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Computational social science ≠ computer science + social data

Published: 21 February 2018 Publication History

Abstract

The important intersection of computer science and social science.

References

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Barocas, S. and Selbst, A.D. Big data's disparate impact. California Law Review 104 (2016), 671--732.
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Clauset, A., Arbesman, S., and Larremore, D.B. Systematic inequality and hierarchy in faculty hiring networks. Science Advances 1, 1 (Jan. 2015).
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Gerrish, S. and Blei, D. How they vote: Issue-adjusted models of legislative behavior. In Advances in Neural Information Processing Systems Twenty Five (2012), 2753--2761.
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Hardt, H. How big data is unfair; http://bit.ly/1BBglLr.
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Hopkins, D.J. and King, G. A method of automated nonparametric content analysis for social science. American Journal of Political Science 54, 1 (Jan. 2010), 229--247.
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Jackman, S. Bayesian Analysis for the Social Sciences. Wiley, 2009.
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Lauderdale, B.E. and Clark, T.S. Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58, 3 (Mar. 2014), 754--771.
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Sharma, A., Hofman, J., and Watts, D. Estimating the causal impact of recommendation systems from observational data. In Proceedings of the Sixteenth ACM Conference on Economics and Computation (2015), 453--470.
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Szegedy, C. et al. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015).

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  1. Computational social science ≠ computer science + social data

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    Published In

    cover image Communications of the ACM
    Communications of the ACM  Volume 61, Issue 3
    March 2018
    107 pages
    ISSN:0001-0782
    EISSN:1557-7317
    DOI:10.1145/3190347
    Issue’s Table of Contents
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 21 February 2018
    Published in CACM Volume 61, Issue 3

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    • (2024)How to measure political polarization in text-as-data? A scoping review of computational social science approachesJournal of Information Technology & Politics10.1080/19331681.2024.2318404(1-14)Online publication date: 13-Feb-2024
    • (2024)Computational strategic communication in a data-driven worldPublic Relations Review10.1016/j.pubrev.2024.10249650:5(102496)Online publication date: Dec-2024
    • (2023)Rising adoption of artificial intelligence in scientific publishing: evaluating the role, risks, and ethical implications in paper drafting and review processClinical Chemistry and Laboratory Medicine (CCLM)10.1515/cclm-2023-113662:5(835-843)Online publication date: 30-Nov-2023
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    • (2023)Biases and Ethical Considerations for Machine Learning Pipelines in the Computational Social SciencesEthics in Artificial Intelligence: Bias, Fairness and Beyond10.1007/978-981-99-7184-8_6(99-113)Online publication date: 30-Dec-2023
    • (2023)Computationale Methoden in den Sozial- und HumanwissenschaftenForschungsmethoden und Evaluation in den Sozial- und Humanwissenschaften10.1007/978-3-662-64762-2_19(1011-1062)Online publication date: 23-Feb-2023
    • (2023)The Ethics of Computational Social ScienceHandbook of Computational Social Science for Policy10.1007/978-3-031-16624-2_4(57-104)Online publication date: 24-Jan-2023
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