Vertaile menetelmiä

	Valheuutisten tunnistus – harhaanjohtavan tiedon luokittelu	Tekstinluokittelu
Tieteenala	Tekstinlouhinta	Tekstinlouhinta
Menetelmäperhe	Process / pipeline	Process / pipeline
Syntyvuosi	—	—
Kehittäjä	—	—
Tyyppi	NLP text-classification task	Supervised NLP classification task
Alkuperäislähde	Shu, K. et al. (2017). Fake News Detection on Social Media. ACM SIGKDD.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
Rinnakkaisnimet	misinformation detection, false news classification, automated fact checking, Yanlış/Sahte Haber Tespiti	text categorization, document classification, topic classification, metin sınıflandırma
Liittyvät	4	4
Tiivistelmä	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.	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.