital Destination Branding (DDB) framework drawing from the academic literature and practitioners’ perspectives. This framework is organised on three dimensions in which the main digital branding choices are grouped to promote a tourist destination, engage visitors and offer them services for a superior visiting experience: Social Network Presence (SNP), Social Network Content (SNC) and Digital Tourism Services (DTS). Each of these pillars is based on multiple items and micro-items. Assigning scores to each element of the framework allows for measuring compliance with the model’s requirements for digital destination branding. Applying our proposed framework, the empirical component of the study provides a comparative evaluation of the DDB practices of the 2021 top 10 European destinations. The results provide an overview of how DMOs are currently using digital channels and tools for destination branding.

Beyond reading together: Facilitating knowledge construction through participation roles and social annotation in college classrooms

Social reading is a common practice in higher education to engage students in critical reading and classroom dialogues around course materials. This paper introduces a study that integrated a social annotation technology and a scaffolding framework to support social reading in undergraduate classrooms. The framework, grounded in the computer-supported collaborative learning literature, specifies three participation roles—namely, facilitator, synthesizer, and summarizer—that have distinct responsibilities in each week's social reading activities. Using Hypothesis, the social annotation technology, we piloted the framework in a fully online undergraduate course. To examine how the framework facilitated social interaction and knowledge construction, we applied social network analysis and content analysis to a rich dataset. Results revealed that the framework fostered active participation in class with students followed the assigned roles properly. This study has practical implications for online teaching and collaborative learning in higher education.

DIGITAL SERVICES AND PORTALS FOR MIGRANTS IN THE SOCIAL SECURITY SYSTEM

DIGITAL SERVICES AND PORTALS FOR MIGRANTS IN THE SOCIAL SECURITY SYSTEM

UKRAINIAN LANGUAGE TWEETS ANALYSIS TECHNOLOGY FOR PUBLIC OPINION DYNAMICS CHANGE PREDICTION BASED ON MACHINE LEARNING

Context. Automation of public opinion research will allow not only to reduce the amount of manual work, but also to obtain time slices of the results without additional efforts. Since direct interaction with respondents should be avoided, public opinion should be analyzed based on the sources of its free expression. Social networks are great for this role, as their people freely publish their thoughts or emotionally truthfully react to published information about certain events. Statistics show that data from social networks is not enough to obtain a full-fledged result, because a significant percentage of people do not use social networks. However, the automation of the study of even such a stratum of the population is already a good result for analyzing the dynamics of changes in public opinion in accordance with events in the country/world and, accordingly, for correcting the processes of public administration in the future.
 Objective of the study is to develop a technology for analyzing the flow of Ukrainian-language content in social networks for public opinion research based on finding clustered thematic groups of tweets.
 Method. The article develops a technology for finding tweet trends based on clustering, which forms a data stream in the form of short representations of clusters and their popularity for further research of public opinion. An effective approach to tweet collection, filtering, cleaning and pre-processing based on a comparative analysis of Bag of Words, TF-IDF and BERT algorithms is described. The impact of stemming and lemmatization on the quality of the obtained clusters was determined. And optimal combinations of clustering methods (K-Means, Agglomerative Hierarchical Clustering and HDBSCAN) and vectorization of tweets were found based on the analysis of 27 clusterings of one data sample. The method of presenting clusters of tweets in a short format is selected.
 Results. Algorithms using the Levenstein Distance, i.e. fuzz sort, fuzz set and levenshtein, showed the best results. These algorithms quickly perform checks, have a greater difference in similarities, so it is possible to more accurately determine the limit of similarity. According to the results of the clustering, the optimal solutions are to use the HDBSCAN clustering algorithm and the BERT vectorization algorithm to achieve the most accurate results, and to use K-Means together with TF-IDF to achieve the best speed with the optimal result. Stemming can be used to reduce execution time.
 Conclusions. In this study, the optimal options for comparing cluster fingerprints among the following similarity search methods were experimentally found: Fuzz Sort, Fuzz Set, Levenshtein, Jaro Winkler, Jaccard, Sorensen, Cosine, Sift4. In some algorithms, the average fingerprint similarity reaches above 70%. 3 effective tools were found to compare their similarity, as they show a sufficient difference between comparisons of similar and different clusters (> 20%). Based on the selected effective methods, trend analysis was successfully performed on 90,000 tweets over 7 days for 5 topics of the week using K-Means and TF-IDF for clustering and vectorization, as well as fuzz sort for cluster fingerprint comparison with a 55% similarity threshold.

