Trends In Social Networks Research Articles

Exploring Social Networking Trends among School Students from Class 4th To 8th of Kendriya Vidyalaya Dholpur, Rajasthan

Exploring Social Networking Trends among School Students from Class 4th To 8th of Kendriya Vidyalaya Dholpur, Rajasthan

MEDIA’S VIDEO FORMAT OF REELS ON INSTAGRAM: GENRE AND LEGAL ASPECTS OF CREATION

The article is devoted to one of the modern media trends in social networks – reels, short vertical explanatory videos created to inform the audience that receives information from mobile devices. The article emphasizes the popularity of this format of information provision and its demand, which is confirmed by the data from various sociological surveys. A preliminary analysis of scientific sources has shown a lack of research on media’s reels. Authors characterize the few works of scholars, mostly foreign researchers, which partially reveal this object. It was found that most of the articles about the reels were written by practitioners, particularly in the marketing field. The publication aims to analyze reels’ genre specifics and characterize the legal rules for their creation. The task is to reveal reels as a popular media video format on Instagram, their genre features, and compliance with legal norms. Adequate research methods have been used: analysis, observation, description, comparative analysis, analytical and synthetic information processing, induction, deduction, generalization, and systematization. The paper emphasizes the active use of media, particularly BBC News Ukraine, in social networks of a new genre of short vertical videos, i.e., reels that are explanatory and well-received by the audience. Reels are characterized by specific editing, with previews, information hooks at the beginning, dynamic narration against the background of rapid image changes, and encouragement of the audience to comment and go to the media’s website as their mandatory components. Captions and audio play an important role in their composition. The studied videos comply with the current international legislation on freedom of speech, particularly Article 10 of the European Convention on Human Rights. They also comply with the national norms of the laws «On Information» and «On Media», as well as with the bylaws on limiting the dissemination of information about the war. The BBC complies with copyright law when using audiovisual content from other sources. It is summarized that reels are a powerful media tool for informing the audience on Instagram and explaining complex issues, which is specific in terms of editing and composition, and it meets current legal standards. The author emphasizes the prospect of further comparative studies of media reels.

Analyzing online public opinion on Thailand-China high-speed train and Laos-China railway mega-projects using advanced machine learning for sentiment analysis

Sentiment analysis is becoming a very popular research technique. It can effectively identify hidden emotional trends in social networks to understand people’s opinions and feelings. This research therefore focuses on analyzing the sentiments of the public on the social media platform, YouTube, about the Thailand-China high-speed train project and the Laos-China Railway, a mega-project that is important to the country and a huge investment to develop transportation infrastructure. It affects both the economic and social dimensions of Thai people and is also an important route to connect the rail systems of ASEAN countries as part of the Belt and Road Initiative. We gathered public Thai reviews from YouTube using the Data Application Program Interface. This dataset was used to train six sentiment classifiers using machine learning and deep learning algorithms. The performance of all six models by means of precision, recall, F1-score and accuracy are compared to find the most suitable model architecture for sentiment classification. The results show that the transformer model with the WangchanBERTa language model yields best accuracy, 94.57%. We found that the use of a Thai language-specific model that was trained from a large variety of data sources plays a major role in the model performance and significantly increases the accuracy of sentiment prediction. The promising performance of this sentiment classification model also suggests that it can be used as a tool for government agencies to plan, make strategic decisions, and improve communication with the public for better understanding of their projects. Furthermore, the model can be integrated with any online platform to monitor people's sentiments on other public matters. Regular monitoring of public opinions could help the policy makers in designing public policies to address the citizens’ problems and concerns as well as planning development strategies for the country.

Influencia de las redes sociales en la autoestima, imagen corporal y satisfacción corporal de adolescentes y jóvenes

Objective: to explore the relationship between the use of social networks and the self-esteem, body image and body satisfaction of adolescents and young adults. Methods: a narrative review was conducted. There was a bibliographic search in the PubMed, CINAHL and PsycInfo databases, for articles published during the 2017-2022 period, using the descriptors “young adult”, “adolescent”, “social media”, “body dissatisfaction”, “body image” and “self-concept”; as well as natural language. Articles with a male and/or female population sample between 12 and 30 years of age were included., with social networks mentioned generically or specifically (Instagram, TikTok and/or Facebook), and analysing the relationship between social networks and some of the following constructs: self-esteem, corporal image, and/or body satisfaction/dissatisfaction, either individually or as a whole. Results: out of the 536 initial records, 29 articles met the selection criteria and were reviewed in depth. Findings were grouped into four themes associated with the constructs to be studied: social comparison, feedback, dedication, and trends in social networks. Conclusion: social networks have a negative impact on body satisfaction and body image, although their impact on self-esteem is influenced by the feedback received for the images published and the time invested. The new #bodypositive trend is associated with higher psychological, social and emotional wellbeing, healthier habits and improvements in self-care, as well as being a protective factor against body dissatisfaction. It is necessary to develop Nursing interventions for health promotion and prevention in the school and community settings, based on these findings.

