scholarly journals A Scalable Cloud-based Architecture to Deploy JupyterHub for Computational Social Science Research

2021 ◽  
Author(s):  
Da Li ◽  
Robert Pyke ◽  
Runchao Jiang
Keyword(s):  
Social Science ◽  
Social Science Research ◽  
Science Research ◽  
Computational Social Science

Content Analysis

2021 ◽  
pp. 56-62
Author(s):  
Leeann Bass ◽  
Holli A. Semetko
Keyword(s):  
Content Analysis ◽  
Social Science ◽  
Research Method ◽  
Social Science Research ◽  
Science Research ◽  
Computational Social Science ◽  
Qualitative Approach ◽  
Systematic Analysis ◽  
Media Communication ◽  
Inductive Approach

This chapter explains content analysis, which is a social science research method that involves the systematic analysis of text, media, communication, or information. The source, the message, the receiver, the medium, and the influence of the message are all topics that have been studied using content analysis and in combination with other methods. There are deductive and inductive approaches to content analysis. Two widely cited studies using content analysis take a deductive approach: using predefined categories and variables based on findings and best practices from prior research. Studies taking an inductive approach to content analysis, by contrast, have an open view of the content, usually involve a small-N sample, and are often based on a qualitative approach. Meanwhile, much has been written on methods and approaches to measuring reliability with human coders. Traditional content analysis uses human coders, whereas a variety of software has emerged that can be used to download and score or code vast amounts of textual news data. The chapter then identifies key benefits and challenges associated with new computational social science tools such as text analysis.

Taming Big Data

2016 ◽  
Vol 46 (2) ◽  
pp. 189-217 ◽  
Author(s):  
Christopher A. Bail
Keyword(s):  
Social Media ◽  
Social Science ◽  
Nonprofit Organizations ◽  
Collective Behavior ◽  
Social Science Research ◽  
Science Research ◽  
Computational Social Science ◽  
New Method ◽  
Public Attention ◽  
Public Data

Social media websites such as Facebook and Twitter provide an unprecedented amount of qualitative data about organizations and collective behavior. Yet these new data sources lack critical information about the broader social context of collective behavior—or protect it behind strict privacy barriers. In this article, I introduce social media survey apps (SMSAs) that adjoin computational social science methods with conventional survey techniques in order to enable more comprehensive analysis of collective behavior online. SMSAs (1) request large amounts of public and non-public data from organizations that maintain social media pages, (2) survey these organizations to collect additional data of interest to a researcher, and (3) return the results of a scholarly analysis back to these organizations as incentive for them to participate in social science research. SMSAs thus provide a highly efficient, cost-effective, and secure method for extracting detailed data from very large samples of organizations that use social media sites. This article describes how to design and implement SMSAs and discusses an application of this new method to study how nonprofit organizations attract public attention to their cause on Facebook. I conclude by evaluating the quality of the sample derived from this application of SMSAs and discussing the potential of this new method to study non-organizational populations on social media sites as well.

scholarly journals Computational Social Science for Nonprofit Studies: Developing a Toolbox and Knowledge Base for the Field

2021 ◽  
Author(s):  
Ji Ma ◽  
Islam Akef Ebeid ◽  
Arjen de Wit ◽  
Meiying Xu ◽  
Yongzheng Yang ◽  
...  
Keyword(s):  
Social Science ◽  
Social Science Research ◽  
Science Research ◽  
Computational Social Science ◽  
Concept Representation ◽  
Nonprofit Studies ◽  
Knowledge Infrastructure ◽  
Design Perspective ◽  
Philanthropic Studies ◽  
Computationally Intensive

AbstractHow can computational social science (CSS) methods be applied in nonprofit and philanthropic studies? This paper summarizes and explains a range of relevant CSS methods from a research design perspective and highlights key applications in our field. We define CSS as a set of computationally intensive empirical methods for data management, concept representation, data analysis, and visualization. What makes the computational methods “social” is that the purpose of using these methods is to serve quantitative, qualitative, and mixed-methods social science research, such that theorization can have a solid ground. We illustrate the promise of CSS in our field by using it to construct the largest and most comprehensive database of scholarly references in our field, the Knowledge Infrastructure of Nonprofit and Philanthropic Studies (KINPS). Furthermore, we show that through the application of CSS in constructing and analyzing KINPS, we can better understand and facilitate the intellectual growth of our field. We conclude the article with cautions for using CSS and suggestions for future studies implementing CSS and KINPS.

scholarly journals Digital bioethics: introducing new methods for the study of bioethical issues

2021 ◽  
pp. medethics-2021-107387
Author(s):  
Manuel Schneider ◽  
Effy Vayena ◽  
Alessandro Blasimme
Keyword(s):  
Social Science ◽  
Data Science ◽  
Ethical Issues ◽  
Social Science Research ◽  
Science Research ◽  
Computational Social Science ◽  
Empirical Bioethics ◽  
Public Square ◽  
The Social ◽  
Bioethical Issues

The online space has become a digital public square, where individuals interact and share ideas on the most trivial to the most serious of matters, including discussions of controversial ethical issues in science, technology and medicine. In the last decade, new disciplines like computational social science and social data science have created methods to collect and analyse such data that have considerably expanded the scope of social science research. Empirical bioethics can benefit from the integration of such digital methods to investigate novel digital phenomena and trace how bioethical issues take shape online.Here, using concrete examples, we demonstrate how novel methods based on digital approaches in the social sciences can be used effectively in the domain of bioethics. We show that a digital turn in bioethics research aligns with the established aims of empirical bioethics, integrating with normative analysis and expanding the scope of the discipline, thus offering ways to reinforce the capacity of bioethics to tackle the increasing complexity of present-day ethical issues in science and technology. We propose to call this domain of research in bioethics digital bioethics.

scholarly journals Computational social science for nonprofit studies: Developing a toolbox and database for the field

2020 ◽  
Author(s):  
Ji Ma ◽  
Islam Akef Ebeid ◽  
Arjen de Wit ◽  
Meiying Xu ◽  
Yongzheng Yang ◽  
...  
Keyword(s):  
Social Science ◽  
Social Science Research ◽  
Science Research ◽  
Computational Social Science ◽  
Future Research ◽  
Concept Representation ◽  
Knowledge Infrastructure ◽  
Empirical Social Science ◽  
Philanthropic Studies ◽  
Computationally Intensive

How can computational social science (CSS) methods be applied in nonprofit and philanthropic studies? This paper summarizes and explains a range of relevant CSS methods, and highlights key applications in our field. Based on a typical design of empirical social science research, we define CSS as a set of computationally intensive empirical methods for data organization, concept representation, data analysis, and visualization. What makes the computational methods “social” is that the purpose of using these methods is to serve empirical social science research, such that theorization can have a solid ground. We illustrate the promise of CSS in our field by using it to construct the largest and most comprehensive database of scholarly references in our field so far, the Knowledge Infrastructure of Nonprofit and Philanthropic Studies (KINPS). Furthermore, we show that through the application of CSS in the analyses of the KINPS, our field’s knowledge and knowledge producing activities can be advanced, which is a core requisite for the development of our field as a discipline. We conclude the article with cautions for using CSS and suggestions for future research directions implementing CSS and the KINPS.

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