Social Bots for Online Public Health Interventions

TL;DR

This paper presents Notobot, an AI-powered Twitter bot designed to identify pro-tobacco tweets and deliver personalized anti-tobacco interventions by leveraging machine learning and peer influence, aiming to enhance public health outreach.

Contribution

The work introduces a novel framework and a machine learning-based Twitter bot for targeted tobacco cessation interventions using real-time social media data.

Findings

Achieved 90% recall on training data and 74% on test data for identifying pro-tobacco tweets.

Matched pro-tobacco users with former smokers for personalized interventions.

System shows promise for scalable public health campaigns.

Abstract

According to the Center for Disease Control and Prevention, in the United States hundreds of thousands initiate smoking each year, and millions live with smoking-related dis- eases. Many tobacco users discuss their habits and preferences on social media. This work conceptualizes a framework for targeted health interventions to inform tobacco users about the consequences of tobacco use. We designed a Twitter bot named Notobot (short for No-Tobacco Bot) that leverages machine learning to identify users posting pro-tobacco tweets and select individualized interventions to address their interest in tobacco use. We searched the Twitter feed for tobacco-related keywords and phrases, and trained a convolutional neural network using over 4,000 tweets dichotomously manually labeled as either pro- tobacco or not pro-tobacco. This model achieves a 90% recall rate on the training set and 74% on test data. Users posting pro- tobacco tweets are matched with former smokers with similar interests who posted anti-tobacco tweets. Algorithmic matching, based on the power of peer influence, allows for the systematic delivery of personalized interventions based on real anti-tobacco tweets from former smokers. Experimental evaluation suggests that our system would perform well if deployed. This research offers opportunities for public health researchers to increase health awareness at scale. Future work entails deploying the fully operational Notobot system in a controlled experiment within a public health campaign.