Trends of social networks in the American College of Surgeons Clinical Congress and the Congreso Nacional de Cirugía. Analysis of the #ACSCC20 and #CNCirugia2020

AimThe objective of this study is to analyze the impact of the congresses of the American College of Surgeons (ACSCC2020) and the National Surgery Congress of the Spanish Association of Surgeons (CNC2020) in virtual format due to the SARS-CoV2 pandemic according to the fingerprint. Material and methodsThe Twitter hashtags # ACSCC20 and # CNCirugia2020 were studied to determine tweets, retweets, users and impressions. The data on the accounts with the greatest influence and the historical evolution of the congresses between 2015 and 2020 were analyzed. We used the symplur software to collect and analyze the data. ResultsBetween 2015 and 2017 there was a consistent increase in the number of tweets, participants and impressions. Between 2018 and 2020, the ACS maintains the number of impressions with the fewest number of tweets. However, the CNC continues to grow and achieves its best metrics in 2020. We found statistically significant differences between the most prolific accounts of the ACSCC versus the CNC (P < .002) but there are no differences between the 10 most influential accounts (P = ,19) or the accounts with the highest number of impressions (P = .450) ConclusionsVirtual congresses generate a global impact through the use of Twitter for the dissemination of knowledge. In the present 2020, the growth of the impact on social networks has been proportionally greater in the CNC than in the ACSCC. However, the ACS virtual congress generated the greatest impact on social networks measured by the number of users, tweets and impressions between 2015 and 2020.

Korean popular culture analytics in social media streaming: evidence from YouTube channels in Thailand

This research aimed to study and analyze the influence and impact of Korean popular culture (K-pop) on Thai society. In this study, we used Social Network Analysis (SNA) to analyze streaming data obtained from a variety of YouTube channels belonging to YouTubers across the world, text analytics to analyze demographic characteristics, YouTuber's presentation techniques, as well as subscriber behavior, and multiple correlations analysis to analyze the relationship between factors affecting YouTube Channels in Thailand. The findings revealed that five Thai YouTube Channels were influencing Thai society. Furthermore, there were robust positive correlations between the number of dislikes and the number of comments (0.79), and the number of likes and comments (0.65). Additionally, there was a positive correlation between the number of views and the number of dislikes and one between the number of likes and dislikes. Future research can supplement the present findings with other social media sources to yield an even more diverse and comprehensive analysis. These analytics can be applied to various situations, including corporate marketing strategies, political campaigns, or disease/symptom analysis in medicine. This research extends to social computing by revealing intelligent trends in social networks.

Social Networking Trend in Improving Educational Leadership Competency in Higher Learning: Systematic Data Analysis

This study examined social network trends in improving educational leadership quality and higher learning outcomes through systematic data analysis. Forward or backwards, the quality of human resources and scholarly output in the academic environment is closely related to social networking, which is increasingly becoming a phenomenon today. The researcher has carried out a series of data collection through electronic searches in several international publications in various scientific communication contexts to understand this. Furthermore, the analysis process includes understanding the study questions, coding, analysis, and in-depth interpretation to obtain valid and reliable study findings that answer this study's questions. Our e-search, especially on the Google Scholar data source, ERIC publications, and other online data sources from the 2010 to 2021 publications, was prioritized. Based on the discussion of results and observations of current social networking trends, it can be concluded that social networking brings benefits such as building social relationships, communication, keeping up with renewal, more personal relationships, more genuine connections, advertising, and other networking among educational leaders worldwide.

