Contact
Biomedical Research Center251 Bayview Blvd.
Suite 200
Baltimore, MD 21224
Phone: 667-312- 5077
Email: brenda.curtis@nih.gov
Education
Ph.D., Health Communication - Annenberg School for Communication - University of Pennsylvania - Philadelphia, PA
M.A. Health Communication - Annenberg School for Communication - University of Pennsylvania - Philadelphia, PA
M.S.P.H. Public Health, University of Illinois - Urbana-Champaign, IL
B.S. Biology - University of Illinois - Urbana-Champaign, IL
Research Interests
Dr. Curtis is a Tenure-Track Clinical Investigator since January 2019. She is Chief of the TTRU and NIH Distinguished Scholar. The Curtis Lab pairs traditional methodologies with computational psychiatry to study the digital phenotypes of people who use drugs. While much of the labs focus is on converting raw signals from digital data sources into useful clinical insights, the TTRU is very much committed to solving problems experienced by people in recovery and undergoing substance use treatment. Through clinical research, the Curtis lab intersects addiction treatment, computational psychiatry, and innovative technologies. Using natural-language processing, digital phenotyping, and deep learning, the lab focuses on enhancing precision assessment of substance use and behavioral predictors using intensive longitudinal data and integrating passive sensor data from smartphones and wearable devices. Among others, an overall goal is to develop personalized smartphone interventions for individuals living with SUD to enhance the recovery experience. Other areas of research focus on the intersect between addiction and Covid and on the impact of stigma on addiction.
Publications
Selected Publications
2024 |
Giorgi, Salvatore; Bellew, Douglas; Habib, Daniel Roy Sadek; Sedoc, Joao; Smitterberg, Chase; Devoto, Amanda; Himelein-Wachowiak, McKenzie; Curtis, Brenda Lived Experience Matters: Automatic Detection of Stigma toward People Who Use Substances on Social Media. Presentation Forthcoming The International AAAI Conference on Web and Social Media (ICWSM), 06.06.2024. @misc{nokey, |
Rai, Sunny; Stade, Elizabeth C; Giorgi, Salvatore; Francisco, Ashley; Ungar, Lyle H; Curtis, Brenda; Guntuku, Sharath C Key language markers of depression on social media depend on race Journal Article In: Proc Natl Acad Sci U S A, vol. 121, no. 14, pp. e2319837121, 2024, ISSN: 1091-6490. @article{pmid38530887b,Depression has robust natural language correlates and can increasingly be measured in language using predictive models. However, despite evidence that language use varies as a function of individual demographic features (e.g., age, gender), previous work has not systematically examined whether and how depression's association with language varies by race. We examine how race moderates the relationship between language features (i.e., first-person pronouns and negative emotions) from social media posts and self-reported depression, in a matched sample of Black and White English speakers in the United States. Our findings reveal moderating effects of race: While depression severity predicts I-usage in White individuals, it does not in Black individuals. White individuals use more belongingness and self-deprecation-related negative emotions. Machine learning models trained on similar amounts of data to predict depression severity performed poorly when tested on Black individuals, even when they were trained exclusively using the language of Black individuals. In contrast, analogous models tested on White individuals performed relatively well. Our study reveals surprising race-based differences in the expression of depression in natural language and highlights the need to understand these effects better, especially before language-based models for detecting psychological phenomena are integrated into clinical practice. |
Rai, Sunny; Stade, Elizabeth C; Giorgi, Salvatore; Francisco, Ashley; Ungar, Lyle H; Curtis, Brenda; Guntuku, Sharath C Key language markers of depression on social media depend on race Journal Article In: Proc Natl Acad Sci U S A, vol. 121, no. 14, pp. e2319837121, 2024, ISSN: 1091-6490. @article{pmid38530887,Depression has robust natural language correlates and can increasingly be measured in language using predictive models. However, despite evidence that language use varies as a function of individual demographic features (e.g., age, gender), previous work has not systematically examined whether and how depression's association with language varies by race. We examine how race moderates the relationship between language features (i.e., first-person pronouns and negative emotions) from social media posts and self-reported depression, in a matched sample of Black and White English speakers in the United States. Our findings reveal moderating effects of race: While depression severity predicts I-usage in White individuals, it does not in Black individuals. White individuals use more belongingness and self-deprecation-related negative emotions. Machine learning models trained on similar amounts of data to predict depression severity performed poorly when tested on Black individuals, even when they were trained exclusively using the language of Black individuals. In contrast, analogous models tested on White individuals performed relatively well. Our study reveals surprising race-based differences in the expression of depression in natural language and highlights the need to understand these effects better, especially before language-based models for detecting psychological phenomena are integrated into clinical practice. |
