مقایسهٔ روشها
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| آشکارسازیِ پروپاگاندا× | طبقهبندی متن× | |
|---|---|---|
| حوزه | متنکاوی | متنکاوی |
| خانواده | Process / pipeline | Process / pipeline |
| سال پیدایش | — | — |
| پدیدآور | — | — |
| نوع≠ | NLP text-classification task | Supervised NLP classification task |
| منبع بنیادین≠ | Da San Martino, G. et al. (2019). Fine-Grained Analysis of Propaganda in News Articles. EMNLP. DOI ↗ | Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer. DOI ↗ |
| نامهای دیگر≠ | propaganda and manipulation detection, propaganda technique detection, Propaganda ve Manipülasyon Tespiti | text categorization, document classification, topic classification, metin sınıflandırma |
| مرتبط | 4 | 4 |
| خلاصه≠ | 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. |
| ScholarGateمجموعهداده ↗ |
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