scholarly journals Birds of a Feather Flock Together and Opposites Attract: The Nonlinear Relationship Between Personality and Friendship

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Haiyan Liu ◽  
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Relations ◽  
Research Question ◽  
Psychological Research ◽  
Personality Psychology ◽  
Analysis Techniques ◽  
Social Network Data ◽  
Social Entities

Whether birds of a feather flock together or opposites attract is a classical research question in social and personality psychology. In most existing studies, correlation-based techniques are commonly used to study the similarity/dissimilarity among social entities. Social network data comprises two primary components: actors and the possible social relations between them. It, therefore, has observations on both the dyads with and without social relations. Because of the availability of the baseline group (dyads without social relations), it is possible to contrast the two groups of dyads using social network analysis techniques. This study aims to illustrate how to use social network analysis techniques to address psychological research questions. Specifically, we will investigate how the similarity or dissimilarity of actor's characteristics relates to the likelihood for them to build social relations. By analyzing a college friendship network, we found the quadratic relations between personality similarity and friendship. Both very similar and very dissimilar personalities boost friendship among college students.

An Introduction to Social Network Analysis for Personality and Social Psychologists

2017 ◽  
Vol 8 (4) ◽  
pp. 442-453 ◽  
Author(s):  
Allan Clifton ◽  
Gregory D. Webster
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Groups ◽  
Network Data ◽  
Personality Psychology ◽  
Social Network Data ◽  
And Behavior

Social network analysis (SNA) is a methodology for studying the connections and behavior of individuals within social groups. Despite its relevance to social and personality psychology, SNA has been underutilized in these fields. We first examine the paucity of SNA research in social and personality journals. Next we describe methodological decisions that must be made before collecting social network data, with benefits and drawbacks for each. We discuss common SNAs and give an overview of software available for SNA. We provide examples from the literature of SNA for both one-mode and two-mode network data. Finally, we make recommendations to researchers considering incorporating SNA into their research.

scholarly journals Women and universities in El País (1977-2011): A methodological proposal for use of the ITCS for historical analysis

2019 ◽  
Vol 8 (1) ◽  
pp. 009
Author(s):  
Carlos G. Figuerola ◽  
Tamar Groves ◽  
Francisco J. Rodríguez
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Text Mining ◽  
Historical Analysis ◽  
Historical Research ◽  
Software Systems ◽  
Practical Case ◽  
Computer Tools ◽  
Analysis Techniques

The practice of historical research in recent years has been substantially affected by the emergence of the so-called digital humanities. New computer tools have been appearing, software systems capable of processing vast quantities of information in ways that until recently were inconceivable. Text mining and social network analysis techniques are sophisticated instruments that can help render a more enriching reading of the available data and draw useful conclusions. We reflect on this in the first part of this article, and then apply these tools to a practical case: quantifying and identifying the women who appear in university-related articles in the newspaper El País from its founding until 2011.

Social Network Analysis in Marketing

E-Marketing ◽  
2012 ◽  
pp. 185-197
Author(s):  
Przemyslaw Kazienko ◽  
Piotr Doskocz ◽  
Tomasz Kajdanowicz
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Relationships ◽  
Network Data ◽  
Social Network Data ◽  
The Social ◽  
Targeted Marketing ◽  
Potential Customers ◽  
Demographic Features

The chapter describes a method how to perform a classification task without any demographic features and based only on the social network data. The concept of such collective classification facilitates to identify potential customers by means of services used or products purchased by the current customers, i.e. classes they belong to as well as using social relationships between the known and potential customers. As a result, a personalized offer can be prepared for the new clients. This innovative marketing method can boost targeted marketing campaigns.

Social Network Analysis in Marketing

2011 ◽  
pp. 271-283
Author(s):  
Przemyslaw Kazienko ◽  
Piotr Doskocz ◽  
Tomasz Kajdanowicz
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Relationships ◽  
Network Data ◽  
Social Network Data ◽  
The Social ◽  
Targeted Marketing ◽  
Potential Customers ◽  
Demographic Features

The chapter describes a method how to perform a classification task without any demographic features and based only on the social network data. The concept of such collective classification facilitates to identify potential customers by means of services used or products purchased by the current customers, i.e. classes they belong to as well as using social relationships between the known and potential customers. As a result, a personalized offer can be prepared for the new clients. This innovative marketing method can boost targeted marketing campaigns.

Network Perspectives on the Past: Tackling the Challenges

2016 ◽  
Author(s):  
Tom Brughmans ◽  
Anna Collar
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Relations ◽  
Social Life ◽  
Academic Career ◽  
Special Method ◽  
The Past ◽  
Full Diversity ◽  
Strength Of Weak Ties