Stamatis, Caitlin A; Meyerhoff, Jonah; Meng, Yixuan; Lin, Zhi Chong Chris; Cho, Young Min; Liu, Tony; Karr, Chris J; Liu, Tingting; Curtis, Brenda L; Ungar, Lyle H; Mohr, David C Differential temporal utility of passively sensed smartphone features for depression and anxiety symptom prediction: a longitudinal cohort study Journal Article In: Npj Ment Health Res, vol. 3, no. 1, pp. 1, 2024, ISSN: 2731-4251. @article{pmid38609548,While studies show links between smartphone data and affective symptoms, we lack clarity on the temporal scale, specificity (e.g., to depression vs. anxiety), and person-specific (vs. group-level) nature of these associations. We conducted a large-scale (n = 1013) smartphone-based passive sensing study to identify within- and between-person digital markers of depression and anxiety symptoms over time. Participants (74.6% female; M age = 40.9) downloaded the LifeSense app, which facilitated continuous passive data collection (e.g., GPS, app and device use, communication) across 16 weeks. Hierarchical linear regression models tested the within- and between-person associations of 2-week windows of passively sensed data with depression (PHQ-8) or generalized anxiety (GAD-7). We used a shifting window to understand the time scale at which sensed features relate to mental health symptoms, predicting symptoms 2 weeks in the future (distal prediction), 1 week in the future (medial prediction), and 0 weeks in the future (proximal prediction). Spending more time at home relative to one's average was an early signal of PHQ-8 severity (distal β = 0.219, p = 0.012) and continued to relate to PHQ-8 at medial (β = 0.198, p = 0.022) and proximal (β = 0.183, p = 0.045) windows. In contrast, circadian movement was proximally related to (β = -0.131, p = 0.035) but did not predict (distal β = 0.034, p = 0.577; medial β = -0.089, p = 0.138) PHQ-8. Distinct communication features (i.e., call/text or app-based messaging) related to PHQ-8 and GAD-7. Findings have implications for identifying novel treatment targets, personalizing digital mental health interventions, and enhancing traditional patient-provider interactions. Certain features (e.g., circadian movement) may represent correlates but not true prospective indicators of affective symptoms. Conversely, other features like home duration may be such early signals of intra-individual symptom change, indicating the potential utility of prophylactic intervention (e.g., behavioral activation) in response to person-specific increases in these signals. |
2023 |
Wu, Tiffany; Sherman, Garrick; Giorgi, Salvatore; Thanneeru, Priya; Ungar, Lyle H; Kamath, Patrick S; Simonetto, Douglas A; Curtis, Brenda L; Shah, Vijay H Smartphone sensor data estimate alcohol craving in a cohort of patients with alcohol-associated liver disease and alcohol use disorder Journal Article In: Hepatol Commun, vol. 7, no. 12, 2023, ISSN: 2471-254X. @article{pmid38055637,BACKGROUND: Sensors within smartphones, such as accelerometer and location, can describe longitudinal markers of behavior as represented through devices in a method called digital phenotyping. This study aimed to assess the feasibility of digital phenotyping for patients with alcohol-associated liver disease and alcohol use disorder, determine correlations between smartphone data and alcohol craving, and establish power assessment for future studies to prognosticate clinical outcomes.nnMETHODS: A total of 24 individuals with alcohol-associated liver disease and alcohol use disorder were instructed to download the AWARE application to collect continuous sensor data and complete daily ecological momentary assessments on alcohol craving and mood for up to 30 days. Data from sensor streams were processed into features like accelerometer magnitude, number of calls, and location entropy, which were used for statistical analysis. We used repeated measures correlation for longitudinal data to evaluate associations between sensors and ecological momentary assessments and standard Pearson correlation to evaluate within-individual relationships between sensors and craving.nnRESULTS: Alcohol craving significantly correlated with mood obtained from ecological momentary assessments. Across all sensors, features associated with craving were also significantly correlated with all moods (eg, loneliness and stress) except boredom. Individual-level analysis revealed significant relationships between craving and features of location entropy and average accelerometer magnitude.nnCONCLUSIONS: Smartphone sensors may serve as markers for alcohol craving and mood in alcohol-associated liver disease and alcohol use disorder. Findings suggest that location-based and accelerometer-based features may be associated with alcohol craving. However, data missingness and low participant retention remain challenges. Future studies are needed for further digital phenotyping of relapse risk and progression of liver disease. |
