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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.16793593 |
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| author | Rakshya Sharma, Bidhya Sharma, Prashikshya Baral, Susmita Parajuli |
| author_facet | Rakshya Sharma, Bidhya Sharma, Prashikshya Baral, Susmita Parajuli |
| contents | <p>The Internet's rapid development makes it possible for information to spread quickly through websites<br>or social networks. Fake or unverified news spreads on social media and reaches thousands of users<br>without anyone questioning its veracity. Misinformation or fabricated news that spreads on social media<br>with the intention of harming a person, group, or agency is known as fake news. Various machine-learning<br>techniques have been used to distinguish fake news from real. In this paper, we tried to achieve this using<br>machine learning techniques and natural language processing. We use frequency-inverse document<br>frequency as a feature extraction method and logistic regression, support vector machine and Naive Bayes<br>as the classifier. We use a dataset with labels for fake and real news to train our model. </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_16793593 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Nepali Fake News Detection Using Machine Learning Algorithms Rakshya Sharma, Bidhya Sharma, Prashikshya Baral, Susmita Parajuli <p>The Internet's rapid development makes it possible for information to spread quickly through websites<br>or social networks. Fake or unverified news spreads on social media and reaches thousands of users<br>without anyone questioning its veracity. Misinformation or fabricated news that spreads on social media<br>with the intention of harming a person, group, or agency is known as fake news. Various machine-learning<br>techniques have been used to distinguish fake news from real. In this paper, we tried to achieve this using<br>machine learning techniques and natural language processing. We use frequency-inverse document<br>frequency as a feature extraction method and logistic regression, support vector machine and Naive Bayes<br>as the classifier. We use a dataset with labels for fake and real news to train our model. </p> |
| title | Nepali Fake News Detection Using Machine Learning Algorithms |
| url | https://doi.org/10.5281/zenodo.16793593 |