手法を比較

	ヘイトスピーチ検出	テキスト分類
分野	テキストマイニング	テキストマイニング
系統	Process / pipeline	Process / pipeline
提唱年	—	—
提唱者	—	—
種類	NLP text-classification task	Supervised NLP classification task
原典	Davidson, T., Warmsley, D., Macy, M. & Weber, I. (2017). Automated Hate Speech Detection and the Problem of Offensive Language. ICWSM, 11(1), 512-515.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
別名	offensive language detection, toxic content detection, Nefret Söylemi Tespiti	text categorization, document classification, topic classification, metin sınıflandırma
関連	4	4
概要	Hate speech detection is a natural-language-processing task that automatically identifies hateful, offensive, or harmful text on social media and online platforms. The task was sharpened by Davidson and colleagues (2017), who showed why separating genuine hate speech from merely offensive language is a hard, distinct classification problem rather than a single toxicity score.	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.