Crozier, Madeline E; Farokhnia, Mehdi; Persky, Susan; Leggio, Lorenzo; Curtis, Brenda Relationship between self-stigma about alcohol dependence and severity of alcohol drinking and craving Journal Article In: BMJ Ment Health, vol. 26, no. 1, 2023, ISSN: 2755-9734. @article{pmid37993282,BACKGROUND: The correlates and consequences of stigma surrounding alcohol use are complex. Alcohol use disorder (AUD) is typically accompanied by self-stigma, due to numerous factors, such as shame, guilt and negative stereotypes. Few studies have empirically examined the possible association between self-stigma and alcohol-related outcomes.nnOBJECTIVE: To investigate the relationship between self-stigma about alcohol dependence and the severity of alcohol consumption and craving.nnMETHODS: In a sample of 64 participants, the majority of whom had a diagnosis of AUD (51), bivariate correlations were first conducted between Self-Stigma and Alcohol Dependence Scale (SSAD-Apply subscale) scores and Alcohol Use Disorders Identification Test (AUDIT) scores, Alcohol Timeline Follow-Back, Obsessive-Compulsive Drinking Scale (OCDS) scores and Penn Alcohol Cravings Scale scores. Based on the results, regression analyses were conducted with SSAD scores as the predictor and AUDIT and OCDS scores as the outcomes.nnFINDINGS: SSAD scores positively correlated with AUDIT scores, average drinks per drinking day, number of heavy drinking days and OCDS scores (p<0.001, p=0.014, p=0.011 and p<0.001, respectively). SSAD scores were also found to be a significant predictor of AUDIT and OCDS scores (p<0.001 and p<0.001, respectively), even after controlling for demographics.nnCONCLUSIONS: Higher levels of self-stigma were associated with more severe AUD, greater alcohol consumption, and more obsessive thoughts and compulsive behaviours related to alcohol.nnCLINICAL IMPLICATIONS: Our results suggest that potential interventions to reduce self-stigma may lead to improved quality of life and treatment outcomes for individuals with AUD. |
Giorgi, Salvatore; Yaden, David B; Eichstaedt, Johannes C; Ungar, Lyle H; Schwartz, H Andrew; Kwarteng, Amy; Curtis, Brenda Predicting U.S. county opioid poisoning mortality from multi-modal social media and psychological self-report data Journal Article In: Sci Rep, vol. 13, no. 1, pp. 9027, 2023, ISSN: 2045-2322. @article{pmid37270657,Opioid poisoning mortality is a substantial public health crisis in the United States, with opioids involved in approximately 75% of the nearly 1 million drug related deaths since 1999. Research suggests that the epidemic is driven by both over-prescribing and social and psychological determinants such as economic stability, hopelessness, and isolation. Hindering this research is a lack of measurements of these social and psychological constructs at fine-grained spatial and temporal resolutions. To address this issue, we use a multi-modal data set consisting of natural language from Twitter, psychometric self-reports of depression and well-being, and traditional area-based measures of socio-demographics and health-related risk factors. Unlike previous work using social media data, we do not rely on opioid or substance related keywords to track community poisonings. Instead, we leverage a large, open vocabulary of thousands of words in order to fully characterize communities suffering from opioid poisoning, using a sample of 1.5 billion tweets from 6 million U.S. county mapped Twitter users. Results show that Twitter language predicted opioid poisoning mortality better than factors relating to socio-demographics, access to healthcare, physical pain, and psychological well-being. Additionally, risk factors revealed by the Twitter language analysis included negative emotions, discussions of long work hours, and boredom, whereas protective factors included resilience, travel/leisure, and positive emotions, dovetailing with results from the psychometric self-report data. The results show that natural language from public social media can be used as a surveillance tool for both predicting community opioid poisonings and understanding the dynamic social and psychological nature of the epidemic. |