Professional self-presentation of research and teaching staff on social network sites

. In the modern labour sphere, the image of an employee in the virtual space has a tangible value, becoming a significant social resource in employment and subsequent career advancement. The degree of demand for a personal brand in the online environment is associated with the characteristics of specific professions. For scientific and pedagogical workers, self-presentation in virtual social networks is significant not only for their career growth, development of social capital and personal brand, but also valuable as a factor in scientific communication. A competent professional self-presentation in the virtual space helps to form a favourable social image that enhances the prestige of a university teacher and scientist. During the pandemic and the transition of educational and research activities to the online space caused by it, the demand for professional self-presentation of educators in the virtual environment has increased significantly. The article analyses the practices of professional self-presentation of scientific and pedagogical workers in virtual social networks. A typology of strategies for using universal social networks (VKontakte, etc.) and professional (scientific) social networks (Research Gate, Academia Edu, Mendeley) is presented. Particular research interest is focused on the practices of communication and professional self-presentation through virtual social networks. The advantages and limitations of the use of online communication by teachers are identified, as well as the types of priority results that users achieve or would like to achieve. For the empirical basis of the analysis the authors used the data of a survey conducted by the method of semi-structured interviews by a research team with the participation of the author of the article of 120 scientific and pedagogical employees of six Russian universities included in the implementation of the 5–100 project. Based on the results of the study, a classification of higher education teachers was formed depending on their awareness and the desire to broadcast content in virtual social networks: experts and opinion leaders, conservatives, spammers, passive, absent. A typology of the strategy of professional self-presentation of scientific and pedagogical workers in virtual social networks has been created: voluntary, forced, non-priority professional self-presentation; refusal of self-presentation (external or internal).

Exploring the mechanisms of data-supported reflective assessment for pre-service teachers’ knowledge building

ABSTRACT This interventional case study adopted a data-supported reflective assessment (DSRA) design to help pre-service teachers (PTs) engage in effective Knowledge Building (KB) and examined the mechanisms of this design to support PTs’ productive KB discourse. The participants were 80 PTs from two classes taking the same course. Statistical analysis of indices from social network theory and content analysis of the participants’ Knowledge Forum discourse revealed DSRA’s positive influence on PTs’ KB discourse. Thematic analysis of the PTs’ prompt sheets, supplemented by analysis of classroom videos and classroom observations, revealed four mechanisms by which the DSRA fostered PTs’ KB: (1) identifying promising directions for further inquiry; (2) navigating data for extending collective focal ideas; (3) integrating separated keywords and identifying problems for further inquiry; (4) and improving “storylines” in the collective inquiry process and making numerical values meaningful. These findings extend the literature by revealing the mechanisms by which learners engage in productive shared regulation which are crucial for successful inquiry and KB. The findings also have significant implications for teacher educators and researchers seeking to design technology-enhanced learning to develop PTs’ higher-level KB competencies.

Social Network Analysis of Basic Necessity Scarcity on Twitter: Evidence from Indonesia

The scarcity of basic necessities, a popular discussion topic in Indonesia, poses significant challenges to the citizens. Indonesians frequently comment on the issue on social media, including Twitter, which is perceived as a democratic public space to express opinions, interests, and information discursively in establishing communications as part of intercultural dialogues. The current study aims to analyse relevant communication networks and content regarding the topic of fundamental necessity scarcity in Indonesia on Twitter. Specifically, a cross-sectional design was employed with social network analysis (SNA) and content analysis (CA) conducted on public Twitter accounts. The study discovered a low communication intensity between nodes due to the existing dominance of several central actors. Simultaneously, the most frequently employed words were food, oil, cooking, the task force, and the hashtag phrase “punishthehoardersofcookingoil”. Auto-coded sentiment results demonstrated 8,963 references at neutral levels, 566 with moderately negative degrees, 500 with high negative levels, 90 with moderately positive, and 21 with highly positive degrees. The findings propounded that Twitter is an online public space, allowing autonomous and unrestricted debates on pertinent topics.

Vocational Domain Identification with Machine Learning and Natural Language Processing on Wikipedia Text: Error Analysis and Class Balancing

Highly-skilled migrants and refugees finding employment in low-skill vocations, despite professional qualifications and educational backgrounds, has become a global tendency, mainly due to the language barrier. Employment prospects for displaced communities are mostly decided by their knowledge of the sublanguage of the vocational domain they are interested in working. Common vocational domains include agriculture, cooking, crafting, construction, and hospitality. The increasing amount of user-generated content in wikis and social networks provides a valuable source of data for data mining, natural language processing, and machine learning applications. This paper extends the contribution of the authors’ previous research on automatic vocational domain identification by further analyzing the results of machine learning experiments with a domain-specific textual data set while considering two research directions: a. prediction analysis and b. data balancing. Wrong prediction analysis and the features that contributed to misclassification, along with correct prediction analysis and the features that were the most dominant, contributed to the identification of a primary set of terms for the vocational domains. Data balancing techniques were applied on the data set to observe their impact on the performance of the classification model. A novel four-step methodology was proposed in this paper for the first time, which consists of successive applications of SMOTE oversampling on imbalanced data. Data oversampling obtained better results than data undersampling in imbalanced data sets, while hybrid approaches performed reasonably well.