dc.contributor.author |
Amin, Md. Abdullah Al |
|
dc.contributor.author |
Sayem, Aquibuzzaman Md. |
|
dc.date.accessioned |
2020-10-04T06:57:02Z |
|
dc.date.available |
2020-10-04T06:57:02Z |
|
dc.date.issued |
2019-11 |
|
dc.identifier.uri |
http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/4387 |
|
dc.description.abstract |
Nowadays political people or other people are spreading fake news for their own
benefit. Ordinary people easily believe these fake news. As a result, riots are
spreading among the people across the country which is risky for a developing
country. In this paper, we are working to detect fake news and provide a model for
checking fake news. We collected our dataset which has different type of news and
labeled them as 0 and 1, which means true and fake respectively. For detecting the
fake news we use Long Short-Term Memory, Bidirectional Long-Short Term
Memory and Random Forest algorithms in our dataset and compared the results
among these model for checking which one gives us a better result. After our
experiment we found that Random Forest algorithm had 87.75% accuracy to detect
the fake news. |
en_US |
dc.language.iso |
en_US |
en_US |
dc.publisher |
Daffodil International University |
en_US |
dc.relation.ispartofseries |
;P15323 |
|
dc.subject |
Machine learning |
en_US |
dc.subject |
Fake news |
en_US |
dc.title |
C Becomes The Most Popular Language for "Machine Learning" Fake News Detection |
en_US |
dc.type |
Other |
en_US |