scholarly journals Books in Internet Recommendations (based on the video in Tik-Tok)

2021 ◽  
Vol 5 (4) ◽  
pp. 31-45
Author(s):  
Sofia Vladimirovna Kushpil ◽  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Love Stories

Despite the traditional opposition of reading literature and the active use of social networks, the latter are becoming a source of reading recommendations. This article discusses publications under the hashtag #bookselection on the TikTok social network and analyzes thematic profiles. Most authors create their posts to recommend books and 17%, on the contrary, write about unsuccessful, in their opinion, books. The most common genres of literature in TikTok video are fantasy (34%), love stories (27%) and life novels (21%). We also analyzed the reaction of users (according to the comments they left) to such videos in their recommendations, 82% of comments are positive. People are ready to share their opinions on books and read books on such video recommendations.

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.

Social Networks and Business Performance of Igbo Migrant Traders in Ibadan, South-West Nigeria

2014 ◽  
Vol 12 (2) ◽  
Author(s):  
Deborah O. Obor ◽  
Emeka E. Okafor
Keyword(s):  
Social Support ◽  
Social Networks ◽  
Social Network ◽  
Social Exchange ◽  
Business Performance ◽  
Exploratory Research ◽  
Business Growth ◽  
South West ◽  
Key Informant ◽  
Depth Interviews

This study focused on social networks and business performance among Igbo businessmen in Ibadan, South-west Nigeria through the exploratory research design. Social exchange, social network and social capital theories were employed as theoretical framework. Twenty-six in-depth interviews, key informant interviews and case studies were conducted with purposively selected respondents in four business locations in Ibadan. The results showed that among the factors that facilitated migration of the Igbo to Ibadan were their interest to learn a trade, their inability to attain higher education, and having a relative in Ibadan. The types of social networks available showed that social network was not location bound, as all the respondents belonged to town progressive unions and mutual benefits/cooperative associations. Social networks played vital roles in business performance, including social support, access to loan, business growth and expansion. The main challenges to maintaining adequate social network in business were distrust, envy, unbridled competition, dishonesty and inability to keep terms of agreement. The study concludes that social networks have positively influenced the business performance of migrant Igbo in Ibadan. There is need for the Igbo to strengthen their social networks through honesty, forthrightness, and transparency in all their dealings.

Networks

2020 ◽  
pp. 534-562
Author(s):  
Matthew O. Jackson ◽  
Brian W. Rogers ◽  
Yves Zenou
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Statistical Analysis ◽  
Labor Markets ◽  
Network Formation ◽  
Labor Market Outcomes ◽  
Negative Consequences ◽  
Social Scientists ◽  
Market Outcomes

What is the role of social networks in driving persistent differences between races and genders in education and labor market outcomes? What is the role of homophily in such differences? Why is such homophily seen even if it ends up with negative consequences in terms of labor markets? This chapter discusses social network analysis from the perspective of economics. The chapter is organized around the theme of externalities: the effects that one’s behavior has on others’ welfare. Externalities underlie the interdependencies that make networks interesting to social scientists. This chapter discusses network formation, as well as interactions between people’s behaviors within a given network, and the implications in a variety of settings. Finally, the chapter highlights some empirical challenges inherent in the statistical analysis of network-based data.

Introduction

2020 ◽  
pp. xxii-13
Author(s):  
Ryan Light ◽  
James Moody
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Bibliometric Analysis ◽  
Social Life ◽  
New Directions ◽  
Central Paradigm

This chapter provides an introduction to this volume on social networks. It argues that social network analysis is greater than a method or data, but serves as a central paradigm for understanding social life. The chapter offers evidence of the influence of social network analysis with a bibliometric analysis of research on social networks. This analysis underscores how pervasive network analysis has become and highlights key theoretical and methodological concerns. It also introduces the sections of the volume broadly structured around theory, methods, broad conceptualizations like culture and temporality, and disciplinary contributions. The chapter concludes by discussing several promising new directions in the field of social network analysis.

The Oxford Handbook of Social Networks

2020 ◽  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Life ◽  
Information Science ◽  
Computational Social Science ◽  
The Social ◽  
Diverse Field ◽  
The Rich

Social networks fundamentally shape our lives. Networks channel the ways that information, emotions, and diseases flow through populations. Networks reflect differences in power and status in settings ranging from small peer groups to international relations across the globe. Network tools even provide insights into the ways that concepts, ideas and other socially generated contents shape culture and meaning. As such, the rich and diverse field of social network analysis has emerged as a central tool across the social sciences. This Handbook provides an overview of the theory, methods, and substantive contributions of this field. The thirty-three chapters move through the basics of social network analysis aimed at those seeking an introduction to advanced and novel approaches to modeling social networks statistically. The Handbook includes chapters on data collection and visualization, theoretical innovations, links between networks and computational social science, and how social network analysis has contributed substantively across numerous fields. As networks are everywhere in social life, the field is inherently interdisciplinary and this Handbook includes contributions from leading scholars in sociology, archaeology, economics, statistics, and information science among others.

