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Potentials of Digital Behavioural Trace Data: An Application from Radicalisation Research

  • Behavioural traces from interactions with digital technologies are diverse and abundant. Yet, their capacity for theory-driven research is still to be constituted. In the present cumulative dissertation project, I deliberate the caveats and potentials of digital behavioural trace data in behavioural and social science research. One use case is online radicalisation research. The three studies included, set out to discern the state-of-the-art of methods and constructs employed in radicalization research, at the intersection of traditional methods and digital behavioural trace data. Firstly, I display, based on a systematic literature review of empirical work, the prevalence of digital behavioural trace data across different research strands and discern determinants and outcomes of radicalisation constructs. Secondly, I extract, based on this literature review, hypotheses and constructs and integrate them to a framework from network theory. This graph of hypotheses, in turn, makes the relative importance of theoretical considerations explicit. One implication of visualising the assumptions in the field is to systematise bottlenecks for the analysis of digital behavioural trace data and to provide the grounds for the genesis of new hypotheses. Thirdly, I provide a proof-of-concept for incorporating a theoretical framework from conspiracy theory research (as a specific form of radicalisation) and digital behavioural traces. I argue for marrying theoretical assumptions derived from temporal signals of posting behaviour and semantic meaning from textual content that rests on a framework from evolutionary psychology. In the light of these findings, I conclude by discussing important potential biases at different stages in the research cycle and practical implications.

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Metadaten
Author:Veronika Batzdorfer
URN:urn:nbn:de:hbz:385-1-19424
DOI:https://doi.org/10.25353/ubtr-xxxx-d8ca-e037
Document Type:Doctoral Thesis
Language:English
Date of completion:2023/01/10
Publishing institution:Universität Trier
Granting institution:Universität Trier, Fachbereich 1
Date of final exam:2022/12/01
Release Date:2023/01/16
GND Keyword:Big Data; Forschung; Netzwerkanalyse; Radikalismus; Verschwörungstheorie
Number of pages:10, 91, i Blätter
First page:2
Last page:i
Licence (German):License LogoCC BY-NC-ND: Creative-Commons-Lizenz 4.0 International

$Rev: 13581 $