The overall goal of this application is to support and facilitate the necessary training to develop an
independent research career focused on examining online and offline social contexts that influence adolescent
substance use (SU). In the long-term, the applicant seeks to develop a program of research focused on the use
of digital technologies to examine social risk factors that lead to alcohol and drug use among adolescents and
to deliver prevention programming. Through the training goals, guided mentorship, and complementary
experiences, the proposed project will strategically advance the applicant's knowledge of social media and SU
research. The applicant will also be trained in state-of-the-art quantitative methodologies to enhance the design,
conduct, and analysis of big data to improve our current understanding of socialization effects on adolescent SU.
The prevention of alcohol and drug use among adolescents remains a critical area for research, as
experimentation with alcohol and drug use can lead to long-term negative consequences. In fact, 9 out of 10
people that experience SU problems began using before the age of 18 (CASAColumbia, 2011). While initiation
of SU typically occurs during the teenage years, adolescents are also spending a substantial amount of time
using social media. Adolescents use social media as a way to connect with a social network, as well as view and
display SU behaviors. However, research evaluating the impact of social media on adolescent SU has been
understudied, and the available research has several methodological limitations. Namely, prior work has
primarily focused on college samples, as well as a less popular social media platform among adolescents (i.e.,
Facebook). Furthermore, prior work has used self-report data or human coding to assess online SU content. The
proposed study seeks to advance the knowledge regarding the role of social media, specifically exposure to SU
content, and user-generated e-cigarette content, in the escalation of alcohol and drug use among adolescents.
The proposed project at the center of this training fellowship includes two aims that propose secondary
data analyses, and the collection of original data. Aim 1 of the proposed project (n = 243) will use secondary
data analyses to determine whether SU attitudes, subjective norms, and perceived behavioral control mediate
the prospective association between exposure to SU-related content posted by peers and influential figures on
offline SU behaviors. Aim 2 of the proposed project (n = 200) will prospectively examine the association between
online user-generated e-cigarette content on Instagram and offline e-cigarette use using a novel methodological
approach. Machine learning algorithms will be developed to detect e-cigarette content on Instagram profiles, and
will be compared to self-report data to assess whether the strength of this association varies based on approach.
This project will increase knowledge of adolescent SU behaviors that are not visible offline that could uniquely
inform prevention programs by identifying modifiable targets for intervention that are relevant for today's youth.