Analysis of addiction craving onset through natural language processing of the online forum Reddit

• Published in: PloS one Vol. 19; no. 5; p. e0301682
• Main Authors: Kramer, Thea, Groh, Georg, Stüben, Nathalie, Soyka, Michael
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 20-05-2024; Public Library of Science (PLoS)
• Subjects: Addiction; Addictions; Alcohol use; Alcoholic beverages; Alcoholism - psychology; Artificial Intelligence; Behavior, Addictive - psychology; Biology and Life Sciences; Boredom; Computational linguistics; Coping; Craving - physiology; Data mining; Datasets; Dimensional analysis; Dirichlet problem; Electronic cigarettes; Emotions; Emotions - physiology; Engineering and Technology; Humans; Identification methods; Language processing; Medical research; Medicine and Health Sciences; Medicine, Experimental; Natural language interfaces; Natural Language Processing; Psychological aspects; Psychological research; Smoking; Social aspects; Social environment; Social Media; Social networks; Social Sciences; Statistical analysis; Vector spaces; Virtual communities; Germany
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0301682

Alcohol cravings are considered a major factor in relapse among individuals with alcohol use disorder (AUD). This study aims to investigate the frequency and triggers of cravings in the daily lives of people with alcohol-related issues. Large amounts of data are analyzed with Artificial Intelligence (AI) methods to identify possible groupings and patterns.
For the analysis, posts from the online forum "stopdrinking" on the Reddit platform were used as the dataset from April 2017 to April 2022. The posts were filtered for craving content and processed using the word2vec method to map them into a multi-dimensional vector space. Statistical analyses were conducted to calculate the nature and frequency of craving contexts and triggers (location, time, social environment, and emotions) using word similarity scores. Additionally, the themes of the craving-related posts were semantically grouped using a Latent Dirichlet Allocation (LDA) topic model. The accuracy of the results was evaluated using two manually created test datasets.
Approximately 16% of the forum posts discuss cravings. The number of craving-related posts decreases exponentially with the number of days since the author's last alcoholic drink. The topic model confirms that the majority of posts involve individual factors and triggers of cravings. The context analysis aligns with previous craving trigger findings related to the social environment, locations and emotions. Strong semantic craving similarities were found for the emotions boredom, stress and the location airport. The results for each method were successfully validated on test datasets.
This exploratory approach is the first to analyze alcohol cravings in the daily lives of over 24,000 individuals, providing a foundation for further AI-based craving analyses. The analysis confirms commonly known craving triggers and even discovers new important craving contexts.