Sammenlign metoder
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| Detektion af falske nyheder× | Tekstklassificering× | |
|---|---|---|
| Fagområde | Tekstmining | Tekstmining |
| Familie | Process / pipeline | Process / pipeline |
| Oprindelsesår | — | — |
| Ophavsperson | — | — |
| Type≠ | NLP text-classification task | Supervised NLP classification task |
| Oprindelig kilde≠ | Shu, K. et al. (2017). Fake News Detection on Social Media. ACM SIGKDD. link ↗ | Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer. DOI ↗ |
| Aliasser | misinformation detection, false news classification, automated fact checking, Yanlış/Sahte Haber Tespiti | text categorization, document classification, topic classification, metin sınıflandırma |
| Relaterede | 4 | 4 |
| Resumé≠ | 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. |
| ScholarGateDatasæt ↗ |
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