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Handbook of Computational Social Science
Published in Uwe Engel, Anabel Quan-Haase, Sunny Xun Liu, Lars Lyberg, Handbook of Computational Social Science, Volume 2, 2021
Uwe Engel, Anabel Quan-Haase, Sunny Xun Liu, Lars Lyberg
Computational social science is an interdisciplinary field of study at the intersection of data science and social science that pursues causal and predictive inference as its main objective. With historical roots in mathematical modeling and social simulation, the recent digitization of all aspects of everyday life has turned CSS into a dynamically developing and rapidly growing research field. With the seamless integration of digital technology, from mobile phones to AI, into the rhythms of everyday life, there is also a greater generation and accumulation of related behavioral data of prime interest to the social sciences. Because most of these data are digital, CSS calls for computational methods of data collection, data management, data processing, and data analysis (Lazer et al., 2020). Computational social science is, thus, an evolving field with a mix of big-data, computational-methods, and data-science facets, as will be further detailed in this handbook (Engel, volume 1: chapter 9).
Screenomics: A Framework to Capture and Analyze Personal Life Experiences and the Ways that Technology Shapes Them
Published in Human–Computer Interaction, 2021
Byron Reeves, Nilam Ram, Thomas N. Robinson, James J. Cummings, C. Lee Giles, Jennifer Pan, Agnese Chiatti, Mj Cho, Katie Roehrick, Xiao Yang, Anupriya Gagneja, Miriam Brinberg, Daniel Muise, Yingdan Lu, Mufan Luo, Andrew Fitzgerald, Leo Yeykelis
The screenome is an example of big data as defined within computational social science (Shah et al., 2015). It uses complex data measured in tera- and petabytes from naturally occurring digital media sources; it depends on computational or algorithmic solutions to identify patterns; and it is applicable to a variety of information domains, from politics to health to social relationships. While the advantages of big data are clear, so too are the risks. Big data, including the screenome, raises a variety of ethical concerns (Butler, 2007; Lazer et al., 2009). The screenome contains substantial private information—perhaps as much or more than any other individual record.