Online Extremist Ecosystems: How a Network of Platforms and Devices Shape Far-Right and Involuntary Celibate Extremism and Violence
Bibliographic record
Abstract
This doctoral thesis by publication critically explores and sought to improve the adoption of ecological concepts and terms in extremism and terrorism studies, focusing on networked digital technologies and their capacity to influence the expression of or involvement in online extremist communities related to far-right and incel ideologies. The objective is to provide a foundational theoretical and empirical assessment of the use of ecology in extremism and terrorism studies. This research aims to provide theoretical and empirical support for future research or interventions concentrated on understanding and addressing the anticipated influence of networked digital media environments on involuntary celibacy and far-right extremist communities. The research questions guiding this research are: 1. What are the advantages and limitations of using ecology to understand and address far-right or incel online violent extremism?; and 2. In what ways do digital media environments (constituted by social media platforms and hardware devices) shape an individual’s involvement in far-right or incel OVEC? The study adopted an interdisciplinary, mixed-methods approach which combined methodological elements from social and ecological sciences, and produced a sociotechnical framework to better understand the social and technical relationships within online communities that involve far-right and incel extremism. This research compared different national and ideological online communities to identify commonalities, idiosyncrasies, and factors contributing to their respective emergence and evolution. Its research design is composed of combinations of qualitative and quantitative methods, such as narrative and thematic analysis, network analysis, and statistical modelling to analyse stories, identities, masculinities, and emotional expressions that enable the formation and maintenance of these communities as characterised by technology. Findings reveal ten concepts common to the form and function of ecosystems and ten foundational theoretical and methodological advantages and limitations of using ecological concepts and methods in extremism and terrorism research. The metaphorical use of ecosystems is mostly devoid of the abundant list of concepts core to the meaning of “ecosystems”, including (but not limited to): network; dynamism; complexity; classification; self-organisation; evolution; adaptation; swarm intelligence; non-linear behaviour; and emergence. At the time of this thesis’ writing, an “online extremist ecosystem” has been neither conceptualised nor theorised in any rigorous or consistent manner. This research advances the interdisciplinary uses of ecological concepts and terms in social sciences, specifically studies in terrorism and extremism and media and communication studies. This research produced important contextual information on the threat landscape within Australia and Canada concerning far-right extremism, and the threat landscape internationally concerning incel extremism. It contributed the first comprehensive socioecological assessment of how technologies facilitate conditions favourable to far-right and incel ideologies or identities as expressed in international online space. This study progressed understandings about how community behaviour and structural pressures can coalesce in “online extremist ecosystems” to characterise an individual’s involvement in far-right or incel online (violent) extremist communities. Outcomes of this thesis can directly contribute to policy and practice designs when implementing an interpersonal, measurable, sociotechnical intervention to address clients referred for their online extremist activities. Findings strongly support the idea that digital media technologies not only enable a violent extremist’s intention to action beliefs, but simultaneously shapes these intentions and beliefs. This thesis contributed a foundational theoretical and empirical knowledge base to advance interdisciplinary research and policy production concerning communities where far-right and incel extremists dwell and develop, and where violent extremism emerges as a result.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".