scholarly journals Efficiency Analysis of Russian Regional Authorities on Social Networks

2021 ◽  
Author(s):  
Natalya Zimova ◽  
Egor Fomin ◽  
Aleksandra Smagina
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Effective Means ◽  
Public Confidence ◽  
Behavioral Models ◽  
Active Population ◽  
Regional Authorities ◽  
The Impact ◽  
Politically Active

Nowadays it is hardly possible to overestimate the role of digital technologies in the work of Russian politicians. One of the effective means that gives new opportunities for modern politicians is social media. The phenomenon of social media has no longer been just entertainment, it has become a powerful tool of influencing public opinion. If used properly social media can help both shape the image of a politician and increase the electorate trust and loyalty. Social networks appear to be the most accessible and popular type of social media for politicians. This article covers the topic of maintaining social network profiles by the heads of several Russian regions, moreover, the authors assess the impact on perception of politicians by the politically active population. To achieve this goal we conducted an analysis of the politician activity on the popular Russian social network VKontakte over a certain period using a special technical tool — a social media multi-factor monitoring and analyzing system developed by Russian company “Kribrum”. It contains various analysis methods, as well as linguo-semantic and psychological behavioral models. It was found that maintaining accounts on social networks contributes to the increase of public confidence in the authorities. However, the potential of this tool is not exploited to the full scale: in some cases, the heads of regions do not pay sufficient attention to maintaining their own pages on the Internet. They cannot cope with technical tools or ignore them, do not work with citizens through social networks, which eventually leads to the lack of credibility. In the modern conditions, with the federal government recognizing the importance of social media and taking into account transparency and public acknowledgement of regional authorities, the tendency towards intensifying work with citizens on virtual networks is likely to increase. Keywords: digitalization, social networks, credibility of authorities, key performance indicators (KPI)

scholarly journals I Know Where You Are Coming From: On the Impact of Social Media Sources on AI Model Performance (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13971-13972
Author(s):  
Yang Qi ◽  
Farseev Aleksandr ◽  
Filchenkov Andrey
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Model Performance ◽  
User Profiling ◽  
Personalized Recommendation ◽  
Modal Data ◽  
Social Media Networks ◽  
The Impact

Nowadays, social networks play a crucial role in human everyday life and no longer purely associated with spare time spending. In fact, instant communication with friends and colleagues has become an essential component of our daily interaction giving a raise of multiple new social network types emergence. By participating in such networks, individuals generate a multitude of data points that describe their activities from different perspectives and, for example, can be further used for applications such as personalized recommendation or user profiling. However, the impact of the different social media networks on machine learning model performance has not been studied comprehensively yet. Particularly, the literature on modeling multi-modal data from multiple social networks is relatively sparse, which had inspired us to take a deeper dive into the topic in this preliminary study. Specifically, in this work, we will study the performance of different machine learning models when being learned on multi-modal data from different social networks. Our initial experimental results reveal that social network choice impacts the performance and the proper selection of data source is crucial.

scholarly journals COMPANY IMAGE IN SOCIAL NETWORK AS PREDICTOR OF INTENTION TO APPLY FOR A JOB POSITION

2019 ◽  
pp. 209-222
Author(s):  
Neringa Vilkaite-Vaitone ◽  
Ugne Lukaite
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Labour Market ◽  
Positive Impact ◽  
Research Problem ◽  
Job Seekers ◽  
Scientific Discussion ◽  
The Impact ◽  
Job Position

