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In recent years, what has come to be termed the "digitalization" of society has led to changes in the way scientists study social, psychological, political and economic phenomena. According to New York University’s Center for Data Science, by 2020, we will have created 35 zetabytes worth of data (a Cisco blogpost states a single zettabyte of data is equivalent to the data on about 250 billion DVDs). Many researchers in the field of “Computational Social Science” are capitalizing on the abundance of data (often called “Big Data”) about human behavior generated from society’s obsession with new media platforms and technology. Computational social science is, in a broad sense, the use of computational tools to model, simulate and analyze complex social phenomena such as inequality, healthcare, education, environment and democracy.
The new opportunities that arise from Big Data also bring new challenges. One such challenge is how to use this data to investigate problems that span disciplines when interdisciplinary collaborations are not exactly commonplace. For instance, a computer scientist may have expertise in the tools needed to collect and analyze data scraped from the web, but may not have the deep subject matter knowledge of a psychologist or sociologist that is essential in asking the right questions and in formulating relevant models with the data. Fortunately, there is a movement toward encouraging teams of researchers with different domain expertise to work together to maximize the impact of big data on scientific discovery. As a result, the importance of including psychologists to work in this field is becoming clear as well. So what do psychologists involved in computational social science look like? Below are brief descriptions of a few psychologists who make interesting contributions to computational social science.
Dr. Rosaria Conte- Head of the LABSS (Laboratory of Agent Based Social Simulation) at the ISTC (Institute for Cognitive Science and Technology) in Rome, Italy.
Dr. Conte is a cognitive and social scientist, whose lab uses agent based models (ABM) to study positive social action such as altruism, cooperation and social norms. ABM involves building a computational model consisting of "agents," that represent actors in the social world, and an "environment" in which the agents act. Agents may interact with each other and are programmed to be autonomous. Much of Conte’s work considers particular solutions to social dilemmas (i.e. situations where cooperation between members of society is hard to achieve because the best move for an individual does not produce the best outcome for the group). Previous work shows that norms, conventions and societal regulations are effective in preventing the collapse of societal cooperation when members of the society are known to one another (see Ostrom, 2005 for a review). However, when individuals are faced with unknown strangers, with little or no opportunities for future re-encounters, cooperation easily collapses, unless non-cooperators are punished. Conte and Giardini (2012) used ABM to offer a novel alternative. In particular, they showed how reputation spreading (gossip) evolves as a way to identify non-cooperators and that it acts as a cost-effective solution to enforcing group cooperation.
Dr. Morteza Dehghani- Assistant Professor of Psychology at University of Southern California.
Dr. Dehghani’s research encompasses psychology and artificial intelligence, and he draws upon big data to investigate human behavior. He uses both text analysis methods and traditional behavioral studies to investigate properties of cognition. In one study, Dehghani and colleagues used 731,000 tweets about the 2013 U.S. government shutdown to determine how five basic moral concerns -- care/harm, fairness/cheating, loyalty/betrayal, authority/subversion and purity/degradation -- widen or narrow the social closeness between people. They observed relationships on Twitter, and found that rhetoric within the tweets concerning purity was the best predictor of distance between two people on Twitter. In other words, purity is the moral foundation that drives people apart and holds them together.
Dr. Michael Jones- W. K. Estes Chair of Cognitive Modeling, Indiana University Bloomington.
Dr. Jones research encompasses the realms of cognitive modeling, semantic memory, artificial intelligence and data science. In a project funded by NSF and Google, Dr. Jones’ lab studied how to integrate perceptual components into models of how humans learn the meanings of words (semantic learning) and represent these meanings mentally. Standard models of human semantic learning use only the statistical information contained in the patterns of language (e.g. word frequency, co-occurrence of words in text) to infer semantic structure. In addition, most of these models are trained with far less data than humans typically experience during semantic learning. To account for these issues, Jones’ lab developed a suite of online games that crowdsource human coding of perceptual information. The massive amounts of data collected from subjects playing these online games is then used to develop computational models of semantic learning that integrate perceptual and linguistic information. For more information on these games and work in the lab, click here.
How to Get There
If you are interested in becoming a computational social scientist, more schools are beginning to offer programs specifically for this field. Alternatively, if you are already a trained social scientist, it may be worth your while to attend a workshop to learn how to work with big data including social media data (June 23rd, 2016 Northwestern is hosting a conference and related workshop). If you already have the computational skills but are looking for collaborators, it may be useful to seek out calls for networking opportunities in this field, such as this one, recently held in February, 2016.
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References
E. Ostrom, Understanding Institutional Diversity, Princeton University Press; 2005
