Ensemble approach for fake news classification using machine learning
During the covid 19 outbreak, fake news has grown highly, affecting people’s mental and physical health. There is a wide range of solutions for fake news classification which are machine learning-based proposed models. Research shows that the existing proposed models have less accuracy, and they are...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2022-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2022/04/itmconf_icacc2022_03017.pdf |
| _version_ | 1811307794278645760 |
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| author | Pogul Gopi Rohokhale Sankei More Priya Chavan Pallavi |
| author_facet | Pogul Gopi Rohokhale Sankei More Priya Chavan Pallavi |
| author_sort | Pogul Gopi |
| collection | DOAJ |
| description | During the covid 19 outbreak, fake news has grown highly, affecting people’s mental and physical health. There is a wide range of solutions for fake news classification which are machine learning-based proposed models. Research shows that the existing proposed models have less accuracy, and they are only text-based models. In our research paper, we are focused on different algorithms, and we are comparing these algorithms in our proposed model in this research paper. We are considering the title author and text in the proposed model. Based on our experiments, Logistic Regression has high accuracy, recall, and precision score values. This research paper suggests using a logistic regression model to classify fake news. |
| first_indexed | 2024-04-13T09:10:39Z |
| format | Article |
| id | doaj.art-fd7b1f2fc1f04461916d9787ea2138f7 |
| institution | Directory Open Access Journal |
| issn | 2271-2097 |
| language | English |
| last_indexed | 2024-04-13T09:10:39Z |
| publishDate | 2022-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj.art-fd7b1f2fc1f04461916d9787ea2138f72022-12-22T02:52:53ZengEDP SciencesITM Web of Conferences2271-20972022-01-01440301710.1051/itmconf/20224403017itmconf_icacc2022_03017Ensemble approach for fake news classification using machine learningPogul Gopi0Rohokhale Sankei1More Priya2Chavan Pallavi3Department of Information TechnologyRamrao Adik Institute of TechnologyNavi Mumbai IndiaNavi Mumbai IndiaDuring the covid 19 outbreak, fake news has grown highly, affecting people’s mental and physical health. There is a wide range of solutions for fake news classification which are machine learning-based proposed models. Research shows that the existing proposed models have less accuracy, and they are only text-based models. In our research paper, we are focused on different algorithms, and we are comparing these algorithms in our proposed model in this research paper. We are considering the title author and text in the proposed model. Based on our experiments, Logistic Regression has high accuracy, recall, and precision score values. This research paper suggests using a logistic regression model to classify fake news.https://www.itm-conferences.org/articles/itmconf/pdf/2022/04/itmconf_icacc2022_03017.pdf |
| spellingShingle | Pogul Gopi Rohokhale Sankei More Priya Chavan Pallavi Ensemble approach for fake news classification using machine learning ITM Web of Conferences |
| title | Ensemble approach for fake news classification using machine learning |
| title_full | Ensemble approach for fake news classification using machine learning |
| title_fullStr | Ensemble approach for fake news classification using machine learning |
| title_full_unstemmed | Ensemble approach for fake news classification using machine learning |
| title_short | Ensemble approach for fake news classification using machine learning |
| title_sort | ensemble approach for fake news classification using machine learning |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2022/04/itmconf_icacc2022_03017.pdf |
| work_keys_str_mv | AT pogulgopi ensembleapproachforfakenewsclassificationusingmachinelearning AT rohokhalesankei ensembleapproachforfakenewsclassificationusingmachinelearning AT morepriya ensembleapproachforfakenewsclassificationusingmachinelearning AT chavanpallavi ensembleapproachforfakenewsclassificationusingmachinelearning |