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Complex Spreading Phenomena in Social Systems pp 335–350Cite as

Network Experiments Through Academic-Industry Collaboration

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Abstract

Our main goal in this chapter is to summarize and describe our work on get-out-the-vote experiments run on the Facebook social media platform. We ran randomized experiments and observed both direct effects—a message on Election Day made Facebook users more likely to vote and cascading effects in the social network—the friends of treated users became more likely to vote. Collaborating with Facebook vastly increased the scope of our research project from what we originally planned. We will also discuss why academic collaboration with industry is not only important in general, but particularly important for understanding complex social systems. We will conclude with a discussion of some of the opportunities we see for scientific advancement in this area.

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Notes

  1. 1.

    The examples from this literature utilize both observational and experimental research designs but are too numerous to cite completely. Prominent examples from this literature include [2, 8, 9, 12, 16, 21,22,23,24,25,26,27,28,29, 36,37,38, 42, 44, 45, 52, 54, 55, 58,59,60, 62, 63], among many others.

  2. 2.

    Shadish [56], who builds on the research design tradition from [14], defines internal validity, as “[t]he validity of inferences about whether observed covariation between A (the presumed treatment) and B (the presumed outcome) reflects a causal relationship from A to B, as those variables were manipulated or measured,” and external validity as “The validity of inferences about whether the cause–effect relationship holds over variation in persons, settings, treatment variables, and measurement variables.” (4). Within the potential outcomes framework developed by Rubin [53], the focus is oriented primarily towards internal validity. However, some authors have related the SUTVA assumption from the potential outcomes framework to issues of external validity and construct validity [51, 56]. There are many sources available for additional and more detailed discussions that link together different validity types. For classic discussions of the relationships between these concepts, see [1, 11, 14, 57, 64]. For more recent treatments, see [17, 19, 20]. A focus on internal validity for massive social interventions forces the analyst to intentionally design the study to avoid violations to the Stable Unit-Treatment Value Assumption (SUTVA) [53]. Recognizing this assumption and designing the study to address are critical steps, which are necessary for exploring social contagions (e.g., [13, 51, 61]).

  3. 3.

    The states we collected data from were Arkansas, California, Connecticut, Florida, Kansas, Kentucky, Missouri, Nevada, New Jersey, New York, Oklahoma, Pennsylvania, and Rhode Island.

  4. 4.

    Later methodological work pointed out this procedure for simulating the null distribution rests on the unnecessary assumption of no direct effects [5]. Thus, our method of naively permuting treatments over the network could elevate the rate of false alarms. Focal unit analysis [4, 5] allows the researcher to more explicitly specify the null hypothesis and test for the presence of spillovers. The analysis performed on the 2012 election experiment data utilizes focal unit analysis.

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Author information

Authors and Affiliations

  1. The Ohio State University, Columbus, OH, USA

    Robert M. Bond

  2. University of Michigan, Ann Arbor, MI, USA

    Christopher J. Fariss

  3. State University of New York, Stony Brook, Stony Brook, NY, USA

    Jason J. Jones

  4. William & Mary, Williamsburg, VA, USA

    Jaime E. Settle

Authors
  1. Robert M. Bond
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  2. Christopher J. Fariss
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  3. Jason J. Jones
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  4. Jaime E. Settle
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Corresponding author

Correspondence to Robert M. Bond .

Editor information

Editors and Affiliations

  1. Technical University of Denmark, Lyngby, Denmark

    Sune Lehmann

  2. Indiana University, Bloomington, Indiana, USA

    Yong-Yeol Ahn

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Bond, R.M., Fariss, C.J., Jones, J.J., Settle, J.E. (2018). Network Experiments Through Academic-Industry Collaboration. In: Lehmann, S., Ahn, YY. (eds) Complex Spreading Phenomena in Social Systems. Computational Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-77332-2_18

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