পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| ঘৃণামূলক বক্তব্য শনাক্তকরণ× | ভুয়ো খবর শনাক্তকরণ× | |
|---|---|---|
| ক্ষেত্র | টেক্সট খনন | টেক্সট খনন |
| পরিবার | Process / pipeline | Process / pipeline |
| উদ্ভবের বছর | — | — |
| প্রবর্তক | — | — |
| ধরন | NLP text-classification task | NLP text-classification task |
| মৌলিক উৎস≠ | 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 ↗ |
| অপর নাম≠ | offensive language detection, toxic content detection, Nefret Söylemi Tespiti | misinformation detection, false news classification, automated fact checking, Yanlış/Sahte Haber Tespiti |
| সম্পর্কিত | 4 | 4 |
| সারসংক্ষেপ≠ | 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. |
| ScholarGateডেটাসেট ↗ |
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