Тенденції розвитку бізнес-реклами в соціальних мережах

Abstract. Introduction. The article examines current trends in advertising campaigns on the Internet and social networks. Social networks are seen as a tool to influence the target audience and study its benefits, growth, development of companies and expanding market share. Studied the theory of the importance of social networks at the initial levels of small and medium business development, including in connection with the possibility of attracting the necessary financial resources and access to information of various kinds, and in the subsequent stages of development. An analysis of examples of companies whose growth has been largely due to the integration of development strategies through social networks. The medium- and long-term prospects for the development of the advertising industry with the mass introduction of virtual and augmented reality technologies and the expansion of 5G Internet networks were considered. Purpose. The purpose of the study is to substantiate the reasons for doing business on social networks, analysis of current trends in social networks, assessing the possibility of doing business and building marketing strategies through social networks. Results. The article describes the main trends of advertising on social networks, why is it worth to run advertising campaign on social networks, describes the social network as an advertising tool, how social networks will effectively interact with the target audience and gives examples of successful projects of large companies implemented in social networks. Were reviewed main directions of development and improvement of advertising methods in social networks, how it will happen, what is needed for this and why it is impossible to implement at this stage of development. Listed all the positive aspects that will help increase the efficiency of sales goods and services, as well as the negative aspects with the implementation of these ways. Conclusion. Development of virtual and augmented reality and also 5G techonologies: 1. Significantly increase the perception of advertising; 2. Allows you to process large amounts of information in a short time; 3. Solve the problem of oversaturation with advertising of various sources of information (billboards, newspapers, the Internet); 4. Advertising will become more relevant and timelier; 5. Advertising will become more relevant for the user, which will increase business revenue; 6. Advertising will be less intrusive, which will increase user loyalty to it. In the long run, the amount of data passively collected by technical devices around will increase, which will invariably affect the prospects for marketing development in general. With the expansion of Internet coverage around the world and the increasing availability of technical devices, the number of network users will increase, which will make it easier to interact with the intended target audience, change the ways of advertising and over time social networks will become its main location. Increasing the data collected will increase user loyalty and help make advertising less intrusive and more useful to each potential customer. Given advertising prices and audience reach, businesses will be more focused on developing brands on social media and marketing mostly online.

Online social network trend discovery using frequent subgraph mining

Graph mining has become a well-established discipline within the domain of data mining. It has received much interest over the last decade as advances in computer hardware have provided the processing power to enable large-scale graph data mining to be conducted. Frequent subgraph mining (FSM) plays a very significant role in graph mining, attracting a great deal of attention in different domains, such as Bioinformatics, web data mining and social networks. Online social networks (SNs) play an important role in today’s Internet. These social networks contain huge amounts of data and present a challenging problem. FSM has been used in SNs to identify the frequent pattern trends existing in the network. A frequent pattern trend is defined as a sequence of time-stamped occurrences (support) value for specific frequent pattern that exist in the data. For example, most active researchers, most visited web pages or users’ navigation patterns over the web are few to mention. In the past few years, social network trend mining has been an active area of research. Many graph mining algorithms have been proposed, but a very limited effort exists for capturing an important dimension of SNs, which is trends discovery. Therefore, this paper introduces a novel FSM approach, called A-RAFF (A RAnked Frequent pattern-growth Framework), to discovering and comparing the frequent pattern trends exist in the social network data. Furthermore, the social network frequent pattern trend analysis has been evaluated using two standard social networks, Facebook-like network and the famous MSNBC news network datasets. Consequently, the discovered trends will help the underlying social networks to further enhance their platforms for the betterment of the users as well as for their business growth.

THE IMPORTANCE OF SOCIAL NETWORKS AS A TOOL FOR EMPLOYER BRANDING FROM THE VIEWPOINT OF SLOVAK CONSUMERS

Social networks are a phenomenon that has become a regular part of life for the majority of consumers and they spend much of free time there. With the increasing popularity of social networks, the possibilities of their use are also expanding. Recently, the trend of social networks has also penetrated into the personnel marketing. Due to the current situation on the labour market, in addition to traditional techniques, companies have begun to use the tools of the social networks to recruit employees and build their employer brands. The aim of this article is to define the theoretical basis of social networks and their usage in employer branding from the viewpoint of domestic (Slovak) and foreign authors. This includes an analysis of the practical usage of social networks in employer branding. The secondary data for the analysis were obtained from annual companies reports, statistical tables and published professional publications. In order to determine the perception of social networks as a tool for employer branding, a questionnaire survey was conducted among Slovak people. General scientific methods were applied for the processing of the data, as well as mathematical methods to evaluate the data, collated from the results of the questionnaire survey. Based on this, benefits of social networks and their usage in employer branding are highlighted.

