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Using Natural Language Processing to Explore "Dry January" Posts on Twitter: Longitudinal Infodemiology Study.

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This study analyzed over 200,000 tweets about Dry January (alcohol abstinence) from 2020-2022. Key themes like health benefits and progress updates remained consistent, with unique pandemic-related discussions in 2021. Human-authored posts received higher engagement than bot-generated content.

Keywords:
Dry JanuaryTwitteralcoholdrinkinginfodemiologyinfoveillancenatural language processingsocial media

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Area of Science:

  • Social Media Analysis
  • Public Health
  • Digital Communication

Background:

  • Dry January is a popular alcohol abstinence campaign.
  • Limited research exists on participants' experiences.
  • Social media offers insights into public discourse on Dry January.

Purpose of the Study:

  • Analyze themes in Dry January tweets (2020-2022).
  • Examine 2021 themes in the context of the COVID-19 pandemic.
  • Assess the relationship between tweet composition and engagement.

Main Methods:

  • Natural language processing on 222,917 tweets.
  • Topic modeling (LDA) and sentiment analysis (VADER).
  • Bot detection (Botometer) and engagement metrics (likes, retweets).

Main Results:

  • Consistent themes observed annually (resources, benefits, progress).
  • Unique pandemic-related themes emerged in 2021.
  • Tweet composition (human vs. bot) correlated with engagement; bots had lower engagement.

Conclusions:

  • Social media data is valuable for studying drinking reduction efforts.
  • Twitter discussions reveal evolving needs of individuals reducing alcohol intake.
  • Monitoring online discourse can inform support for cessation attempts.