Sammenlign metoder

	Propaganda Detection	Tekstklassificering
Fagområde	Tekstmining	Tekstmining
Familie	Process / pipeline	Process / pipeline
Oprindelsesår	—	—
Ophavsperson	—	—
Type	NLP text-classification task	Supervised NLP classification task
Oprindelig kilde	Da San Martino, G. et al. (2019). Fine-Grained Analysis of Propaganda in News Articles. EMNLP.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
Aliasser	propaganda and manipulation detection, propaganda technique detection, Propaganda ve Manipülasyon Tespiti	text categorization, document classification, topic classification, metin sınıflandırma
Relaterede	4	4
Resumé	Propaganda detection is a natural-language-processing task that automatically identifies and labels persuasion and manipulation techniques in text — such as loaded language, oversimplified solutions, bandwagon appeals, and glittering generalities. It builds on the fine-grained propaganda analysis introduced by Da San Martino et al. (2019), turning rhetorical manipulation into structured, technique-level labels.	Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.