Towards Computational and Behavioral Social Science

2016 ◽  
Vol 21 (2) ◽  
pp. 131-140 ◽  
Author(s):  
Rosaria Conte ◽  
Francesca Giardini
Keyword(s):  
Big Data ◽  
Social Science ◽  
Data Science ◽  
Behavioral Science ◽  
Big Data Analytics ◽  
Computational Social Science ◽  
Evolution Of Cooperation ◽  
Social Phenomena ◽  
Integrative View ◽  
Social Data Mining

Abstract. In the last few years, the study of social phenomena has hosted a renewal of interest in Computational Social Science (CSS). While this field is not new – Axelrod’s first computational work on the evolution of cooperation goes back to 1981 – CSS has recently resurged under the pressure of quantitative social science and the application of Big Data analytics to social datasets. However, Big Data is no panacea and the data deluge that it provides raises more questions than it answers. The aim of this paper is to present an overview in which CSS will be introduced and the costs of CSS will be balanced against its benefits, in an attempt to propose an integrative view of the new and the old practice of CSS. In particular, two routes to integration will be drawn. First, it will be advocated that social data mining and computational modeling need to be integrated. Second, we will introduce the generative approach, aimed to understand how social phenomena can be generated starting from the micro-components, including psychological mechanisms, and we will discuss the necessity of combining it with the anticipatory, data-driven objective. By these means, Computational Social Science will develop into a more comprehensive field of Computational Social and Behavioral Science in which data science, ICT, as well as the behavioral and social sciences will be fruitfully integrated.

scholarly journals SAMFUNDSVIDENSKABERNE OG DE STORE DATA

2020 ◽  
pp. 16
Author(s):  
My Madsen ◽  
Katinka Schyberg
Keyword(s):  
Big Data ◽  
Social Science ◽  
Data Science ◽  
Computational Social Science ◽  
Social Data

I dette interview diskuterer tre samfundsvidenskabeligere forskere fra CSS – lektor i sociologi Anders Blok, professor i statskundskab Rebecca Adler-Nissen og professor i antropologi Morten Axel Pedersen – deres erfaringer med og tanker om det at arbejde med Big Data, „computational social science“, heterogene datatyper og tværfagligt samarbejde i forbindelse med projekter på Copenhagen Center for Social Data Science, SODAS

scholarly journals Big Data, Data Science and Emerging Analytic tools : Impact in social science

2019 ◽  
Author(s):  
Satabdi Saha ◽  
Tapabrata Maiti
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Social Science ◽  
Data Analytics ◽  
Data Science ◽  
Social Science Research ◽  
Big Data Analytics ◽  
Science Research ◽  
Data Sources ◽  
The Internet

Rapid advancement of the Internet and Internet of Things have led to companies generating gigantic volumes of data in every field of business. Big data research has thus become one of the most prominent topic of discussion garnering simultaneous attention from academia and industry. This paper attempts to understand the significance of big data in current scientific research and outline its unique characteristics, otherwise unavailable from traditional data sources. We focus on how big data has altered the scope and dimension of data science thus making it severely interdisciplinary. We further discuss the significance and opportunities of big data in the domain of social science research with a scrutiny of the challenges previously faced while using smaller datasets. Given the extensive utilization of big data analytics in all forms of socio-technical research, we argue the need to critically interrogate its assumptions and biases; thereby advocating the need for creating a just and ethical big data world.

scholarly journals Multi Objective Optimization Based Feature Selection Algorithms for Big Data Analytics: A Review

2021 ◽  
pp. 1-4
Author(s):  
Aakriti Shukla ◽  
◽  
Dr Damodar Prasad Tiwari ◽  
Keyword(s):  
Feature Selection ◽  
Big Data ◽  
Data Science ◽  
Big Data Analytics ◽  
Data Sets ◽  
Big Data Applications ◽  
High Workload ◽  
Long Time ◽  
Analytical Result ◽  
Result Analysis

Dimension reduction or feature selection is thought to be the backbone of big data applications in order to improve performance. Many scholars have shifted their attention in recent years to data science and analysis for real-time applications using big data integration. It takes a long time for humans to interact with big data. As a result, while handling high workload in a distributed system, it is necessary to make feature selection elastic and scalable. In this study, a survey of alternative optimizing techniques for feature selection are presented, as well as an analytical result analysis of their limits. This study contributes to the development of a method for improving the efficiency of feature selection in big complicated data sets.