As his keynote address to the 1990 Sunbelt Social Networks conference, Mark Granovetter presented a paper entitled ‘The Myth of Social Network Analysis as a Special Method in the Social Sciences’ (Granovetter 1990). In it, he described how the popular social network theory he proposed, ‘The Strength of Weak Ties’ (Granovetter 1973), was like a spectre that haunted his academic career: although he subsequently pursued other research interests, he found that ‘as I got more deeply into any subject, network ideas kept coming in the back door’. He concluded that social network analysis (SNA) is not a ‘special’ method in social science, because ‘no part of social life can be properly analysed without seeing how it is fundamentally embedded in networks of social relations’ (Granovetter 1990: 15). However, he noted that to many, SNA is an alien concept: ‘we need to remember that there are many scholars outside the house of social network analysis who think in a relational way but don’t see the kinship with network methods and ideas’ (Granovetter 1990: 15). This observation echoes the current position of network studies in archaeology and history. Few would argue that relationships between social entities are not important for understanding past social processes. However, more explicit application of network theories and methods is not yet a mainstream part of our disciplines. Although it is the case that some researchers are not aware of the advantages such perspectives might offer, the current ‘niche’ status of network applications in archaeological and historical research relates to a more general misperception: that network concepts and methodologies per se are simply not appropriate for use in research in these disciplines. This volume aims to address both issues: the contributions in this volume demonstrate both the enormous potential of network methodologies, and also—and perhaps more importantly—acknowledge and address a range of perceived problems and reservations relating to the application of network perspectives to the study of the past, thereby encouraging and enabling their wider use in archaeology and history. The full diversity of network perspectives has only been introduced in our disciplines relatively recently.

Finding Automated (Bot, Sensor) or Semi-Automated (Cyborg) Social Media Accounts Using Network Analysis and NodeXL Basic

2017 ◽  
pp. 383-424 ◽  
Author(s):  
Shalin Hai-Jew
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Research Question ◽  
Other Information ◽  
The Face ◽  
Research Findings ◽  
Levels Of Automation ◽  
Information Streams

Various research findings suggest that humans often mistake social robot (‘bot) accounts for human in a microblogging context. The core research question here asks whether the use of social network analysis may help identify whether a social media account is fully automated, semi-automated, or fully human (embodied personhood)—in the contexts of Twitter and Wikipedia. Three hypotheses are considered: that automated social media account networks will have less diversity and less heterophily; that automated social media accounts will tend to have a botnet social structure, and that cyborg accounts will have select features of human- and robot- social media accounts. The findings suggest limited ability to differentiate the levels of automation in a social media account based solely on social network analysis alone in the face of a determined and semi-sophisticated adversary given the ease of network account sock-puppetry but does suggest some effective detection approaches in combination with other information streams.

scholarly journals Research on China’s Green Finance Policies Based on Text Mining

2020 ◽  
Vol 185 ◽  
pp. 02024
Author(s):  
Yuqing Liao ◽  
Jingliang Chen
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
High Frequency ◽  
Policy Documents ◽  
Analysis Techniques ◽  
The Past ◽  
Word Cloud ◽  
Green Finance ◽  
Occurrence Matrix

Based on the green finance policies in China from 2017 to 2019, this paper extracts feature and high-frequency words from policy documents, uses word cloud diagram, co-occurrence matrix and social network analysis techniques to quantitatively analyse the information contained in the green finance policies over the past three years and highlights the hot issues in question, thus providing a multi-layered and wideranging pathway for facilitating the orderly development of green finance industries across China.

Do clusters create shared value? A social network analysis of the motor valley case

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fernando G. Alberti ◽  
Federica Belfanti
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Impact ◽  
Statistical Significance ◽  
Research Question ◽  
Quadratic Assignment ◽  
Shared Value ◽  
Content Type ◽  
Cluster Development

Purpose This paper aims to contribute to the debate about creating shared value (CSV) and clusters, by shedding light on how clusters might generate shared value, i.e. cause social and business benefits, hence focusing on the following research question “do clusters create shared value?” Design/methodology/approach The study relied on social network analysis methods and techniques. Data have been collected from both primary and secondary sources, in the empirical context of the Motor Valley cluster in Emilia-Romagna. The authors computed three independent and four dependent variables to operationalize the concept of cluster development and shared value creation. A multiple regression quadratic assignment procedure and, more specifically, the most accurate model of that procedure, that is the double semi-partialling method, has been carried out to answer the research question. Finally, empirical evidence has been complemented with other cluster-level data recently collected by the Italian Cluster Mapping project. Findings The findings confirm how the development of the Motor Valley cluster in Emilia-Romagna contributed to the creation of economic and social growth opportunities for all the actors. The study shows that clusters do create shared value and the chosen cluster development variables do explain much of the business and social impact variables at a very high statistical significance level. Originality/value The paper contributes to the under-explored research on clusters and CSV with a very first attempt in providing quantitative evidence of the phenomenon.

Application of Computer System in the Study of Sociology

2014 ◽  
Vol 926-930 ◽  
pp. 1680-1683
Author(s):  
Ying Ming Xu ◽  
Shu Juan Jin
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Computer Algorithms ◽  
Useful Knowledge ◽  
Social Network Data ◽  
Analysis Task ◽  
Original Analysis ◽  
The Social ◽  
The Relationship

With the development of information technology, more and more data about social to be collected. If we can analyze them effectively, it will help people to understand sociological understanding, promoting the development of social science. But the increasing amount of data and analysis to put forward a huge challenge. Now the social networks have already surpassed the processing ability of the original analysis means, must use a more effective tool to complete the analysis task. The computer as a way of helping people from massive data to find the potential useful knowledge tools, play an important role in many fields. Social network analysis, also known as link mining, refers to the handling of the relationship between social network data in the computer method. In this paper, the methods of computer and the social network analysis was introduced in this paper and the computer algorithms are summarized in the application of social network analysis.

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