A 3D Similarity Matrix for Mobile Text Misinformation Identification

Abstract
Mobile health text messages could be true, fake, or controversial. This research does not judge their correctness by its own standards. Instead, it tries to classify the messages as one of the five categories (true, fake, misinformative, disinformative, and neutral) based on previous known messages, and lets users to judge the correctness of the messages by themselves according to our recommendations. The 3D similarity matrix, which is used to classify the incoming mobile messages into the five classes by comparing the incoming message to the known messages saved in the database. Consider the following two messages: The 2D similarity matrix is as follows:

cdc disease control prevent center announce covid-19 vaccine prevent effect
valid x
omicron x
booster x
develop
pfizer
coronavirus x
vaccine x
cdc x
center x
disease x
control x
prevent x x

Another dimension is the synonyms. For example, Omicron, COVID-19, coronavirus, and delta are in another dimension. Once you have the sparse 3D matrix, you come up with a value by analyzing the matches like The highest value from the incoming message and the saved message shows the category of the incoming message, which is the same as the one of the saved message.

Keywords
mobile computing, security and privacy, text/data mining, misinformation identification, 3D similarity matrix, sentence similarity, mobile data management

Conference