Mobile Text Misinformation Detection Using Effective Information Retrieval Methods

Abstract

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.