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Topic prediction for tobacco control based on COP9 tweets using machine learning techniques.

Sherif Elmitwalli¹, John Mehegan¹, Georgie Wellock¹

¹Tobacco Control Research Group, Department for Health, University of Bath, Bath, United Kingdom.

|February 15, 2024

Summary

This summary is machine-generated.

This study accurately predicts online discussions on tobacco harm reduction using machine learning, achieving 91.87% accuracy. Findings aid policymakers in understanding public opinion and supporting tobacco control efforts.

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Area of Science:

Public Health
Computational Social Science
Data Science

Background:

Online discussions significantly influence public health policy.
Understanding public discourse on tobacco harm reduction is crucial for effective tobacco control.

Purpose of the Study:

To develop and evaluate a machine learning approach for predicting "harm reduction" topics in tweets.
To analyze sentiment, retweets, and toxicity in online tobacco control discussions.

Main Methods:

Latent Dirichlet Allocation (LDA) for topic modeling.
Random Forest algorithm for topic prediction, achieving 91.87% accuracy.
Sentiment analysis and toxicity analysis of tweets.

Main Results:

Successfully categorized "harm reduction" tweets using LDA.
Achieved high prediction accuracy (91.87%) for harm reduction topics.
Identified correlations between retweets, sentiment, and conversation toxicity.

Conclusions:

Machine learning effectively predicts online discussions on tobacco harm reduction.
Analysis of tweet sentiment and toxicity provides insights into public opinion.
Findings support policymakers in public engagement for tobacco control policies.