Porovnat metody

	Detekce falešných zpráv	TF-IDF
Obor	Dolování textu	Dolování textu
Rodina	Process / pipeline	Process / pipeline
Rok vzniku	—	1988
Tvůrce	—	Salton & Buckley
Typ	NLP text-classification task	Text vectorization / term-weighting scheme
Původní zdroj	Shu, K. et al. (2017). Fake News Detection on Social Media. ACM SIGKDD.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
Další názvy	misinformation detection, false news classification, automated fact checking, Yanlış/Sahte Haber Tespiti	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Příbuzné	4	3
Shrnutí	Fake news detection is a natural-language-processing classification task that assesses the credibility of news text and labels content as fake or genuine. Building on the social-media framing of Shu et al. (2017) and the automated-fact-checking framing of Thorne and Vlachos (2018), it turns unstructured news articles into a supervised credibility decision learned from labelled examples.	TF-IDF, introduced by Salton and Buckley (1988), is a term-weighting scheme that scores each word in a document by how often it appears there and how rare it is across the whole collection. It turns raw text into weighted document vectors, giving high weight to terms that are frequent in one document but uncommon elsewhere.