Social Bots for Online Public Health Interventions

According to the Center for Disease Control and Prevention, hundreds of thousands initiate smoking each year, and millions live with smoking-related diseases in the United States. Many tobacco users discuss their opinions, 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 curb their tobacco use. We searched the Twitter feed for tobacco-related keywords and phrases, and trained a convolutional neural network using over 4,000 tweets manually labeled as either pro-tobacco or not pro-tobacco. This model achieved a 90% accuracy rate on the training set and 74% on test data. Users posting protobacco tweets were matched with former smokers with similar interests who posted anti-tobacco tweets. Algorithmic matching, leveraging the power of peer influence, allows for the systematic delivery of personalized interventions based on real anti-tobacco tweets from former smokers. Experimental evaluation suggested that our system would perform well if deployed.