This paper summarizes the arguments and counterarguments within the scientific discussion on the issue of employer attractiveness on social networks, organizational image impact upon intentions to apply for a job position in banking industry. The main purpose of the research is to theoretically and empirically evaluate the impact of attractive workplace communications on social networks and image of a bank as an employer upon intentions to apply for a job position. Systematization of literary sources and approaches for solving the research problem indicates that there exist sufficient scientific background to expect a positive impact of workplace attractiveness on the image of employer. Usage of social media is also supposed to have a positive impact on the image, while the image might be a predictor of intentions to apply for a job. The relevance of the decision of this scientific problem is that social media has a huge potential to strengthen employer’s image, however, it also might destroy a carefully formed employer’s image. Such a potential stresses the role of banks’ communications on social network for building efficient employer image. A questionnaire research of students is carried out in Lithuania. The paper presents the results of an empirical analysis, which showed that job seekers in a labour market behave similarly to consumers in a marketplace. Banks should put efforts in order to present themselves as attractive working places on social networks in order to form a favourable employer’s image and encourage job seekers to apply for positions. Results of this study indicate that usage of social networks for the formation of image of employer can be a helpful tool. This is important because there are clear links between workplace attractiveness, usage of social networks and image of an employer. The latter construct is positively related to intentions to apply for a job. The results of the research can be useful for commercial banks. This study provides banks with useful insights of the factors that determine their image in labour market. Such insights give banks a perceptible information of how to become more attractive in labour market with the help of communications on social networks.

scholarly journals The Role of Social Networks in Emergency Management

2019 ◽  
Vol 10 (4) ◽  
pp. 1346
Author(s):  
Alexey SMIRNOV ◽  
Natalia BELOZEROVA ◽  
Natalia ALANICHEVA ◽  
Irina TRUBNIKOVA ◽  
Yuriy MOTORYGIN
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Emergency Management ◽  
Information Dissemination ◽  
The Social ◽  
The Russian Federation ◽  
Definition Of ◽  
The Impact

The article is focused on the impact of social networks which are becoming increasingly important for information dissemination, user-centered response and efficient coordination between residents and governmental departments at all stages of a disaster. We intend to examine the definition of ‘social network», the most important types of emergency domain-related social media, their functions and benefits. We also propose to investigate the recent activities of the Ministry of Emergencies of the Russian Federation in the sphere of emergency prevention and risk reduction via accessing the social networks pages and assessing their effectiveness.

Board structure and corporate disclosure via social media: an empirical study in the UK

2018 ◽  
Vol 42 (5) ◽  
pp. 595-614 ◽  
Author(s):  
Mohamed A.K. Basuony ◽  
Ehab K.A. Mohamed ◽  
Khaled Samaha
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Stock Exchange ◽  
Binary Logistic Regression ◽  
Board Structure ◽  
Corporate Disclosure ◽  
Board Diversity ◽  
Content Type ◽  
The Impact

PurposeThe purpose of this paper is to investigate the impact of board structure on voluntary corporate disclosure via social media among the top 150 companies listed on the London Stock Exchange.Design/methodology/approachA disclosure index comprising of a set of items that encompass two facets of disclosure, namely corporate disclosure via social networks and social media sites, is developed and used. Binary logistic regression is used to test the research hypotheses.FindingsThe results of this study reveal the underlying relations between board composition and control variables as the determining factors of corporate disclosure, i.e. board size, board activism, board independence and board diversity (gender and ethnicity). The gender of the board can affect the corporate disclosure via a social network. The results of this study indicate that an increase in the number of female in the board members leads to higher corporate disclosure using social network. Moreover, firm size has a positive effect on corporate disclosure indicating that large firms tend to disclose more information on their websites and social networks.Practical implicationsThe paper provides new insights into the role played by the non-executive female directors in monitoring and controlling managerial processes related to corporate disclosure using social media.Originality/valueTo the best of the authors’ knowledge, this is the first paper that examines the role of board structure in monitoring and controlling management decisions and managerial processes in the area of corporate disclosure via social media.

scholarly journals Branding in the age of social media: how entrepreneurs use social networks to boost their service-based businesses

2017 ◽  
Vol 2 (1) ◽  
pp. 79-85 ◽  
Author(s):  
Alexandra Ioanid ◽  
Dana Corina Deselnicu ◽  
Gheorghe Militaru
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Business Development ◽  
Brand Name ◽  
Network Marketing ◽  
Online Activities ◽  
The Impact ◽  
A Company

Abstract The basics of product and service branding are generally the same. However, creating a brand for a company that offers services requires more effort because the services are hard to visualize and each client has different expectations. More than promoting any of the services, branding aims to create a perception of trust so that the brand name to be associated with integrity, quality, and innovation. The purpose of this paper is to determine how entrepreneurs develop their service-based business through social networks branding and also what are the benefits obtained from each method. The authors have researched previously what is the impact of social network marketing on business development and noticed that service-based businesses tend to obtain benefits from different activities comparing to product-based businesses. The authors researched through a semistructured questionnaire the online activities of 71 SMEs from Bucharest that offer various types of services to their customers. The results obtained are then correlated with the online and offline performance of the company.

