scholarly journals Studying Positive and Negative Direct and Extended Contact: Complementing Self-Reports With Social Network Analysis

2017 ◽  
Vol 43 (11) ◽  
pp. 1566-1581 ◽  
Author(s):  
Ralf Wölfer ◽  
Eva Jaspers ◽  
Danielle Blaylock ◽  
Clarissa Wigoder ◽  
Joanne Hughes ◽  
...  
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Intergroup Relations ◽  
Large Scale ◽  
Intergroup Contact ◽  
Network Analyses ◽  
Self Reports ◽  
Extended Contact ◽  
Contact Parameters

Traditionally, studies of intergroup contact have primarily relied on self-reports, which constitute a valid method for studying intergroup contact, but has limitations, especially if researchers are interested in negative or extended contact. In three studies, we apply social network analyses to generate alternative contact parameters. Studies 1 and 2 examine self-reported and network-based parameters of positive and negative contact using cross-sectional datasets ( N = 291, N = 258), indicating that both methods help explain intergroup relations. Study 3 examines positive and negative direct and extended contact using the previously validated network-based contact parameters in a large-scale, international, and longitudinal dataset ( N = 12,988), demonstrating that positive and negative direct and extended contact all uniquely predict intergroup relations (i.e., intergroup attitudes and future outgroup contact). Findings highlight the value of social network analysis for examining the full complexity of contact including positive and negative forms of direct and extended contact.

scholarly journals Social Network Analysis and Mining to Monitor and Identify Problems with Large-Scale Information and Communication Technology Interventions

PLoS ONE ◽  
2016 ◽  
Vol 11 (1) ◽  
pp. e0146220 ◽  
Author(s):  
Aleksandra do Socorro da Silva ◽  
Silvana Rossy de Brito ◽  
Nandamudi Lankalapalli Vijaykumar ◽  
Cláudio Alex Jorge da Rocha ◽  
Maurílio de Abreu Monteiro ◽  
...  
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Information And Communication Technology ◽  
Communication Technology ◽  
Large Scale ◽  
Information And Communication

Visualizing Co-Authorship Social Networks and Collaboration Recommendations With CNARe

2018 ◽  
pp. 173-188
Author(s):  
Michele A. Brandão ◽  
Matheus A. Diniz ◽  
Guilherme A. de Sousa ◽  
Mirella M. Moro
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Large Scale ◽  
Online Tool ◽  
Strength Of Ties ◽  
E Learning

Studies have analyzed social networks considering a plethora of metrics for different goals, from improving e-learning to recommend people and things. Here, we focus on large-scale social networks defined by researchers and their common published articles, which form co-authorship social networks. Then, we introduce CNARe, an online tool that analyzes the networks and present recommendations of collaborations based on three different algorithms (Affin, CORALS and MVCWalker). Through visualizations and social networks metrics, CNARe also allows to investigate how the recommendations affect the co-authorship social networks, how researchers' networks are in a central and eagle-eye context, and how the strength of ties behaves in large co-authorship social networks. Furthermore, users can upload their own network in CNARe and make their own recommendation and social network analysis.

scholarly journals Tether: A Study on Bubble-Networks

2021 ◽  
Vol 4 ◽  
Author(s):  
Giovanni Rosa ◽  
Remo Pareschi
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Large Scale ◽  
Leading Role ◽  
The Rich

Tether is a stablecoin, namely a cryptocurrency associated with an underlying security. Tether provides one of the most relevant ways to buy bitcoins and has been the centre of many controversies. In fact, it has been hypothesized that new tethers are issued without the underlying reserves, and that new massive Tether emissions are the basis of strong speculative movements on the Bitcoin, with consequent bubble effects. In the course of this article, we conduct a Social Network Analysis focused on the Tether transaction graph to identify the main actors that play a leading role on the network and characterize the transaction flow between them. From our analysis, we conclude that 1) the Tether transaction network does not enjoy the Smalltalk property, with the robustness and reliability it carries with it; 2) cryptopcurrency exchanges are the nodes with the greatest centrality; 3) even Assortativity is not found, as the subjects who move Tether on a large scale do not give continuity to their presence and operations, therefore do not get a chance to consolidate stable links between them; and 4) among the exchanges, Bitfinex, which has co-ownership and co-administration relationships with the Tether issuer, can be mostly associated with the Rich-gets-Richer property.

scholarly journals Social Networks of Lexical Innovation. Investigating the Social Dynamics of Diffusion of Neologisms on Twitter

2021 ◽  
Vol 4 ◽  
Author(s):  
Quirin Würschinger
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Dynamics ◽  
Temporal Dynamics ◽  
New Words ◽  
Network Analyses ◽  
The Social ◽  
Lexical Innovation ◽  
Social Diffusion