Curtis, Brenda; Giorgi, Salvatore; Ungar, Lyle; Vu, Huy; Yaden, David; Liu, Tingting; Yadeta, Kenna; Schwartz, H Andrew AI-based analysis of social media language predicts addiction treatment dropout at 90 days Journal Article In: Neuropsychopharmacology, 2023, ISSN: 1740-634X. @article{pmid37095253,The reoccurrence of use (relapse) and treatment dropout is frequently observed in substance use disorder (SUD) treatment. In the current paper, we evaluated the predictive capability of an AI-based digital phenotype using the social media language of patients receiving treatment for substance use disorders (N = 269). We found that language phenotypes outperformed a standard intake psychometric assessment scale when predicting patients' 90-day treatment outcomes. We also use a modern deep learning-based AI model, Bidirectional Encoder Representations from Transformers (BERT) to generate risk scores using pre-treatment digital phenotype and intake clinic data to predict dropout probabilities. Nearly all individuals labeled as low-risk remained in treatment while those identified as high-risk dropped out (risk score for dropout AUC = 0.81; p < 0.001). The current study suggests the possibility of utilizing social media digital phenotypes as a new tool for intake risk assessment to identify individuals most at risk of treatment dropout and relapse. |
Lou, Sophia; Giorgi, Salvatore; Liu, Tingting; Eichstaedt, Johannes C; Curtis, Brenda Measuring disadvantage: A systematic comparison of United States small-area disadvantage indices Journal Article In: Health Place, vol. 80, pp. 102997, 2023, ISSN: 1873-2054. @article{pmid36867991,Extensive evidence demonstrates the effects of area-based disadvantage on a variety of life outcomes, such as increased mortality and low economic mobility. Despite these well-established patterns, disadvantage, often measured using composite indices, is inconsistently operationalized across studies. To address this issue, we systematically compared 5 U.S. disadvantage indices at the county-level on their relationships to 24 diverse life outcomes related to mortality, physical health, mental health, subjective well-being, and social capital from heterogeneous data sources. We further examined which domains of disadvantage are most important when creating these indices. Of the five indices examined, the Area Deprivation Index (ADI) and Child Opportunity Index 2.0 (COI) were most related to a diverse set of life outcomes, particularly physical health. Within each index, variables from the domains of education and employment were most important in relationships with life outcomes. Disadvantage indices are being used in real-world policy and resource allocation decisions; an index's generalizability across diverse life outcomes, and the domains of disadvantage which constitute the index, should be considered when guiding such decisions. |
Matero, Matthew; Giorgi, Salvatore; Curtis, Brenda; Ungar, Lyle H; Schwartz, H Andrew Opioid death projections with AI-based forecasts using social media language Journal Article In: NPJ Digit Med, vol. 6, no. 1, pp. 35, 2023, ISSN: 2398-6352. @article{pmid36882633,Targeting of location-specific aid for the U.S. opioid epidemic is difficult due to our inability to accurately predict changes in opioid mortality across heterogeneous communities. AI-based language analyses, having recently shown promise in cross-sectional (between-community) well-being assessments, may offer a way to more accurately longitudinally predict community-level overdose mortality. Here, we develop and evaluate, TROP (Transformer for Opiod Prediction), a model for community-specific trend projection that uses community-specific social media language along with past opioid-related mortality data to predict future changes in opioid-related deaths. TOP builds on recent advances in sequence modeling, namely transformer networks, to use changes in yearly language on Twitter and past mortality to project the following year's mortality rates by county. Trained over five years and evaluated over the next two years TROP demonstrated state-of-the-art accuracy in predicting future county-specific opioid trends. A model built using linear auto-regression and traditional socioeconomic data gave 7% error (MAPE) or within 2.93 deaths per 100,000 people on average; our proposed architecture was able to forecast yearly death rates with less than half that error: 3% MAPE and within 1.15 per 100,000 people. |
Giorgi, Salvatore; Habib, Daniel Roy Sadek; Bellew, Douglas; Sherman, Garrick; Curtis, Brenda A linguistic analysis of dehumanization toward substance use across three decades of news articles Miscellaneous 2023, ISSN: 2296-2565. @misc{pmid38074754,INTRODUCTION: Substances and the people who use them have been dehumanized for decades. As a result, lawmakers and healthcare providers have implemented policies that subjected millions to criminalization, incarceration, and inadequate resources to support health and wellbeing. While there have been recent shifts in public opinion on issues such as legalization, in the case of marijuana in the U.S., or addiction as a disease, dehumanization and stigma are still leading barriers for individuals seeking treatment. Integral to the narrative of "substance users" as thoughtless zombies or violent criminals is their portrayal in popular media, such as films and news.nnMETHODS: This study attempts to quantify the dehumanization of people who use substances (PWUS) across time using a large corpus of over 3 million news articles. We apply a computational linguistic framework for measuring dehumanization across three decades of New York Times articles.nnRESULTS: We show that (1) levels of dehumanization remain high and (2) while marijuana has become less dehumanized over time, attitudes toward other substances such as heroin and cocaine remain stable.nnDISCUSSION: This work highlights the importance of a holistic view of substance use that places all substances within the context of addiction as a disease, prioritizes the humanization of PWUS, and centers around harm reduction. |
