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Measuring the social influence of online communications at the individual and collective level: a causal framework

Measuring the social influence of online communications at the individual and collective level: a causal framework
Measuring the social influence of online communications at the individual and collective level: a causal framework
A central problem in the analysis of observational data is inferring and measuring causal relationships - what are the underlying causes of the observed outcomes? With the recent proliferation of Big Data from Web-mediated social communications, it has become important to measure the social influence of online communications, i.e. to determine to what extent online social communications cause certain messages, ideas, behaviours to be widely adopted (to ‘go viral’), and to what extent other causes also play a role. This thesis proposes a critique and a causal conceptual and methodological framework for analysing, measuring and qualifying the social influence of online text-based communications in a given setting, while accounting for the effects of other relevant causes, at the individual and the collective level, based on ‘found’ observational digital data. At the individual level, this thesis demonstrates theoretically and through an analytical discussion how the proposed causal framework can successfully address the key limitations of the popular contagion-based paradigm for online social influence, enabling researchers to disentangle, measure and qualify the social influence of online communications, versus the effects of other (social and non-social) causes. At the collective level, by applying the proposed causal framework, this thesis empirically shows that the assumption of the contagion-based paradigm that the influence of online communications can be measured in isolation, without regard for other causes, does not hold, as it is empirically found that other causes can introduce non-negligible confounding bias to estimates of the social influence of online communications, and that these confounding causes themselves can be stronger causes of the outcomes of interest than online social communications, more robust to bias, with their effects following a much steadier pattern over time. Overall, the proposed causal framework enables researchers to empirically test claims and assumptions about which causes should be accounted for when measuring the social influence of online communications on outcomes of interest, and to pick apart and compare the social influence of online communications versus the influence of other causes, over time and across contexts, at the individual and at the collective level..
University of Southampton
Liotsiou, Dimitra
daf84d0f-299b-403f-94e3-bfdf4aadc7a0
Liotsiou, Dimitra
daf84d0f-299b-403f-94e3-bfdf4aadc7a0
Weal, Mark
e8fd30a6-c060-41c5-b388-ca52c81032a4

Liotsiou, Dimitra (2018) Measuring the social influence of online communications at the individual and collective level: a causal framework. University of Southampton, Doctoral Thesis, 345pp.

Record type: Thesis (Doctoral)

Abstract

A central problem in the analysis of observational data is inferring and measuring causal relationships - what are the underlying causes of the observed outcomes? With the recent proliferation of Big Data from Web-mediated social communications, it has become important to measure the social influence of online communications, i.e. to determine to what extent online social communications cause certain messages, ideas, behaviours to be widely adopted (to ‘go viral’), and to what extent other causes also play a role. This thesis proposes a critique and a causal conceptual and methodological framework for analysing, measuring and qualifying the social influence of online text-based communications in a given setting, while accounting for the effects of other relevant causes, at the individual and the collective level, based on ‘found’ observational digital data. At the individual level, this thesis demonstrates theoretically and through an analytical discussion how the proposed causal framework can successfully address the key limitations of the popular contagion-based paradigm for online social influence, enabling researchers to disentangle, measure and qualify the social influence of online communications, versus the effects of other (social and non-social) causes. At the collective level, by applying the proposed causal framework, this thesis empirically shows that the assumption of the contagion-based paradigm that the influence of online communications can be measured in isolation, without regard for other causes, does not hold, as it is empirically found that other causes can introduce non-negligible confounding bias to estimates of the social influence of online communications, and that these confounding causes themselves can be stronger causes of the outcomes of interest than online social communications, more robust to bias, with their effects following a much steadier pattern over time. Overall, the proposed causal framework enables researchers to empirically test claims and assumptions about which causes should be accounted for when measuring the social influence of online communications on outcomes of interest, and to pick apart and compare the social influence of online communications versus the influence of other causes, over time and across contexts, at the individual and at the collective level..

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Published date: October 2018

Identifiers

Local EPrints ID: 455943
URI: http://eprints.soton.ac.uk/id/eprint/455943
PURE UUID: 78494ce6-3671-49f2-ab8b-8adab7c4f02d
ORCID for Mark Weal: ORCID iD orcid.org/0000-0001-6251-8786

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Date deposited: 08 Apr 2022 18:12
Last modified: 24 May 2022 04:02

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Contributors

Author: Dimitra Liotsiou
Thesis advisor: Mark Weal ORCID iD

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