scholarly journals Teaching Computational Social Science Skills to Psychology Students: An Undergraduate Research Lab Case Study

2020 ◽  
Vol 4 (1) ◽  
pp. 5-14
Author(s):  
Brian A. Eiler ◽  
◽  
Patrick C. Doyle ◽  
Rosemary L. Al-Kire ◽  
Heidi A. Wayment ◽  
...  
Keyword(s):  
Social Science ◽  
Data Science ◽  
Undergraduate Research ◽  
Psychology Students ◽  
Psychological Research ◽  
Open Science ◽  
Computational Social Science ◽  
Research Experience ◽  
Science Practices

This article provides a case study of a student-focused research experience that introduced basic data science skills and their utility for psychological research, providing practical learning experiences for students interested in learning computational social science skills. Skills included programming; acquiring, visualizing, and managing data; performing specialized analyses; and building knowledge about open-science practices.

Data Science and Big Data Analytics in Financial Services

Web Services ◽  
2019 ◽  
pp. 1301-1329
Author(s):  
Suren Behari ◽  
Aileen Cater-Steel ◽  
Jeffrey Soar
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Financial Services ◽  
Data Science ◽  
Big Data Analytics ◽  
Disruptive Innovation ◽  
Big Data Analysis ◽  
Future Trends ◽  
The Right

The chapter discusses how Financial Services organizations can take advantage of Big Data analysis for disruptive innovation through examination of a case study in the financial services industry. Popular tools for Big Data Analysis are discussed and the challenges of big data are explored as well as how these challenges can be met. The work of Hayes-Roth in Valued Information at the Right Time (VIRT) and how it applies to the case study is examined. Boyd's model of Observe, Orient, Decide, and Act (OODA) is explained in relation to disruptive innovation in financial services. Future trends in big data analysis in the financial services domain are explored.

Analytics-as-a-Service (AaaS)

Web Services ◽  
2019 ◽  
pp. 1262-1281
Author(s):  
Chitresh Verma ◽  
Rajiv Pandey
Keyword(s):  
Big Data ◽  
Case Studies ◽  
Data Analytics ◽  
Data Science ◽  
Business Processes ◽  
Service Oriented Architecture ◽  
Big Data Analytics ◽  
Major Branch ◽  
Implementation Level ◽  
Industrial Level

Big Data Analytics is a major branch of data science where the huge amount raw data is processed to get insight for relevant business processes. Integration of big data, its analytics along with Service Oriented Architecture (SOA) is need of the hour, such integration shall render reusability and scalability to various business processes. This chapter explains the concept of Big Data and Big Data Analytics at its implementation level. The Chapter further describes Hadoop and its technologies which are one of the popular frameworks for Big Data Analytics and envisage integrating SOA with relevant case studies. The chapter demonstrates the SOA integration with Big Data through, two case studies of two different scenarios are incorporated that integrates real world implementation with theory and enables better understanding of the industrial level processes and practices.

An Introduction to Computational Social Science for Organizational Communication

2018 ◽  
pp. 184-200
Author(s):  
Andrew N. Pilny ◽  
Marshall Scott Poole
Keyword(s):  
Big Data ◽  
Social Science ◽  
Organizational Communication ◽  
Computational Social Science ◽  
Lessons Learned ◽  
Audit Culture ◽  
Research Designs ◽  
Data Driven Approach ◽  
Interaction Modeling ◽  
The Relationship

The exponential growth of “Big Data” has given rise to a field known as computational social science (CSS). The authors view CSS as the interdisciplinary investigation of society that takes advantage of the massive amount of data generated by individuals in a way that allows for abductive research designs. Moreover, CSS complicates the relationship between data and theory by opening the door for a more data-driven approach to social science. This chapter will demonstrate the utility of a CSS approach using examples from dynamic interaction modeling, machine learning, and network analysis to investigate organizational communication (OC). The chapter concludes by suggesting that lessons learned from OC's history can help deal with addressing several current issues related to CSS, including an audit culture, data collection ethics, transparency, and Big Data hubris.

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