2022 |
Fisher, Celia B; Tao, Xiangyu; Liu, Tingting; Giorgi, Salvatore; Curtis, Brenda L COVID-Related Victimization, Racial Bias and Employment and Housing Disruption Increase Mental Health Risk Among US Asian, Black and Latinx Adults Journal Article In: Frontiers in Public Health, pp. 1625, 2022. @article{fishercovid, |
Bragard, Elise; Giorgi, Salvatore; Juneau, Paul; Curtis, Brenda L. Daily diary study of loneliness, alcohol, and drug use during the COVID-19 Pandemic Journal Article In: Alcoholism: Clinical and Experimental Research, vol. n/a, no. n/a, 2022. @article{bragard2022dialydiary,Abstract Background Research conducted during the COVID-19 Pandemic has identified two co-occurring public health concerns: loneliness and substance use. Findings from research conducted prior to the pandemic are inconclusive as to the links between loneliness and substance use. This study aimed to measure associations of loneliness with three different types of substance use during COVID-19: daily number of alcoholic drinks, cannabis use, and non-cannabis drug use. Method Data were obtained between October 2020 and May 2021 from 2,648 US adults (Mage = 38.76, 65.4% women) diverse with respect to race and ethnicity using online recruitment. Participants completed baseline surveys and daily assessments for 30 days. A daily loneliness measure was recoded into separate within- and between-person predictor variables. Daily outcome measures included the number of alcoholic drinks consumed and dichotomous cannabis and non-cannabis drug use variables. Generalized linear multilevel models (GLMLM) were used to examine within- and between-person associations between loneliness and substance use. Results The unconditional means model indicated that 59.0% of the variance in the daily number of alcoholic drinks was due to within-person variability. GLMLM analyses revealed that, overall, people drank more on days when they felt a particularly high or particularly low degree of loneliness (positive quadratic effect). There was a negative and significant within-person association between daily loneliness and the likelihood of cannabis use. There was also a positive and significant within-person association between daily loneliness and the likelihood of non-cannabis drug use. Conclusions Associations between loneliness and substance use vary with substance type and whether within- or between-person differences are assessed. These findings are relevant to the persistence of substance use disorders and thus of potential clinical importance. Individuals who do not experience severe loneliness at intake but who show daily increases in loneliness above baseline levels are at heightened risk of alcohol and non-cannabis drug use. Future research could profitably examine just-in-time adaptive interventions that assess fluctuations in loneliness to prevent the development or exacerbation of substance use disorders. |
Liu, Tingting; Ungar, Lyle H.; Curtis, Brenda; Sherman, Garrick; Yadeta, Kenna; Tay, Louis; Eichstaedt, Johannes C.; Guntuku, Sharath Chandra Head versus heart: social media reveals differential language of loneliness from depression Journal Article In: npj Mental Health Research, vol. 1, no. 1, pp. 16, 2022, ISBN: 2731-4251. @article{Liu:2022aa,We study the language differentially associated with loneliness and depression using 3.4-million Facebook posts from 2986 individuals, and uncover the statistical associations of survey-based depression and loneliness with both dictionary-based (Linguistic Inquiry Word Count 2015) and open-vocabulary linguistic features (words, phrases, and topics). Loneliness and depression were found to have highly overlapping language profiles, including sickness, pain, and negative emotions as (cross-sectional) risk factors, and social relationships and activities as protective factors. Compared to depression, the language associated with loneliness reflects a stronger cognitive focus, including more references to cognitive processes (i.e., differentiation and tentative language, thoughts, and the observation of irregularities), and cognitive activities like reading and writing. As might be expected, less lonely users were more likely to reference social relationships (e.g., friends and family, romantic relationships), and use first-person plural pronouns. Our findings suggest that the mechanisms of loneliness include self-oriented cognitive activities (i.e., reading) and an overattention to the interpretation of information in the environment. These data-driven ecological findings suggest interventions for loneliness that target maladaptive social cognitions (e.g., through reframing the perception of social environments), strengthen social relationships, and treat other affective distress (i.e., depression). |
