Сравнение на методи

	Детекция на фалшиви новини	TF-IDF
Област	Извличане на текст	Извличане на текст
Семейство	Process / pipeline	Process / pipeline
Година на възникване	—	1988
Създател	—	Salton & Buckley
Тип	NLP text-classification task	Text vectorization / term-weighting scheme
Основополагащ източник	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.
Други названия	misinformation detection, false news classification, automated fact checking, Yanlış/Sahte Haber Tespiti	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Свързани	4	3
Резюме	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.