Network Redundancy and Information Diffusion: The Impacts of Information Redundancy, Similarity, and Tie Strength

2016 ◽  
Vol 46 (2) ◽  
pp. 250-272 ◽  
Author(s):  
Hai Liang ◽  
King-wa Fu
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Information Diffusion ◽  
Tie Strength ◽  
Information Redundancy ◽  
Ego Networks ◽  
Core Concepts ◽  
The Impact

It remains controversial whether community structures in social networks are beneficial or not for information diffusion. This study examined the relationships among four core concepts in social network analysis—network redundancy, information redundancy, ego-alter similarity, and tie strength—and their impacts on information diffusion. By using more than 6,500 representative ego networks containing nearly 1 million following relationships from Twitter, the current study found that (1) network redundancy is positively associated with the probability of being retweeted even when competing variables are controlled for; (2) network redundancy is positively associated with information redundancy, which in turn decreases the probability of being retweeted; and (3) the inclusion of both ego-alter similarity and tie strength can attenuate the impact of network redundancy on the probability of being retweeted.

scholarly journals Measuring the value of the social network: analysis, quantitative approach, and application

2015 ◽  
Author(s):  
Sujata Jindal ◽  
Ritu Sindhu
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Impact ◽  
Quantitative Approach ◽  
Social Data ◽  
The Social ◽  
The Impact ◽  
Day By Day

Social networks are growing day by day. Users of the social networks are generating values for these networks. All the users can’t be considered equal as they have different social network impact value. In this paper we analyze the social impact of a user and propose a method to estimate an individual’s worth to a social network in terms of impact. The mathematical evaluations show the effectiveness of our method. Based on the proposed method many applications can be built taking into consideration the impact any individual’s social profile has. We have tried to make various social data attributes more valuable and meaningful.

Towards a new methodological approach to social historiography of translation

2019 ◽  
Vol 8 (2) ◽  
pp. 231-256
Author(s):  
Nasrin Ashrafi ◽  
Mohammad Reza Hashemi ◽  
Hossein Akbari
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Methodological Approach ◽  
Structuration Theory ◽  
Literary Translation ◽  
Analysis Techniques ◽  
The Impact

Abstract In an attempt to appreciate the contribution that social network analysis (SNA) might offer to translation historiography, two main approaches are presented and discussed in this study: explanatory SNA and exploratory SNA. The former is more concerned with SNA measures while the latter deals with three potential narratives of social networks. The aim is to employ SNA in diachronic and synchronic dimensions of literary translation publishing historiography in Iran from 1991 to 2010, a micro-macro framework that seamlessly integrates agents’ relationships, visualization and network analysis techniques to explore the impact of ideological-political shifts on the quantity as well as quality of major agents’ relations. Furthermore, the study attempts to explore how the synergy between Giddens’ Structuration Theory (GST) and SNA can support a deeper and more empirically grounded understanding of translation historiography. The goal of the study is both methodological and scientific. The results of SNA graphical outputs suggest that there is a significant relationship between the structure of relationships in fiction publishing field and the dominant political discourse in Iran.

Contemporary Gold Rush or Scientific Advancement

2015 ◽  
pp. 210-226
Author(s):  
Darren Quinn ◽  
Liming Chen ◽  
Maurice Mulvenna
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Online Social Networks ◽  
Interaction Analysis ◽  
Rapid Evolution ◽  
Gold Rush ◽  
Community Interactions ◽  
The Impact

Following the expansion and mass adoption of Online Social Networks, the impact upon the domain of Social Network Analysis has been a rapid evolution in terms of approach, developing sophisticated methods to capture and understand individual and community interactions. This chapter provides a comprehensive review, examining state-of-the-art Social Network Analysis research and practices, highlighting key trends within the domain. In section 1, the authors examine the growing awareness concerning data as a marketable and scientific commodity. Section 2 reviews the context of Online Social Networking, highlighting key approaches for analysing Online Social Networks. In section 3, they consider modelling motivations of networks, discussing models in line with tie formation approaches. Section 4 outlines data collection approaches along with common structural properties observed in related literature. The authors discuss future directions and emerging approaches, notably semantic social networks and social interaction analysis before conclusions are provided.

scholarly journals Seeing change in urban informal settlements with social network analysis

2020 ◽  
pp. 095624782095375
Author(s):  
Eric Kasper
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Action Research ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Community Organizing ◽  
Informal Settlements ◽  
Urban Informal Settlements ◽  
Relationship Structures ◽  
The Impact

This paper examines the changes to social networks of people living in seven informal settlements in Raipur, India, who, in line with the “Indian Alliance” model of community organizing, worked with NGO partners to form local associations in their settlements. These associations were meant to help the participants and their fellow settlement residents to access more secure housing through the Rajiv Awas Yojana (RAY) policy. This paper presents findings from a quantitative social network analysis, demonstrating the impact of the organizing efforts in reshaping their relationship structures and strengthening their agency. These findings were tested for resonance and further fleshed out with qualitative details by going through the analysis with participants. Finally, this paper offers reflections on incorporating technical research methods into organizing and action research interventions, affirming the notion that people living in informal settlements are well placed to generate and make use of sophisticated data on their own communities and cities.

scholarly journals Measuring the value of the social network: analysis, quantitative approach, and application

2015 ◽  
Author(s):  
Sujata Jindal ◽  
Ritu Sindhu
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Impact ◽  
Quantitative Approach ◽  
Social Data ◽  
The Social ◽  
The Impact ◽  
Day By Day

Social networks are growing day by day. Users of the social networks are generating values for these networks. All the users can’t be considered equal as they have different social network impact value. In this paper we analyze the social impact of a user and propose a method to estimate an individual’s worth to a social network in terms of impact. The mathematical evaluations show the effectiveness of our method. Based on the proposed method many applications can be built taking into consideration the impact any individual’s social profile has. We have tried to make various social data attributes more valuable and meaningful.

scholarly journals Combining social network analysis and agent-based modelling to explore dynamics of human interaction: A review

2020 ◽  
Vol 2 ◽  
pp. 16325 ◽  
Author(s):  
Meike Will ◽  
Jürgen Groeneveld ◽  
Karin Frank ◽  
Birgit Müller
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Human Interaction ◽  
Extensive Literature ◽  
Agent Based Models ◽  
Agent Based ◽  
Agent Based Modelling ◽  
The Impact

Agent-based modelling (ABM) and social network analysis (SNA) are both valuable tools for exploring the impact of human interactions on a broad range of social and ecological patterns. Integrating these approaches offers unique opportunities to gain insights into human behaviour that neither the evaluation of social networks nor agent-based models alone can provide. There are many intriguing examples that demonstrate this potential, for instance in epidemiology, marketing or social dynamics. Based on an extensive literature review, we provide an overview on coupling ABM with SNA and evaluating the integrated approach. Building on this, we identify current shortcomings in the combination of the two methods. The greatest room for improvement is found with regard to (i) the consideration of the concept of social integration through networks, (ii) an increased use of the co-evolutionary character of social networks and embedded agents, and (iii) a systematic and quantitative model analysis focusing on the causal relationship between the agents and the network. Furthermore, we highlight the importance of a comprehensive and clearly structured model conceptualization and documentation. We synthesize our findings in guidelines that contain the main aspects to consider when integrating social networks into agent-based models.

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.

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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