Intent mining framework for understanding online conversations on vaping to inform social media-based intervention design.
The recent surge in the usage of e-cigarettes amongst youth has highlighted a long-standing societal crisis. To assist public health agencies in policymaking, past research often employed traditional survey-based methods to understand youth behavior, which suffer from response biases and scalability...
| Publicado en: | Behaviour & Information Technology Vol. 44; no. 9; pp. 1828 - 1846 |
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| Autores principales: | , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Jun2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=185486682&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185486682 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 0144929X B6Q jtl: Behaviour & Information Technology issn: 0144929X maglogo: Y pubinfo: dt: Jun2025 vid: 44 iid: 9 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 185486682 178667392 185486682 185486682 10.1080/0144929X.2024.2378882 185486682 ppf: 1828 ppct: 18 formats: tig: atl: Intent mining framework for understanding online conversations on vaping to inform social media-based intervention design. aug: au: Gupta, Anuridhi Velagapuri, Varun Xue, Hong Purohit, Hemant affil: College of Engineering and Computing, George Mason University, Fairfax, VA, USA sug: subj: Intention Social Media Conversation Vaping Prevention and Control Electronic Cigarettes In Adulthood Internet Social Learning Theory Policy Making Health Behavior Human Adolescence Young Adult Adult Machine Learning Algorithms Storytelling Psychometrics Informatics Thematic Analysis Crowdsourcing Adolescent: 13-18 years Adult: 19-44 years ab: The recent surge in the usage of e-cigarettes amongst youth has highlighted a long-standing societal crisis. To assist public health agencies in policymaking, past research often employed traditional survey-based methods to understand youth behavior, which suffer from response biases and scalability, are time-consuming, and their findings often lag the fast-changing public behavior. Our study fills this gap by using social media as a complementary data source to understand user intentions for vape usage at a large scale, thus, providing an alternative to traditional survey-based methods. In this paper, we propose a novel user intent mining framework under the guidance of social cognitive theory for health behavioral interventions that helps study user intentions across different social media platforms. We then employ this framework to investigate the feasibility of automated intent mining on social media by formulating a multi-class classification task, employing machine learning algorithms to classify a social media message across relevant intent classes: Accusational, Anecdotal, Informational, Justificational and Promotional. The analyses indicate that Accusational tweets and Anecdotal messages were most prevalent on X/Twitter and Reddit respectively. We further provide novel insights on the conversational context using topic modeling analysis and psychometric analysis consequently, informing intervention designs and assisting health analysts. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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