Analyzing Performance of Classifiers for Fake News Detection
| Main Authors: | Fathima Shafeek, Gloriya Mathew |
|---|---|
| Format: | Proceeding Journal |
| Terbitan: |
Amal Jyothi College of Engineering Kanjirappally
, 2021
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/5094254 |
Daftar Isi:
- Abstract- Fake information is faux or misleading information supplied as news. Fake news is spread on social media and news outlets to boost readership or as a form of psychological warfare. It regularly has the goal of damaging the popularity of person or entity, or earning money via marketing sales. So the sites containing false and misleading information should be detected and filtered out. One way to get around this is to figure out faux news and identify how to better classify them. This paper compares the output of three machine learning techniques that is for k-Nearest Neighbors (KNN), Naïve Bayes, Logistic Regression on fake news dataset in order to determine which classifier is best for classifying fake news correctly.