Group chat analysis of hoax detection during the covid-19 pandemic using the k nearest neighbors algorithm and massive text processing

Abstract

Group chat is the most widely used choice of various short information. Besides being easy to send messages, sharing short messages in group chat is considered effective compared to sending massively to several users. The ease of sending short messages in group chat is often used as the spread of fake news and untrue news or hoaxes, especially during the Covid-19 pandemic, the information shared can be easily shared by anyone without seeing a valid source. The dissemination of information related to Covid-19 without a clear source is a dangerous act, because it can lead users into false information and endanger themselves. Fake message detectors have not been widely implemented in instant message applications, for this reason, there is a need for a detector and a machine to analyze activities in group chat and see whether the message is included in content containing fake news or not. If a group chat has a lot of fake news, you can be sure that the group chat is not good to follow. The use of the K-Nearest Neighbors algorithm is considered quite effective in classifying an object, the results can be determined whether it is included in fake news, miss-information news, or true news. The process of processing messages is carried out by the massive text processing method because the characteristics of the text are different for each user so that text processing can be maximized for later classification. As a result, group chat can be analyzed based on active time, user messages, user activity, and messages sent between users.