Misinformation is always a serious problem for the general public, especially during a pandemic. People constantly receive text messages of related coronavirus news and its cures from their smartphones, which have become major devices for communication these days. These health text messages help people update their coronavirus knowledge repeatedly and better manage their health, but some of the messages may mislead people and may even cause a fatal result. This research tries to identify mobile health text misinformation by using various effective information retrieval methods including lexical analysis, stopword removal, stemming, synonym discovery, various message similarity measurements, and data fusion. Readers will learn various information retrieval methods applied to contemporary research: mobile misinformation detection. Experiment results show the accuracy of the proposed method meets the expectation but still has room for improvement because misinformation detection is intrinsically difficult, and no satisfactory methods have been found yet.

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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.