So sánh phương pháp

	Phát hiện Tuyên truyền	Phân tích Khung	Phân loại văn bản
Lĩnh vực	Khai phá văn bản	Khai phá văn bản	Khai phá văn bản
Họ	Process / pipeline	Process / pipeline	Process / pipeline
Năm ra đời	—	1982	—
Người khởi xướng	—	Charles J. Fillmore	—
Loại	NLP text-classification task	NLP frame-semantic parsing task	Supervised NLP classification task
Công trình gốc	Da San Martino, G. et al. (2019). Fine-Grained Analysis of Propaganda in News Articles. EMNLP.	Fillmore, C. J. (1982). Frame Semantics. In Linguistics in the Morning Calm. Seoul: Hanshin Publishing. ISBN: 9788970050355	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
Tên gọi khác	propaganda and manipulation detection, propaganda technique detection, Propaganda ve Manipülasyon Tespiti	frame semantics, frame-semantic parsing, FrameNet analysis, Çerçeve Analizi (Frame Analysis) — NLP	text categorization, document classification, topic classification, metin sınıflandırma
Liên quan	4	4	4
Tóm tắt	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.	Frame analysis is a FrameNet-based natural-language-processing task that detects the semantic frames evoked in text and the participant roles (frame-evoking elements and frame elements, FE) that fill them. Rooted in Charles Fillmore's frame semantics (1982) and operationalised by the Berkeley FrameNet Project (Baker et al., 1998), it is widely used to analyse media discourse and political text.	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.