Abstract
According to Pew Research Center, eight-in-ten Americans acquire news from digital devices, favoring mobile devices over desktops and laptops. News is therefore spread faster, wider, and easier. However, many of these mobile messages are at risk of being incorrect or even distorted on purpose. This research aims to mitigate this problem by identifying mobile text misinformation to allow mobile users to accurately judge the messages they receive. The proposed method uses various mobile data mining technologies including ChatGPT and several ensemble learning methods (including recurrent neural networks (RNN) and bagging, boosting, stacking, & voting means) to identify mobile misinformation. In addition, sentiment and emotional analyses are discussed in comparison. Experiment results show the ensemble learning methods provide higher accuracy than standalone ChatGPT or RNN model. Nevertheless, the problem, misinformation identification, is intrinsically difficult. Further refinements are needed before it is put into practical use.
TopA study reveals that misinformation spreads faster than true information. It is crucial to recognize and differentiate between various types of information, particularly during public health crises, as false information can mislead individuals and hinder efforts to mitigate the impact of the pandemic. Misinformation detection is critical and popular in these days because information could be
created and sent by everyone, not just news agencies, and some may distribute misinformation unintentionally or intentionally. Many methods are used to detect all kinds of misinformation like politics, businesses, text messages, emails, or news. This research places the focus on mobile health text misinformation identification. If the results are favorable, the method may be extended to other kinds of information. Related research can be found from the articles (Bozuyla, 2021; Hakak, Alazab, Khan, Gadekallu, Maddikunta, & Khan, 2021; Kaliyar, Goswami, & Narang, 2019).