Societies continually evolve and speakers use new words to talk about innovative products and practices. While most lexical innovations soon fall into disuse, others spread successfully and become part of the lexicon. In this paper, I conduct a longitudinal study of the spread of 99 English neologisms on Twitter to study their degrees and pathways of diffusion. Previous work on lexical innovation has almost exclusively relied on usage frequency for investigating the spread of new words. To get a more differentiated picture of diffusion, I use frequency-based measures to study temporal aspects of diffusion and I use network analyses for a more detailed and accurate investigation of the sociolinguistic dynamics of diffusion. The results show that frequency measures manage to capture diffusion with varying success. Frequency counts can serve as an approximate indicator for overall degrees of diffusion, yet they miss important information about the temporal usage profiles of lexical innovations. The results indicate that neologisms with similar total frequency can exhibit significantly different degrees of diffusion. Analysing differences in their temporal dynamics of use with regard to their age, trends in usage intensity, and volatility contributes to a more accurate account of their diffusion. The results obtained from the social network analysis reveal substantial differences in the social pathways of diffusion. Social diffusion significantly correlates with the frequency and temporal usage profiles of neologisms. However, the network visualisations and metrics identify neologisms whose degrees of social diffusion are more limited than suggested by their overall frequency of use. These include, among others, highly volatile neologisms (e.g., poppygate) and political terms (e.g., alt-left), whose use almost exclusively goes back to single communities of closely-connected, like-minded individuals. I argue that the inclusion of temporal and social information is of particular importance for the study of lexical innovation since neologisms exhibit high degrees of temporal volatility and social indexicality. More generally, the present approach demonstrates the potential of social network analysis for sociolinguistic research on linguistic innovation, variation, and change.

scholarly journals Stakeholder Analysis and Social Network Analysis in the Decision-Making of Industrial Land Redevelopment in China: The Case of Shanghai

2020 ◽  
Vol 17 (24) ◽  
pp. 9206
Author(s):  
Wendong Wu ◽  
Fang He ◽  
Taozhi Zhuang ◽  
Yuan Yi
Keyword(s):  
Decision Making ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Public Space ◽  
Large Scale ◽  
Social Issues ◽  
Stakeholder Analysis ◽  
Industrial Land ◽  
Land Redevelopment

Currently, many large Chinese cities have entered the postindustrial era, leaving a large amount of vacant, inefficiently utilized industrial land and buildings in the inner cities. Industrial land redevelopment (ILR) can benefit cities in multiple ways, such as by increasing urban public space, improving the quality of life of citizens, and improving the environment, and is considered an effective approach to enhance people’s wellbeing. However, large-scale ILR projects often raise a series of social issues in practice, such as injustice and inequality. To address complex urban issues, ILR requires multifaceted, coordinated, and comprehensive strategies involving multitudinous stakeholders. A profound understanding of diverse stakeholders in the decision-making of ILR is a vital step in enhancing the sustainability of ILR. The aim of this paper is to use Shanghai as a case study to understand the diverse stakeholders and their participation during the decision-making of ILR in China. Interviews and questionnaires were used to collect data. Stakeholder analysis (SA) and social network analysis (SNA) were used as complementary research methodologies in this paper. First, stakeholders who participated in the decision-making of ILR were identified. Then, the characteristics of various stakeholders, including power, interests, and knowledge, were analyzed. Following this, the interactive relationships among stakeholders were explored, and their network structure was examined. Finally, policy recommendations were presented regarding stakeholder participation problems in the decision-making of ILR in China.

The Rise of Fashion Informatics: A Case of Data-Mining-Based Social Network Analysis in Fashion

2018 ◽  
Vol 37 (2) ◽  
pp. 87-102 ◽  
Author(s):  
Li Zhao ◽  
Chao Min
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Large Scale ◽  
Dynamic Network ◽  
Fashion Industry ◽  
Cognitive Computing ◽  
Data Set

With the advent of modern cognitive computing technologies, fashion informatics researchers contribute to the academic and professional discussion about how a large-scale data set is able to reshape the fashion industry. Data-mining-based social network analysis is a promising area of fashion informatics to investigate relations and information flow among fashion units. By adopting this pragmatic approach, we provide dynamic network visualizations of the case of Paris Fashion Week. Three time periods were researched to monitor the formulation and mobilization of social media users’ discussions of the event. Initial textual data on social media were crawled, converted, calculated, and visualized by Python and Gephi. The most influential nodes (hashtags) that function as junctions and the distinct hashtag communities were identified and represented visually as graphs. The relations between the contextual clusters and the role of junctions in linking these clusters were investigated and interpreted.

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