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Mobile Health Text Misinformation Identification Using Mobile Data Mining

Mobile Health Text Misinformation Identification Using Mobile Data Mining

Wen-Chen Hu, Sanjaikanth E. Vadakkethil Somanathan Pillai, Abdelrahman Ahmed ElSaid
Copyright: © 2022 |Volume: 12 |Issue: 1 |Pages: 14
ISSN: 2640-4249|EISSN: 2640-4257|EISBN13: 9781799863700|DOI: 10.4018/IJMDWTFE.311433
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MLA

Hu, Wen-Chen, et al. "Mobile Health Text Misinformation Identification Using Mobile Data Mining." IJMDWTFE vol.12, no.1 2022: pp.1-14. http://doi.org/10.4018/IJMDWTFE.311433

APA

Hu, W., Pillai, S. E., & ElSaid, A. A. (2022). Mobile Health Text Misinformation Identification Using Mobile Data Mining. International Journal of Mobile Devices, Wearable Technology, and Flexible Electronics (IJMDWTFE), 12(1), 1-14. http://doi.org/10.4018/IJMDWTFE.311433

Chicago

Hu, Wen-Chen, Sanjaikanth E. Vadakkethil Somanathan Pillai, and Abdelrahman Ahmed ElSaid. "Mobile Health Text Misinformation Identification Using Mobile Data Mining," International Journal of Mobile Devices, Wearable Technology, and Flexible Electronics (IJMDWTFE) 12, no.1: 1-14. http://doi.org/10.4018/IJMDWTFE.311433

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Abstract

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 have tried to find all kinds of information to keep them from being infected by the coronavirus. This research tries to find out whether the mobile health text information sent to people's devices is correct as smartphones have become 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: (1) true, (2) fake, (3) misinformative, (4) disinformative, and (5) neutral. Experiment results show the accuracy of the proposed method is above the threshold value 50% but is not optimal. It is because the problem, mobile text misinformation identification, is intrinsically difficult.

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