DUI: the drug use insights web server.

MOTIVATION: Substance abuse constitutes one of the major contemporary health epidemics. Recently, the use of social media platforms has garnered interest as a novel source of data for drug addiction epidemiology. Often however, the language used in such forums comprises slang and jargon. Currently, there are no publicly available resources to automatically analyze the esoteric language-use in the social media drug-use sub-culture. This lacunae introduces critical challenges for interpreting, sensemaking and modeling of addiction epidemiology using social media. RESULTS: Drug-Use Insights (DUI) is a public and open-source web application to address the aforementioned deficiency. DUI is underlined by a hierarchical taxonomy encompassing 108 different addiction related categories consisting of over 9000 terms, where each category encompasses a set of semantically related terms. These categories and terms were established by utilizing thematic analysis in conjunction with term embeddings generated from 7 472 545 Reddit posts made by 1 402 017 redditors. Given post(s) from social media forums such as Reddit and Twitter, DUI can be used foremost to identify constituent terms related to drug use. Furthermore, the DUI categories and integrated visualization tools can be leveraged for semantic- and exploratory analysis. To the best of our knowledge, DUI utilizes the largest number of substance use and recovery social media posts used in a study and represents the first significant online taxonomy of drug abuse terminology. AVAILABILITY AND IMPLEMENTATION: The DUI web server and source code are available at: http://haddock9.sfsu.edu/insight/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Analysis of associations between emotions and activities of drug users and their addiction recovery tendencies from social media posts using structural equation modeling.

BACKGROUND: Addiction to drugs and alcohol constitutes one of the significant factors underlying the decline in life expectancy in the US. Several context-specific reasons influence drug use and recovery. In particular emotional distress, physical pain, relationships, and self-development efforts are known to be some of the factors associated with addiction recovery. Unfortunately, many of these factors are not directly observable and quantifying, and assessing their impact can be difficult. Based on social media posts of users engaged in substance use and recovery on the forum Reddit, we employed two psycholinguistic tools, Linguistic Inquiry and Word Count and Empath and activities of substance users on various Reddit sub-forums to analyze behavior underlining addiction recovery and relapse. We then employed a statistical analysis technique called structural equation modeling to assess the effects of these latent factors on recovery and relapse. RESULTS: We found that both emotional distress and physical pain significantly influence addiction recovery behavior. Self-development activities and social relationships of the substance users were also found to enable recovery. Furthermore, within the context of self-development activities, those that were related to influencing the mental and physical well-being of substance users were found to be positively associated with addiction recovery. We also determined that lack of social activities and physical exercise can enable a relapse. Moreover, geography, especially life in rural areas, appears to have a greater correlation with addiction relapse. CONCLUSIONS: The paper describes how observable variables can be extracted from social media and then be used to model important latent constructs that impact addiction recovery and relapse. We also report factors that impact self-induced addiction recovery and relapse. To the best of our knowledge, this paper represents the first use of structural equation modeling of social media data with the goal of analyzing factors influencing addiction recovery.

SMARTS: the social media-based addiction recovery and intervention targeting server.

MOTIVATION: Substance abuse and addiction is a significant contemporary health crisis. Modeling its epidemiology and designing effective interventions requires real-time data analysis along with the means to contextualize addiction patterns across the individual-to-community scale. In this context, social media platforms have begun to receive significant attention as a novel source of real-time user-reported information. However, the ability of epidemiologists to use such information is significantly stymied by the lack of publicly available algorithms and software for addiction information extraction, analysis and modeling. RESULTS: SMARTS is a public, open source, web-based application that addresses the aforementioned deficiency. SMARTS is designed to analyze data from two popular social media forums, namely, Reddit and Twitter and can be used to study the effect of various intoxicants including, opioids, weed, kratom, alcohol, and cigarettes. The SMARTS software analyzes social media posts using natural language processing, and machine learning to characterize drug use at both the individual- and population-levels. Included in SMARTS is a predictive modeling functionality that can, with high accuracy, identify individuals open to addiction recovery interventions. SMARTS also supports extraction, analysis and visualization of a number of key informational and demographic characteristics including post topics and sentiment, drug- and recovery-term usage, geolocation, and age. Finally, the distributions of the aforementioned characteristics as derived from a set of 170,097 drug users are provided as part of SMARTS and can be used by researchers as a reference. AVAILABILITY: The SMARTS web server and source code are available at: http://haddock9.sfsu.edu/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

QUENTIN: reconstruction of disease transmissions from viral quasispecies genomic data.

Motivation: Genomic analysis has become one of the major tools for disease outbreak investigations. However, existing computational frameworks for inference of transmission history from viral genomic data often do not consider intra-host diversity of pathogens and heavily rely on additional epidemiological data, such as sampling times and exposure intervals. This impedes genomic analysis of outbreaks of highly mutable viruses associated with chronic infections, such as human immunodeficiency virus and hepatitis C virus, whose transmissions are often carried out through minor intra-host variants, while the additional epidemiological information often is either unavailable or has a limited use. Results: The proposed framework QUasispecies Evolution, Network-based Transmission INference (QUENTIN) addresses the above challenges by evolutionary analysis of intra-host viral populations sampled by deep sequencing and Bayesian inference using general properties of social networks relevant to infection dissemination. This method allows inference of transmission direction even without the supporting case-specific epidemiological information, identify transmission clusters and reconstruct transmission history. QUENTIN was validated on experimental and simulated data, and applied to investigate HCV transmission within a community of hosts with high-risk behavior. It is available at https://github.com/skumsp/QUENTIN. Contact: pskums@gsu.edu or alexz@cs.gsu.edu or rahul@sfsu.edu or yek0@cdc.gov. Supplementary information: Supplementary data are available at Bioinformatics online.