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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Cardoso, M., Ribeiro, E. & Batista, F. (2025). Portuguese Far-Right Discourse on Social Media: Insights from Topic Modeling. In 14th Symposium on Languages, Applications and Technologies (SLATE 2025). Faro
Exportar Referência (IEEE) 
M. Cardoso et al.,  "Portuguese Far-Right Discourse on Social Media: Insights from Topic Modeling", in 14th Symp. on Languages, Applications and Technologies (SLATE 2025), Faro, 2025, vol. 135
Exportar BibTeX 
@inproceedings{cardoso2025_1777808628689,
	author = "Cardoso, M. and Ribeiro, E. and Batista, F.",
	title = "Portuguese Far-Right Discourse on Social Media: Insights from Topic Modeling",
	booktitle = "14th Symposium on Languages, Applications and Technologies (SLATE 2025)",
	year = "2025",
	editor = "",
	volume = "135",
	number = "",
	series = "",
	doi = "10.4230/OASIcs.SLATE.2025.12",
	publisher = "",
	address = "Faro",
	organization = ""
}
Exportar RIS 
TY  - CPAPER
TI  - Portuguese Far-Right Discourse on Social Media: Insights from Topic Modeling
T2  - 14th Symposium on Languages, Applications and Technologies (SLATE 2025)
VL  - 135
AU  - Cardoso, M.
AU  - Ribeiro, E.
AU  - Batista, F.
PY  - 2025
DO  - 10.4230/OASIcs.SLATE.2025.12
CY  - Faro
AB  - This study analyzes the social media discourse of leading figures from Portugal’s far right party CHEGA, examining 10,323 posts on X (formerly Twitter) published between late 2019 and mid‑2024. Using BERTopic, 59 latent topics clustered into two main discursive dynamics were found: (1) ideological and public, and (2) party, electoral and parliamentary related. Within the first dynamic, we conducted a focused sub-analysis of themes related with identity, immigration and security narratives - topics that display posting peaks around electoral cycles, suggesting the strategic use of emotionally charged, identitarian frames for political mobilization. The model exhibits strong topic coherence and lexical diversity, indicating its robustness in extracting thematic structures from politically polarized microtexts. Nevertheless, our findings are constrained by source, the absence of interaction metrics, and the unmet need to link online discourse to offline events. This study demonstrates how computational topic modeling can reveal strategic communication patterns in far-right political discourse and underscores the need for cross-platform and interaction-level research to assess broader societal impact.
ER  -