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Using Facebook language to predict and describe excessive alcohol use.

Rupa Jose1, Matthew Matero2, Garrick Sherman1

  • 1Positive Psychology Center, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

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Summary

Social media language can help identify individuals at risk for alcohol problems. This approach shows moderate accuracy in detecting hazardous drinking and alcohol use disorders, aiding public health efforts.

Keywords:
excessive alcohol usenatural language processingsocial mediasubclinical drinking

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Area of Science:

  • Computational Social Science
  • Public Health Research
  • Digital Epidemiology

Background:

  • Assessing excessive alcohol use is crucial for research recruitment and public health interventions.
  • Social media language offers an ecologically valid data source for identifying at-risk individuals.

Purpose of the Study:

  • To evaluate the accuracy of social media language in classifying individuals at risk for alcohol problems.
  • To determine if language patterns on social media can predict alcohol use disorder risk.

Main Methods:

  • Analysis of social media language from 3664 general population respondents.
  • Utilizing contextual word embeddings and Alcohol Use Disorder Identification Test-Consumption (AUDIT-C) benchmarks.
  • Machine learning models to classify at-risk alcohol use based on language patterns.

Main Results:

  • Social media language demonstrated moderate accuracy (AUC = 0.75) in identifying individuals at risk for alcohol problems.
  • Specific word categories (alcohol, partying, informal language, anger) predicted high-risk alcohol use.
  • Social, affiliative, and faith-based language predicted low-risk alcohol use.

Conclusions:

  • Social media data analysis is a promising tool for studying general population drinking behaviors.
  • This methodology can potentially support primary and secondary prevention strategies for at-risk drinking.
  • Early identification of at-risk drinking through social media may reach individuals who would otherwise go undetected.