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	Detekcia postoja	Vložené reprezentácie BERT	Detekcia falošných správ
Odbor	Dolovanie textu	Dolovanie textu	Dolovanie textu
Rodina	Process / pipeline	Process / pipeline	Process / pipeline
Rok vzniku	2016	2019	—
Tvorca	Mohammad et al. (SemEval-2016 Task 6)	Devlin, Chang, Lee & Toutanova (Google AI)	—
Typ	NLP text-classification task toward a target	Contextual transformer text-representation method	NLP text-classification task
Pôvodný zdroj	Mohammad, S. et al. (2016). SemEval-2016 Task 6: Detecting Stance in Tweets. Proceedings of SemEval-2016, 31-41.	Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT, 4171-4186.	Shu, K. et al. (2017). Fake News Detection on Social Media. ACM SIGKDD.
Ďalšie názvy	stance classification, stance identification, Tutum Tespiti (Stance Detection)	contextual embeddings, transformer embeddings, BERT Tabanlı Metin Gömülmeleri	misinformation detection, false news classification, automated fact checking, Yanlış/Sahte Haber Tespiti
Príbuzné	4	4	4
Zhrnutie	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.	BERT-based text embeddings, introduced by Devlin and colleagues at Google AI in 2019, turn text into context-sensitive dense vectors using a bidirectional Transformer encoder. Because the meaning of a word shifts with its context, BERT produces richer representations than static methods such as Word2Vec or topic models like LDA.	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.