Online Extremist Ecosystems: How a Network of Platforms and Devices Shape Far-Right and Involuntary Celibate Extremism and Violence
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,005 |
| Communication savante | 0,008 | 0,005 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».