Comparing the social networks of service users with long term mental health needs living in community with those in a general adult in-patient unit

2021 ◽  
pp. 002076402110175
Author(s):  
Roberto Rusca ◽  
Ike-Foster Onwuchekwa ◽  
Catherine Kinane ◽  
Douglas MacInnes
Keyword(s):  
Mental Health ◽  
Social Networks ◽  
Social Network ◽  
Patient Group ◽  
Mental Health Problems ◽  
Well Being ◽  
Health Problems ◽  
Practical Support ◽  
The Social

Background: Relationships are vital to recovery however, there is uncertainty whether users have different types of social networks in different mental health settings and how these networks may impact on users’ wellbeing. Aims: To compare the social networks of people with long-term mental illness in the community with those of people in a general adult in-patient unit. Method: A sample of general adult in-patients with enduring mental health problems, aged between 18 and 65, was compared with a similar sample attending a general adult psychiatric clinic. A cross-sectional survey collected demographic data and information about participants’ social networks. Participants also completed the Short Warwick Edinburgh Mental Well-Being Scale to examine well-being and the Significant Others Scale to explore their social network support. Results: The study recruited 53 participants (25 living in the community and 28 current in-patients) with 339 named as important members of their social networks. Both groups recorded low numbers in their social networks though the community sample had a significantly greater number of social contacts (7.4 vs. 5.4), more monthly contacts with members of their network and significantly higher levels of social media use. The in-patient group reported greater levels of emotional and practical support from their network. Conclusions: People with serious and enduring mental health problems living in the community had a significantly greater number of people in their social network than those who were in-patients while the in-patient group reported greater levels of emotional and practical support from their network. Recommendations for future work have been made.

scholarly journals Challenging the Status Quo through Social Influence: Changes in Sustainable Consumption through the Influence of Social Networks

2021 ◽  
Vol 13 (10) ◽  
pp. 5513
Author(s):  
Iljana Schubert ◽  
Judith I. M. de Groot ◽  
Adrian C. Newton
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Influence ◽  
Food Consumption ◽  
Status Quo ◽  
Injunctive Norms ◽  
Sustainable Food ◽  
Consumption Choices ◽  
The Status ◽  
Consumption Practices

This study examines the influence of social network members (versus strangers) on sustainable food consumption choices to investigate how social influence can challenge the status quo in unsustainable consumption practices. We hypothesized that changes to individual consumption practices could be achieved by revealing ‘invisible’ descriptive and injunctive social norms. We further hypothesized that it matters who reveals these norms, meaning that social network members expressing their norms will have a stronger influence on other’s consumption choices than if these norms are expressed by strangers. We tested these hypotheses in a field experiment (N = 134), where participants discussed previous sustainable food consumption (revealing descriptive norms) and its importance (revealing injunctive norms) with either a stranger or social network member. We measured actual sustainable food consumption through the extent to which participants chose organic over non-organic consumables during the debrief. Findings showed that revealed injunctive norms significantly influenced food consumption, more so than revealed descriptive norms. We also found that this influence was stronger for social network members compared to strangers. Implications and further research directions in relation to how social networks can be used to evoke sustainable social change are discussed.

scholarly journals The switching mechanisms of social network densification

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Teruyoshi Kobayashi ◽  
Mathieu Génois
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Context ◽  
Social Interactions ◽  
Regime Switching ◽  
Face To Face ◽  
The Social ◽  
Switching Property ◽  
Distinctive Type ◽  
Insight Into

AbstractDensification and sparsification of social networks are attributed to two fundamental mechanisms: a change in the population in the system, and/or a change in the chances that people in the system are connected. In theory, each of these mechanisms generates a distinctive type of densification scaling, but in reality both types are generally mixed. Here, we develop a Bayesian statistical method to identify the extent to which each of these mechanisms is at play at a given point in time, taking the mixed densification scaling as input. We apply the method to networks of face-to-face interactions of individuals and reveal that the main mechanism that causes densification and sparsification occasionally switches, the frequency of which depending on the social context. The proposed method uncovers an inherent regime-switching property of network dynamics, which will provide a new insight into the mechanics behind evolving social interactions.

scholarly journals Discovering the maximum k-clique on social networks using bat optimization algorithm

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Akram Khodadadi ◽  
Shahram Saeidi
Keyword(s):  
Genetic Algorithm ◽  
Social Networks ◽  
Social Network ◽  
Coding Theory ◽  
Evaluation Criteria ◽  
Convergence Speed ◽  
Optimization Approach ◽  
Computational Results ◽  
Complete Subgraph ◽  
Np Complete

AbstractThe k-clique problem is identifying the largest complete subgraph of size k on a network, and it has many applications in Social Network Analysis (SNA), coding theory, geometry, etc. Due to the NP-Complete nature of the problem, the meta-heuristic approaches have raised the interest of the researchers and some algorithms are developed. In this paper, a new algorithm based on the Bat optimization approach is developed for finding the maximum k-clique on a social network to increase the convergence speed and evaluation criteria such as Precision, Recall, and F1-score. The proposed algorithm is simulated in Matlab® software over Dolphin social network and DIMACS dataset for k = 3, 4, 5. The computational results show that the convergence speed on the former dataset is increased in comparison with the Genetic Algorithm (GA) and Ant Colony Optimization (ACO) approaches. Besides, the evaluation criteria are also modified on the latter dataset and the F1-score is obtained as 100% for k = 5.

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