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Social Influence: From Contagion to a Richer Causal Understanding

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Social Informatics (SocInfo 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10047))

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Abstract

A central problem in the analysis of observational data is inferring causal relationships - what are the underlying causes of the observed behaviors? With the recent proliferation of Big Data from online social networks, it has become important to determine to what extent social influence causes certain messages to ‘go viral’, and to what extent other causes also play a role. In this paper, we present a causal framework showing that social influence is confounded with personal similarity, traits of the focal item, and external circumstances. Combined with a set of qualitative considerations on the combination of these sources of causation, we show how this framework can enable investigators to systematically evaluate, strengthen and qualify causal claims about social influence, and we demonstrate its usefulness and versatility by applying it to a variety of common online social datasets.

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Notes

  1. 1.

    As opposed to inference based on statistical prediction methods [9, 20–23, 28, 47], which have been used elsewhere in the literature (e.g. [11, 16, 40]).

  2. 2.

    In [43], it is assumed that one can be directly socially influenced only by those people she considers her ‘friends’ (\(A_{i,j}=1\)), and not by anyone else.

  3. 3.

    We note that \(Y_{i,{t-1}}\) might represent a plausible and relevant kind of cause, e.g. that i does Y at time t because i did Y at \(t-1\) and was happy with the results, or out of habit from having done it previously at time \(t-1\). However, this previous happiness or habit may best be included in \(X_i\) as a variable representing an interest in Y.

  4. 4.

    \(W_i\) could be in \(Z'\) but it is redundant, due to the assumed asymmetry of \(A_{ij}\); if there was an edge \(A_{ij} \rightarrow Y_{j,t-1}\) then \(W_i\) would have to be in the minimal confounding set.

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Authors and Affiliations

  1. University of Southampton, Southampton, UK

    Dimitra Liotsiou, Luc Moreau & Susan Halford

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  1. Dimitra Liotsiou
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  2. Luc Moreau
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Correspondence to Dimitra Liotsiou .

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Editors and Affiliations

  1. University of Washington, Seattle, Washington, USA

    Emma Spiro

  2. Indiana University, Bloomington, Indiana, USA

    Yong-Yeol Ahn

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Liotsiou, D., Moreau, L., Halford, S. (2016). Social Influence: From Contagion to a Richer Causal Understanding. In: Spiro, E., Ahn, YY. (eds) Social Informatics. SocInfo 2016. Lecture Notes in Computer Science(), vol 10047. Springer, Cham. https://doi.org/10.1007/978-3-319-47874-6_9

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  • DOI: https://doi.org/10.1007/978-3-319-47874-6_9

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