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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.19280 |
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| _version_ | 1866914703152775168 |
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| author | Hu, Wen-Chen Pillai, Sanjaikanth E Vadakkethil Somanathan ElSaid, Abdelrahman Ahmed |
| author_facet | Hu, Wen-Chen Pillai, Sanjaikanth E Vadakkethil Somanathan ElSaid, Abdelrahman Ahmed |
| contents | More than six million people died of the COVID-19 by April 2022. The heavy casualties have put people on great and urgent alert and people try to find all kinds of information to keep them from being inflected by the coronavirus. This research tries to find out whether the mobile health text information sent to peoples devices is correct as smartphones becoming the major information source for people. The proposed method uses various mobile information retrieval and data mining technologies including lexical analysis, stopword elimination, stemming, and decision trees to classify the mobile health text information to one of the following classes: (i) true, (ii) fake, (iii) misinformative, (iv) disinformative, and (v) neutral. Experiment results show the accuracy of the proposed method is above the threshold value 50 percentage, but is not optimal. It is because the problem, mobile text misinformation identification, is intrinsically difficult. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_19280 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mobile Health Text Misinformation Identification Using Mobile Data Mining Hu, Wen-Chen Pillai, Sanjaikanth E Vadakkethil Somanathan ElSaid, Abdelrahman Ahmed Computers and Society More than six million people died of the COVID-19 by April 2022. The heavy casualties have put people on great and urgent alert and people try to find all kinds of information to keep them from being inflected by the coronavirus. This research tries to find out whether the mobile health text information sent to peoples devices is correct as smartphones becoming the major information source for people. The proposed method uses various mobile information retrieval and data mining technologies including lexical analysis, stopword elimination, stemming, and decision trees to classify the mobile health text information to one of the following classes: (i) true, (ii) fake, (iii) misinformative, (iv) disinformative, and (v) neutral. Experiment results show the accuracy of the proposed method is above the threshold value 50 percentage, but is not optimal. It is because the problem, mobile text misinformation identification, is intrinsically difficult. |
| title | Mobile Health Text Misinformation Identification Using Mobile Data Mining |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2402.19280 |