Trends of social media use by enterprises

The paper examines the main modern trends of social networks use by enterprises in the world in general and in Ukraine in particular: content marketing, video marketing, influencer marketing (influencers and microinfluencers), brand advocacy; e-mail marketing; social commerce; lead magnets use; personal correspondence of brands with users (messaging) chat bots; augmented reality and gamification. The features of each of the given trend marketing tools in social media are analyzed and the expediency of their use by Ukrainian enterprises to improve the efficiency of interaction with thetarget audience in the space of social networks is substantiated. In the modern world of the rapid development of communication technologies, enterprises not only cannot fail to take into account the significant impact of social networks on consumer behavior, but to ensure the sustainability of the business, they also need to study and take into account the main SMM trends, constantly adjusting the communication strategy taking into account these trends.

Extracting Emerging Topics Based on User Comments

The Social website is the place where people can share information about the current events around the world at any time. Social Network Data Analysis Applications have experienced tremendous improvements over the past few years. Due to its straightforwardness, this social network allows data trends to detect current trends, which will have the capacity to affect company global research on the common market research and group program. Unfortunately, by mistake, social network trends mislead people. Trend handling is based entirely on the users text post, including the text and its current combined searches and detection of current trends, but these methods do not know behind compromise and pseudo account users. Therefore, we try to design a safe dialogue approach for handling trends in social networking, which will have a real account of complex manufacturers and small business entities trying to reach fraudulent means and creates compromises and fake accounts. Users made us pay attention to this logic that will lead us to remove the current topic of interest between users to process these contents in this particular behavior.

Gender Differences in Personality, Motivation and the Tendency to Follow Social Networking Trends among Indian Millennials

Gender Differences in Personality, Motivation and the Tendency to Follow Social Networking Trends among Indian Millennials

A comprehensive study of online event tracking algorithms in social networks

Recently, social networks have provided an important platform to detect trends of real-world events. The trends of real-world events are detected by analysing flow of massive bulks of data in continuous time steps over various social media platforms. Today, many researchers have been interested in detecting social network trends, in order to analyse the gathered information for enabling users and organisations to satisfy their information need. This article is aimed at complete surveying the recent text-based trend detection approaches, which have been studied from three perspectives (algorithms, dimension and diversity of events). The advantages and disadvantages of the considered approaches have also been paraphrased separately to illustrate a comprehensive view of the previous works and open problems.

Personalized Recommendation for Online Social Networks Information: Personal Preferences and Location-Based Community Trends

Microblogs, such as Twitter, are a way for users to express their opinions or share pieces of interesting news by posting relatively short messages (corpus) compared with the regular blogs. The volume of corpus updates that users receive daily is overwhelming. Also, as information diffuses from one user to another, some topics become of interest to only small groups of users, thus do not become widely adopted, and could fade away quickly. This paper proposes a framework to enhance user’s interaction and experience in social networks. It first introduces a model that provides better subscription to the user through a dynamic personalized recommendation system that provides the user with the most important tweets. This paper also presents TrendFusion, an innovative model used to enhance the suggestions provided by the social media to the users. It analyzes, predicts the localized diffusion of trends in social networks, and recommends the most interesting trends to the user. Our performance evaluation demonstrates the effectiveness of the proposed recommendation system and shows that it improves the precision and recall of identifying important tweets by up to 36% and 80%, respectively. Results also show that TrendFusion accurately predicts places in which a trend will appear, with 98% recall and 80% precision.

Mining Influencers Using Information Flows in Social Streams

The problem of discovering information flow trends in social networks has become increasingly relevant due to the increasing amount of content in online social networks, and its relevance as a tool for research into the content trends analysis in the network. An important part of this analysis is to determine the key patterns of flow in the underlying network. Almost all the work in this area has focused on fixed models of the network structure, and edge-based transmission between nodes. In this article, we propose a fully content-centered model of flow analysis in networks, in which the analysis is based on actual content transmissions in the underlying social stream, rather than a static model of transmission on the edges. First, we introduce the problem of influence analysis in the context of information flow in networks. We then propose a novel algorithm InFlowMine to discover the information flow patterns in the network and demonstrate the effectiveness of the discovered information flows using an influence mining application. This application illustrates the flexibility and effectiveness of our information flow model to find topic- or network-specific influencers, or their combinations. We empirically show that our information flow mining approach is effective and efficient than the existing methods on a number of different measures.