Emotive Trending And Tracking of Tweets

2017 ◽  
Vol 7 (11) ◽  
pp. 25
Author(s):  
Sanjay Chhataru Gupta
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
The Social ◽  
The World ◽  
Complex Events

Popularity of the social media and the amount of importance given by an individual to social media has significantly increased in last few years. As more and more people become part of the social networks like Twitter, Facebook, information which flows through the social network, can potentially give us good understanding about what is happening around in our locality, state, nation or even in the world. The conceptual motive behind the project is to develop a system which analyses about a topic searched on Twitter. It is designed to assist Information Analysts in understanding and exploring complex events as they unfold in the world. The system tracks changes in emotions over events, signalling possible flashpoints or abatement. For each trending topic, the system also shows a sentiment graph showing how positive and negative sentiments are trending as the topic is getting trended.

Influence of social networks on business processes’ development

2020 ◽  
Vol 19 (12) ◽  
pp. 2225-2252
Author(s):  
E.V. Popov ◽  
V.L. Simonova ◽  
O.V. Komarova ◽  
S.S. Kaigorodova
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Business Models ◽  
Business Processes ◽  
Digital Economy ◽  
Theoretical Understanding ◽  
Sales Channels ◽  
A Value ◽  
The Impact

Subject. The emergence of new ways of interaction between sellers and buyers, the formation of new sales channels and product promotion based on the use of digital economy tools is at the heart of improving the business processes. Social networks became a tool for development; their rapid growth necessitates theoretical understanding and identification of potential application in enterprise's business process digitalization. Objectives. We explore the role of social media in the digitalization of business processes, systematize the impact of social networks on business processes of enterprises in the digital economy. Methods. The theoretical and methodological analysis of social networks as a tool for digitalization of company's business processes rests on the content analysis of domestic and foreign scientific studies, comparison, generalization and systematization. Results. We highlight the key effects of the impact of social networks on the business processes of the company; show that the digitalization of business processes should be considered in the context of a value-based approach, aimed at creating a value through the algorithmization of company operations. We determine that social networks are one of the most important tools for digitalization of company's business processes, as they have a high organizational and management potential. We also systematize the effects of social media on company's business processes. Conclusions. We present theoretical provisions of the impact of social networks on business processes of enterprises, which will enable to model and organize ideas about the development of digital ecosystems and the formation of business models.

scholarly journals Personality and the Impact of Celebrity Endorsements on Instagram

2021 ◽  
Vol 18 (3) ◽  
pp. 468-474
Author(s):  
Yair Amichai-Hamburger ◽  
Shir Etgar ◽  
Hadar Gil-Ad ◽  
Michal Levitan-Giat ◽  
Gaya Raz
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Locus Of Control ◽  
Strong Influence ◽  
Online Social Network ◽  
Strong Impact ◽  
Celebrity Endorsements ◽  
Internal Locus Of Control ◽  
The Impact ◽  
The Relationship

Celebrities are famous people who often belong to entertainment industry. They are known to have a strong influence on people’s behavior. In the digital age this impact has expanded to include the online arena. Celebrities increasingly utilize Instagram, an online social network, to promote commercial products. It is important to learn to what extent people are influenced by this type of promotion and what sort of people are likely to be swayed by it. Research has demonstrated that people’s personalities have a strong impact on their behaviors online. However, until now, these investigations have not included the relationship between personality and the degree of celebrity influence through social networks. This study examines how much the personality of a user is related to the degree to which he or she is influenced by these Celebrity Instagram messages. Participants comprised 121 students (34 males, 87 females). They answered questionnaires which focused on their personality and were asked about the degree of influence celebrities exerted upon them through Instagram. Results showed that people who are characterized as being open and having an internal locus of control are more resistant to such celebrity influences. This paper demonstrates that the personality of a recipient is likely to influence the degree of impact that a celebrity endorsement is likely to produce. The implications of these results are discussed.

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

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