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| Wykrywanie mowy nienawiści× | Wykrywanie fałszywych wiadomości× | |
|---|---|---|
| Dziedzina | Eksploracja tekstu | Eksploracja tekstu |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania | — | — |
| Twórca | — | — |
| Typ | NLP text-classification task | NLP text-classification task |
| Źródło pierwotne≠ | Davidson, T., Warmsley, D., Macy, M. & Weber, I. (2017). Automated Hate Speech Detection and the Problem of Offensive Language. ICWSM, 11(1), 512-515. DOI ↗ | Shu, K. et al. (2017). Fake News Detection on Social Media. ACM SIGKDD. link ↗ |
| Inne nazwy≠ | offensive language detection, toxic content detection, Nefret Söylemi Tespiti | misinformation detection, false news classification, automated fact checking, Yanlış/Sahte Haber Tespiti |
| Pokrewne | 4 | 4 |
| Podsumowanie≠ | Hate speech detection is a natural-language-processing task that automatically identifies hateful, offensive, or harmful text on social media and online platforms. The task was sharpened by Davidson and colleagues (2017), who showed why separating genuine hate speech from merely offensive language is a hard, distinct classification problem rather than a single toxicity score. | 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. |
| ScholarGateZbiór danych ↗ |
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