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This article examines how far-right extremist discourse manifests, evolves, and circulates across digital media environments and proposes an interdisciplinary methodological framework to better analyze and prevent terrorism. Combining computational linguistics, communication theory, and social science perspectives, the authors highlight the role of online ecosystems in shaping identity-based narratives, recruitment pathways, and ideological reinforcement. They argue that far-right discourse is not only ideological but also algorithmic, shaped through platform dynamics, echo chambers, and affective speech patterns, and they present analytical tools to detect and categorize harmful content across multiple platforms. The article’s relevance to the current era is substantial: with far-right extremism rising globally and digital ecosystems playing a central role in radicalization, the study provides valuable insights for policymakers, researchers, and counterterrorism practitioners seeking to develop evidence-based interventions, improve digital monitoring capabilities, and design community-level counter-narratives that address online extremism in real time.This article represents a meaningful and timely contribution to the study of digital radicalization and far-right extremism, offering a strong interdisciplinary approach with direct implications for counterterrorism policy and digital safety governance.The article’s strengths include its interdisciplinary framework, which combines computational methods with social science theory, making it suitable for analyzing the complexity of online extremist ecosystems. It effectively identifies the linguistic, visual, and network-based indicators of far-right discourse, and the integration of data-driven techniques provides an empirical foundation often missing in theoretical radicalization studies. However, its limitations include a lack of large-scale empirical testing and potential constraints arising from platform data access, which restricts the generalizability of the findings. Moreover, while the article discusses interdisciplinary tools, it provides limited practical guidelines for operationalizing these methods within law enforcement or community-level prevention programs. Compared to established literature on online radicalization and extremist ecosystems, such as works by Conway or Maly, this article offers a complementary but more method-oriented viewpoint, situating itself between computational linguistics and terrorism studies. Its unique contribution lies in presenting a methodological bridge across disciplines rather than focusing exclusively on ideological or sociopolitical analysis.