Classification of Arabic Twitter Users: A Study Based on User Behaviour and Interests

Social networks are among the most popular interactive media today due to their simplicity and their ability to break down the barriers of community rules and their speed and because of the increasing pressures of work environments that make it more difficult for people to visit or call friends. There are many social networking products available and they are widely used for social interaction. As the amount of threading data is growing, producing analysis from this large volume of communications is becoming increasingly difficult for public and private organisations. One of the important applications of this work is to determine the trends in social networks that depend on identifying relationships between members of a community. This is not a trivial task as it has numerous challenges. Information shared between social members does not have a formal data structure but is transmitted in the form of texts, emoticons, and multimedia. The inspiration for addressing this area is that if a company is advertising a sports product, for example, it has a difficulty in identifying targeted samples of Arab people on social networks who are interested in sports. In order to accomplish this, an experiment oriented approach is adopted in this study. A goal for this company is to discover users who have been interacting with other users who have the same interests, so they can receive the same type of message or advertisement. This information will help a company to determine how to develop advertisements based on Arab people’s interests. Examples of such work include the timely advertisement of the utilities that can be effectively marketed to increase the audience; for example, on the weekend days, the effective market approaches can yield considerable results in terms of increasing the sales and profits. In addition, finding an efficient way to recommend friends to a user based on interest similarity, celebrity degree, and online behaviour is of interest to social networks themselves. This problem is explored to establish and apply an efficient and easy way to classify a social network of Arab users based on their interests using available types of information, whether textual or nontextual, and to try to increase the accuracy of interest classification. Since most of the social networking is done from the mobiles nowadays, the efficient and reliable algorithm can help in developing a robust app that can perform the tweet classification on mobile phones.

Social networking trends among female health college students in King Khalid University, Saudi Arabia

Objective: The present research was to study the social networking trends among female health college students in KKU Abha, Saudi Arabia. Methodology: A pretested questionnaire was developed consisting of 11 items concerning with individual’s usage of social media. All female students of colleges in King Khalid University were considered for the study. Results: A total of 466 female students participated in the research. 96% of the students had registered themselves at some social networking site. Whatsapp was found to be the most popular application (17.4%) followed by Twitter (16.2%), YouTube (15.2%), Google-plus (13%), Facebook (13%), Skype (10.7%) and others. Majority of students (64%) accessed Google for their academic assignments and information gathering.76% of students had practice of accessing to SNS on a daily basis. Majority (43%) of students’ accessed social networking sites late night before sleep or after college hours (32%).The most common reason for SNS usage was for Entertainment (45%). The Pearson’s correlation analysis showed that there was no significant correlation between the frequency of SNS usage and student’s average academic grades (r = 0.064, p = 0.174). Conclusions: The female students at the university are very active on SNS. The social media platforms are mainly used for social interactions or entertainment. There was no statistically significant correlation between social media usage and academic outcomes. Social media platforms should be considered as an effective and informal media in engaging students for academic purposes.

A Bayesian approach for predicting the popularity of tweets

We predict the popularity of short messages called tweets created in the micro-blogging site known as Twitter. We measure the popularity of a tweet by the time-series path of its retweets, which is when people forward the tweet to others. We develop a probabilistic model for the evolution of the retweets using a Bayesian approach, and form predictions using only observations on the retweet times and the local network or "graph" structure of the retweeters. We obtain good step ahead forecasts and predictions of the final total number of retweets even when only a small fraction (i.e., less than one tenth) of the retweet path is observed. This translates to good predictions within a few minutes of a tweet being posted, and has potential implications for understanding the spread of broader ideas, memes, or trends in social networks.

Trend mining in social networks: from trend identification to visualization

AbstractA four‐stage social network trend mining framework, the Identification, Grouping, Clustering and Visualization framework, is described. The framework extracts trends from social network data and then applies a sequence of techniques (‘tools’) to this data to facilitate interpretation of the identified trends. Of particular note is the visualization of trend migrations (changes) that feature within time‐stamped network data. The framework is illustrated using a sequence of four social networks extracted from the Cattle Tracing System in operation in Great Britain, although it could equally well be applied to other forms of temporal data. The presented analysis of the Identification, Grouping, Clustering and Visualization framework indicates advantages, with respect to network trend mining, that can be gained, especially when the framework is applied to real data.