방법 비교

	Stance Detection	가짜 뉴스 탐지
분야	텍스트 마이닝	텍스트 마이닝
계열	Process / pipeline	Process / pipeline
기원 연도	2016	—
창시자	Mohammad et al. (SemEval-2016 Task 6)	—
유형	NLP text-classification task toward a target	NLP text-classification task
원전	Mohammad, S. et al. (2016). SemEval-2016 Task 6: Detecting Stance in Tweets. Proceedings of SemEval-2016, 31-41.	Shu, K. et al. (2017). Fake News Detection on Social Media. ACM SIGKDD.
별칭	stance classification, stance identification, Tutum Tespiti (Stance Detection)	misinformation detection, false news classification, automated fact checking, Yanlış/Sahte Haber Tespiti
관련	4	4
요약	Stance detection is a natural-language-processing task that decides the position a text takes toward a specific claim, event, or topic — labelling it as favor, against, or neutral. Formalised by Mohammad et al. in the SemEval-2016 Task 6 shared task, it differs from plain sentiment analysis because the label is always relative to a defined target rather than the overall emotional tone of the text.	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.