Giorgi, Salvatore; Himelein-wachowiak, Mckenzie; Habib, Daniel; Ungar, Lyle; Curtis, Brenda Nonsuicidal Self-Injury and Substance Use Disorders: A Shared Language of Addiction Proceedings Article In: Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology, pp. 177–183, Association for Computational Linguistics, Seattle, USA, 2022. @inproceedings{nokey,Nonsuicidal self-injury (NSSI), or the deliberate injuring of one?s body without intending to die, has been shown to exhibit many similarities to substance use disorders (SUDs), including population-level characteristics, impulsivity traits, and comorbidity with other mental disorders. Research has further shown that people who self-injure adopt language common in SUD recovery communities (e.g., {``}clean{''}, {``}relapse{''}, {``}addiction,{''} and celebratory language about sobriety milestones). In this study, we investigate the shared language of NSSI and SUD by comparing discussions on public Reddit forums related to self-injury and drug addiction. To this end, we build a set of LDA topics across both NSSI and SUD Reddit users and show that shared language across the two domains includes SUD recovery language in addition to other themes common to support forums (e.g., requests for help and gratitude). Next, we examine Reddit-wide posting activity and note that users posting in {emph{r/selfharm} also post in many mental health-related subreddits, while users of drug addiction related subreddits do not, despite high comorbidity between NSSI and SUDs. These results show that while people who self-injure may contextualize their disorder as an addiction, their posting habits demonstrate comorbidities with other mental disorders more so than their counterparts in recovery from SUDs. These observations have clinical implications for people who self-injure and seek support by sharing their experiences online. |
Giorgi, Salvatore; Guntuku, Sharath Chandra; Himelein-Wachowiak, Mckenzie; Kwarteng, Amy; Hwang, Sy; Rahman, Muhammad; Curtis, Brenda Twitter Corpus of the #BlackLivesMatter Movement and Counter Protests: 2013 to 2021 Journal Article In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 16, no. 1, pp. 1228-1235, 2022. @article{giorgi2022twitter, |
Bragard, Elise; Giorgi, Salvatore; Juneau, Paul; Curtis, Brenda L Loneliness and Daily Alcohol Consumption During the COVID-19 Pandemic Journal Article In: Alcohol and Alcoholism, 2022. @article{bragard2021loneliness, |
Devoto, Amanda; Himelein-Wachowiak, McKenzie; Liu, Tingting; Curtis, Brenda Women's Substance Use and Mental Health During the COVID-19 Pandemic Journal Article In: Women's Health Issues, 2022. @article{devoto2022women, |
"Himelein-Wachowiak, McKenzie; Giorgi, Salvatore; Kwarteng, Amy; Schriefer, Destiny; Smitterberg, Chase; Yadeta, Kenna; Bragard, Elise; Devoto, Amanda; Ungar, Lyle; Curtis, Brenda" Getting 'clean' from nonsuicidal self-injury: Experiences of addiction on the subreddit r/selfharm Journal Article In: Journal of Behavioral Addictions, 2022. @article{himeleinwachowiak2022getting, |
Jose, Rupa; Matero, Matthew; Sherman, Garrick; Curtis, Brenda; Giorgi, Salvatore; Schwartz, Hansen Andrew; Ungar, Lyle H. Using Facebook language to predict and describe excessive alcohol use Journal Article In: Alcoholism: Clinical and Experimental Research, vol. 46, no. 5, pp. 836-847, 2022. @article{jose2022using,Abstract Background Assessing risk for excessive alcohol use is important for applications ranging from recruitment into research studies to targeted public health messaging. Social media language provides an ecologically embedded source of information for assessing individuals who may be at risk for harmful drinking. Methods Using data collected on 3664 respondents from the general population, we examine how accurately language used on social media classifies individuals as at-risk for alcohol problems based on Alcohol Use Disorder Identification Test-Consumption score benchmarks. Results We find that social media language is moderately accurate (area under the curve = 0.75) at identifying individuals at risk for alcohol problems (i.e., hazardous drinking/alcohol use disorders) when used with models based on contextual word embeddings. High-risk alcohol use was predicted by individuals' usage of words related to alcohol, partying, informal expressions, swearing, and anger. Low-risk alcohol use was predicted by individuals' usage of social, affiliative, and faith-based words. Conclusions The use of social media data to study drinking behavior in the general public is promising and could eventually support primary and secondary prevention efforts among Americans whose at-risk drinking may have otherwise gone ``under the radar.'' |
