Background: Young adults are navigating contradictory claims about e-cigarettes as alternatives for regular cigarettes. While marketing messages promote e-cigarettes as superior alternatives, public ...agencies issue cautionary health warnings about possible harms of e-cigarette use. As a result, youth may experience attitudinal ambivalence, which is an equal amount of positive and negative attitudes, toward e-cigarette use. While research has indicated that exposure and receptivity to e-cigarette marketing influences product use, no work has examined the extent to which this ambivalence about the harms and benefits of e-cigarettes leaves consumers vulnerable to the effects of e-cigarette marketing. This study addresses this gap and seeks to understand the interrelationships between: (a) e-cigarette use perceptions (b) attitudinal ambivalence regarding e-cigarette use, and (c) exposure and receptivity to e-cigarette messages. Methods: A sample of 350 undergraduate students participated in an online experimental design, in which they were randomly assigned to pretest-posttest condition or posttest only condition. Participants were randomly exposed to one of the e-cigarette message conditions: (1) message argument supporting possible benefits of e-cigarette smoking, (2) message argument harms of e-cigarette smoking, (3) ambiguous message with one argument each for benefit and one harm of e-cigarette smoking. Results: Message condition has no significant effect on e-cigarette use perceptions. Message receptivity has a strong influence on e-cigarette benefit perceptions of e-cigarette use posttest attitudinal ambivalence. Pretest attitudinal ambivalence has persisting carryover effects on posttest attitudinal ambivalence. Asian population reported the least harm perceptions of e-cigarette use in comparison to other ethnic groups. Discussion: The vulnerability of young people lies in them harboring highly pliable benefit perceptions of e-cigarettes that are influenced more by message receptivity than message condition. Attitudinal ambivalence about e-cigarette use emerges as an attitude that is resilient to any message condition. Practitioners should investigate simulated or real word of mouth campaigns using narrative messaging or social media initiatives, to help youth transition to a stage of informed univalence. Future theoretic work should explore ambivalence as a multi-dimensional attitude that prolongs vulnerability to risk behavior.
Social Bots for Online Public Health Interventions Deb, Ashok; Majmundar, Anuja; Sungyong Seo ...
2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM),
2018-August
Conference Proceeding
Odprti dostop
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.
Social bots for online public health interventions Deb, Ashok; Majmundar, Anuja; Seo, Sungyong ...
Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining,
08/2018
Conference Proceeding
Odprti dostop
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 pro-tobacco 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.
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.
PDF
