Identifying Substance Use Risk Based on Deep Neural Networks and Instagram Social Media Data

Title:

Identifying Substance Use Risk Based on Deep Neural Networks and Instagram Social Media Data

Link:

https://www.nature.com/articles/s41386-018-0247-x

Abstract:

Social media may provide new insight into our understanding of substance use and addiction. In this study, we developed a deep-learning method to automatically classify individuals’ risk for alcohol, tobacco, and drug use based on the content from their Instagram profiles. In total, 2287 active Instagram users participated in the study. Deep convolutional neural networks for images and long short-term memory (LSTM) for text were used to extract predictive features from these data for risk assessment. The evaluation of our approach on a held-out test set of 228 individuals showed that among the substances we evaluated, our method could estimate the risk of alcohol abuse with statistical significance. These results are the first to suggest that deep-learning approaches applied to social media data can be used to identify potential substance use risk behavior, such as alcohol use. Utilization of automated estimation techniques can provide new insights for the next generation of population-level risk assessment and intervention delivery.

Citation:

Saeed Hassanpour, Naofumi Tomita, Timothy DeLise, Benjamin Crosier, Lisa Marsch, “Identifying Substance Use Risk Based on Deep Neural Networks and Instagram Social Media Data”, Neuropsychopharmacology, 44, 487-494, 2019.