An Indonesian Hoax News Detection System Using Reader Feedback and Naïve Bayes Algorithm
Hoax news in Indonesia spread at an alarming rate. To reduce this, hoax news detection system needs to be created and put into practice. Such a system may use readers’ feedback and Naïve Bayes algorithm, which is used to verify news. Overtime, by using readers’ feedback, database corpus will continu...
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| Published in | Cybernetics and information technologies : CIT Vol. 20; no. 1; pp. 82 - 94 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Sofia
Sciendo
01.03.2020
De Gruyter Brill Sp. z o.o., Paradigm Publishing Services |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1314-4081 1311-9702 1314-4081 |
| DOI | 10.2478/cait-2020-0006 |
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| Abstract | Hoax news in Indonesia spread at an alarming rate. To reduce this, hoax news detection system needs to be created and put into practice. Such a system may use readers’ feedback and Naïve Bayes algorithm, which is used to verify news. Overtime, by using readers’ feedback, database corpus will continue to grow and could improve system performance. The current research aims to reach this. System performance evaluation is carried out under two conditions ‒ with and without sources (URL). The system is able to detect hoax news very well under both conditions. The highest precision, recall and f-measure values when including URL are 0.91, 1, and 0.95 respectively. Meanwhile, the highest value of precision, recall and f-measure without URL are 0.88, 1 and 0.94, respectively. |
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| AbstractList | Hoax news in Indonesia spread at an alarming rate. To reduce this, hoax news detection system needs to be created and put into practice. Such a system may use readers’ feedback and Naïve Bayes algorithm, which is used to verify news. Overtime, by using readers’ feedback, database corpus will continue to grow and could improve system performance. The current research aims to reach this. System performance evaluation is carried out under two conditions ‒ with and without sources (URL). The system is able to detect hoax news very well under both conditions. The highest precision, recall and f-measure values when including URL are 0.91, 1, and 0.95 respectively. Meanwhile, the highest value of precision, recall and f-measure without URL are 0.88, 1 and 0.94, respectively. |
| Author | Justitia, Army Sani, Kretawiweka Nuraga Zaman, Badrus Purwanti, Endah |
| Author_xml | – sequence: 1 givenname: Badrus surname: Zaman fullname: Zaman, Badrus email: badruszaman@fst.unair.ac.id organization: Information System Study Program, Faculty of Science and Technology, Universitas Airlangga, Surabaya, 60115, Indonesia – sequence: 2 givenname: Army surname: Justitia fullname: Justitia, Army email: army-j@fst.unair.ac.id organization: Information System Study Program, Faculty of Science and Technology, Universitas Airlangga, Surabaya, 60115, Indonesia – sequence: 3 givenname: Kretawiweka Nuraga surname: Sani fullname: Sani, Kretawiweka Nuraga email: ekanuraga@gmail.com organization: Information System Study Program, Faculty of Science and Technology, Universitas Airlangga, Surabaya, 60115, Indonesia – sequence: 4 givenname: Endah surname: Purwanti fullname: Purwanti, Endah email: endahpurwanti@fst.unair.ac.id organization: Information System Study Program, Faculty of Science and Technology, Universitas Airlangga, Surabaya, 60115, Indonesia |
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| SubjectTerms | Hoax news Indonesian news Naïve Bayes reader feedback.s |
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| Title | An Indonesian Hoax News Detection System Using Reader Feedback and Naïve Bayes Algorithm |
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