Bigger Computational Social Science: Data, Theories, Models, and Simulations -- Not Just Big Data

2016 ◽  
Author(s):  
Claudio Cioffi-Revilla
Keyword(s):  
Big Data ◽  
Social Science ◽  
Computational Social Science ◽  
Science Data ◽  
Social Science Data

Big Data under the Microscope and Brains in Social Context

2015 ◽  
Vol 659 (1) ◽  
pp. 274-289 ◽  
Author(s):  
Matthew Brook O’Donnell ◽  
Emily B. Falk
Keyword(s):  
Social Science ◽  
Language Processing ◽  
Social Research ◽  
Computational Social Science ◽  
Science Data ◽  
Individual Level ◽  
Social Network Data ◽  
Social Science Data ◽  
Message Propagation ◽  
The Brain

Methods for analyzing neural and computational social science data are usually used by different types of scientists and generally seen as distinct, but they strongly complement one another. Computational social science methodologies can strengthen and contextualize individual-level analysis, specifically our understanding of the brain. Neuroscience can help to unpack the mechanisms that lead from micro- through meso- to macro-level observations. Integrating levels of analysis is essential to unified progress in social research. We present two example areas that illustrate this integration. First, combining egocentric social network data with neural variables from the “egos” provides insight about why and for whom certain types of antismoking messages may be more or less effective. Second, combining tools from natural language processing with neuroimaging reveals mechanisms involved in successful message propagation, and suggests links from microscopic to macroscopic scales.

Social Science Data and the Courts

1976 ◽  
Vol 5 (5) ◽  
pp. 11-13
Author(s):  
PATRICIA E. STIVERS
Keyword(s):  
Social Science ◽  
Science Data ◽  
Social Science Data

scholarly journals Uncomfortable Decisions

2022 ◽  
Author(s):  
Paul Bloom ◽  
Laurie Paul
Keyword(s):  
Decision Making ◽  
Social Science ◽  
Career Change ◽  
Social Science Research ◽  
Science Research ◽  
Science Data ◽  
Decision Making Processes ◽  
Social Science Data ◽  
Personal Decision ◽  
Algorithmic Process

Some decision-making processes are uncomfortable. Many of us do not like to make significant decisions, such as whether to have a child, solely based on social science research. We do not like to choose randomly, even in cases where flipping a coin is plainly the wisest choice. We are often reluctant to defer to another person, even if we believe that the other person is wiser, and have similar reservations about appealing to powerful algorithms. And, while we are comfortable with considering and weighing different options, there is something strange about deciding solely on a purely algorithmic process, even one that takes place in our own heads.What is the source of our discomfort? We do not present a decisive theory here—and, indeed, the authors have clashing views over some of these issues—but we lay out the arguments for two (consistent) explanations. The first is that such impersonal decision-making processes are felt to be a threat to our autonomy. In all of the examples above, it is not you who is making the decision, it is someone or something else. This is to be contrasted with personal decision-making, where, to put it colloquially, you “own” your decision, though of course you may be informed by social science data, recommendations of others, and so on. A second possibility is that such impersonal decision-making processes are not seen as authentic, where authentic decision making is one in which you intentionally and knowledgably choose an option in a way that is “true to yourself.” Such decision making can be particularly important in contexts where one is making a life-changing decision of great import, such as the choice to emigrate, start a family, or embark on a major career change.

Imperatives for Theories in Social Science: The North-South Divide Notwithstanding

2020 ◽  
Vol 19 (3) ◽  
pp. 195-217
Author(s):  
Aaron Ola Ogundiwin, ◽  
Joel N. Nwachukwu
Keyword(s):  
Social Science ◽  
Conceptual Analysis ◽  
Social Research ◽  
Science Data ◽  
Social Scientists ◽  
The North ◽  
Common Mistake ◽  
History Of ◽  
Research Questions ◽  
Social Science Data

Abstract The paper underscores the place of theories in organizing social science data and experience. It holds that theories are indispensable to social research (The North-South divide notwithstanding), in view of the fact that the framework of knowledge and experience within which theories are established make a meaningful explanation of the world phenomenon reasonably possible. It delineates political philosophy and history of ideas from theory and thus, takes care of common mistake social scientists make differentiating between them. Furthermore, the paper on one hand, takes on the scientific requisites of theory such as assumption, concepts (and their functions), hypothesis (and its characteristics typology), law, models, paradigm and provides lucid conceptual analysis of each with a view to showing their relatedness to theory but not as synonyms to it. On the other hand, we singled out dependency theory in its emanation from knowledge and experience of underdevelopment of Third World countries, as the first and perhaps most relevant theoretic explanation of Africa’s underdevelopment. The paper posits that a good theory that will serve as a rudder for formulation of research questions, problem statement, as well as sustain the data analysis, and findings must parade some, if not all of the following qualities: precision and testability, empirical validity, parsimony, stimulation, and practicability.

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.

Sign in / Sign up

Export Citation Format

Share Document