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.

翻译：截至2022年4月，已有超过六百万人死于新冠肺炎。惨重的伤亡使人们高度警惕，并试图寻找各种信息以避免感染新冠病毒。本研究旨在探讨当智能手机成为人们主要信息来源时，推送至用户设备的医疗短信信息是否准确。所提出的方法综合运用多种移动信息检索与数据挖掘技术，包括词法分析、停用词去除、词干提取及决策树分类，将医疗短信信息划分为以下五类：(i)真实信息、(ii)虚假信息、(iii)误导性信息、(iv)恶意虚假信息及(v)中性信息。实验结果表明，该方法准确率超过50%的阈值，但未达到最优水平，其根本原因在于移动短信虚假信息识别问题本身具有内在难度。