Analyzing the Spread and Impact of 5G-Corona Misinformation on Twitter: A Statistical Approach

Lekia Nkpordee¹, Ibinabo Magnus Ogolo²

¹Department of Mathematics and Statistics, Kampala International University, Kampala, Uganda.

²School of Foundation Studies, Rivers State College of Health Science and Management Technology.  

Abstract

This research uses sophisticated statistical and machine learning techniques to investigate and mitigate the impact of the 5G Corona virus spread misinformation on the Twitter platform using the COVID-19 Misinformation Tweets Labelled Dataset. The research analyzes the temporal distribution characteristics of the spread of misinformation for specific periods in the day, including morning, afternoon, evening, and night, using both categorical and probabilistic approaches. Sentiment analysis using natural language processing (NLP) is done to establish the emotional content analysis of the tweet, while logistic regression, Random Forest, and Naïve Bayes classifiers are used to establish the predictive model for the likelihood that the tweets are either malicious with the help of predictors such as the number of followers, the number of friends, and the hour the tweets are made. The results indicate the highest number of malicious activities takes place late in the night and early morning periods, with the highest levels taking place in the morning periods, with the highest proportionality levels taking place in the morning periods. The results also indicate that the malicious tweets are slightly more negative than their corresponding counterparts in the emotional content analysis results. Of the three classifiers, Random Forest has the highest classification accuracy (AUC = 0.916) in accurately determining the level of the malicious content spread on the Twitter platform, with the highest efficiency in the spread of misinformation on the Twitter platform.    

Keywords: Health misinformation, Social media analytics, Sentiment analysis, Probabilistic modeling, COVID-19 infodemic

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