Identifying misinformation on Twitter with a support vector machine
There is a large amount of information from disparate sources around the world. Due to the recent growth of online social media and its impact on society, identifying misinformation is an important activity. Twitter is one of the most popular applications that can deliver engag...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Khon Kaen University
2020-09-01
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| Series: | Engineering and Applied Science Research |
| Subjects: | |
| Online Access: | https://ph01.tci-thaijo.org/index.php/easr/article/download/231341/164898/ |
| _version_ | 1819206544886071296 |
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| author | Supanya Aphiwongsophon Prabhas Chongstitvatana |
| author_facet | Supanya Aphiwongsophon Prabhas Chongstitvatana |
| author_sort | Supanya Aphiwongsophon |
| collection | DOAJ |
| description | There is a large amount of information from disparate sources around the world. Due to the recent growth of online social media and its impact on society, identifying misinformation is an important activity. Twitter is one of the most popular applications that can deliver engaging data in a timely manner. Developing techniques that can detect misinformation from Twitter has become a challenging yet necessary task. This article proposes a machine learning method that can identify misinformation from Twitter data. The experiment was carried out with three widely used machine learning methods, naïve Bayes, a neural network and a support vector machine, using Twitter data collected from October to November 2017 in Thailand. The results show that all three methods can detect misinformation accurately. The accuracy of the naïve Bayes method was 95.55%,that of the neural network was 97.09%, and that of the support vector machine 98.15%. Furthermore, we analyzed the misinformation and noted some of its characteristics. |
| first_indexed | 2024-12-23T05:09:18Z |
| format | Article |
| id | doaj.art-52f563a8a8414da7a7f2b4860b3efc73 |
| institution | Directory Open Access Journal |
| issn | 2539-6161 2539-6218 |
| language | English |
| last_indexed | 2024-12-23T05:09:18Z |
| publishDate | 2020-09-01 |
| publisher | Khon Kaen University |
| record_format | Article |
| series | Engineering and Applied Science Research |
| spelling | doaj.art-52f563a8a8414da7a7f2b4860b3efc732022-12-21T17:59:01ZengKhon Kaen UniversityEngineering and Applied Science Research2539-61612539-62182020-09-0147330631210.14456/easr.2020.33Identifying misinformation on Twitter with a support vector machineSupanya AphiwongsophonPrabhas ChongstitvatanaThere is a large amount of information from disparate sources around the world. Due to the recent growth of online social media and its impact on society, identifying misinformation is an important activity. Twitter is one of the most popular applications that can deliver engaging data in a timely manner. Developing techniques that can detect misinformation from Twitter has become a challenging yet necessary task. This article proposes a machine learning method that can identify misinformation from Twitter data. The experiment was carried out with three widely used machine learning methods, naïve Bayes, a neural network and a support vector machine, using Twitter data collected from October to November 2017 in Thailand. The results show that all three methods can detect misinformation accurately. The accuracy of the naïve Bayes method was 95.55%,that of the neural network was 97.09%, and that of the support vector machine 98.15%. Furthermore, we analyzed the misinformation and noted some of its characteristics.https://ph01.tci-thaijo.org/index.php/easr/article/download/231341/164898/misinformationidentifying misinformationonline social networksupport vector machine |
| spellingShingle | Supanya Aphiwongsophon Prabhas Chongstitvatana Identifying misinformation on Twitter with a support vector machine Engineering and Applied Science Research misinformation identifying misinformation online social network support vector machine |
| title | Identifying misinformation on Twitter with a support vector machine |
| title_full | Identifying misinformation on Twitter with a support vector machine |
| title_fullStr | Identifying misinformation on Twitter with a support vector machine |
| title_full_unstemmed | Identifying misinformation on Twitter with a support vector machine |
| title_short | Identifying misinformation on Twitter with a support vector machine |
| title_sort | identifying misinformation on twitter with a support vector machine |
| topic | misinformation identifying misinformation online social network support vector machine |
| url | https://ph01.tci-thaijo.org/index.php/easr/article/download/231341/164898/ |
| work_keys_str_mv | AT supanyaaphiwongsophon identifyingmisinformationontwitterwithasupportvectormachine AT prabhaschongstitvatana identifyingmisinformationontwitterwithasupportvectormachine |