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.

Computational Social Science, Big Data, and Networks

2020 ◽  
pp. 515-534
Author(s):  
Bruno Abrahao ◽  
Paolo Parigi
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Social Science ◽  
Social Life ◽  
Computational Models ◽  
Social Space ◽  
Computational Social Science ◽  
Social Processes ◽  
New Science ◽  
Building Models

The emergence of Big data and a quantified social space has prompted the birth of a new science, computational social science (CSS), whose roots are founded in research aiming to describe social processes using computational models. Big data now fuels rapid advancements in the field, providing the basis for building models and algorithms of human behavior. New sources of massive amounts of data fundamentally reflect interactions, and, in this context, networks are intuitive abstractions to model our social life, especially that mediated by technology. The chapter introduces several examples of empirical and theoretical CSS research employing network analysis, machine learning and online experiments. It concludes with a list of challenges confronting CSS practitioners, in and outside of academia.

Citizenship, Social Media, and Big Data

2016 ◽  
Vol 35 (1) ◽  
pp. 3-9 ◽  
Author(s):  
Homero Gil de Zúñiga ◽  
Trevor Diehl
Keyword(s):  
Social Media ◽  
Big Data ◽  
Social Science ◽  
Political Communication ◽  
High Performance ◽  
Large Data ◽  
Computational Social Science ◽  
Communication Research ◽  
Ethical Practices ◽  
Storage Technologies

This special issue of the Social Science Computer Review provides a sample of the latest strategies employing large data sets in social media and political communication research. The proliferation of information communication technologies, social media, and the Internet, alongside the ubiquity of high-performance computing and storage technologies, has ushered in the era of computational social science. However, in no way does the use of “big data” represent a standardized area of inquiry in any field. This article briefly summarizes pressing issues when employing big data for political communication research. Major challenges remain to ensure the validity and generalizability of findings. Strong theoretical arguments are still a central part of conducting meaningful research. In addition, ethical practices concerning how data are collected remain an area of open discussion. The article surveys studies that offer unique and creative ways to combine methods and introduce new tools while at the same time address some solutions to ethical questions.

Big Data, Digital Media, and Computational Social Science

2015 ◽  
Vol 659 (1) ◽  
pp. 6-13 ◽  
Author(s):  
Dhavan V. Shah ◽  
Joseph N. Cappella ◽  
W. Russell Neuman
Keyword(s):  
Big Data ◽  
Social Science ◽  
Digital Media ◽  
Computational Social Science

scholarly journals Empathy Rituals: Small Conversations about Emotional Distress on Twitter

Sociology ◽  
2018 ◽  
Vol 53 (1) ◽  
pp. 104-122 ◽  
Author(s):  
Julie Brownlie ◽  
Frances Shaw
Keyword(s):  
Social Media ◽  
Big Data ◽  
Social Science ◽  
Emotional Distress ◽  
Computational Social Science ◽  
Research Interest ◽  
Small Data ◽  
Emotional Dimension

There is growing research interest in the sharing of emotions through social media. Usually centred on ‘newsworthy’ events and collective ‘flows’ of emotion, this work is often computationally driven. This article presents an interaction-led analysis of small data from Twitter to illustrate how this kind of intensive focus can ‘thicken’ claims about emotions, and particularly empathy. Drawing on Goffman’s work on ritual, we introduce and then apply the idea of ‘empathy rituals’ to exchanges about emotional distress on Twitter, a platform primarily researched using big data approaches. While the potential of Goffman’s work has been explored in some depth in relation to digital performances, its emotional dimension has been less fully examined. Through a focus on Twitter conversations, we show how reading small data can inform computational social science claims about emotions and add to sociological understanding of emotion in (digital) publics.

Sign in / Sign up

Export Citation Format

Share Document