Online Illicit Drug Distribution in the Thai Language on X: Exploratory Qualitative Content Analysis
Issued Date
2025-01-01
Resource Type
eISSN
25641891
DOI
Scopus ID
2-s2.0-105026675588
Journal Title
Jmir Infodemiology
Volume
5
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Jmir Infodemiology Vol.5 No.1 (2025)
Suggested Citation
Lamy F.R., Paek S.C., Meemon N. Online Illicit Drug Distribution in the Thai Language on X: Exploratory Qualitative Content Analysis. Jmir Infodemiology Vol.5 No.1 (2025). doi:10.2196/71703 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/114009
Title
Online Illicit Drug Distribution in the Thai Language on X: Exploratory Qualitative Content Analysis
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
Abstract
Background: By increasing exposure to drug-related advertisements, the illicit digital drug trade promotes drug normalization and eases access to substances, increasing the likelihood of initiation. Social media platforms play an increasingly important role in facilitating the online substance trade by leveraging encrypted communications and user-friendly interfaces to advertise a large variety of readily available substances. Despite its growing importance, there is a paucity of research conducted in Thailand that aims to determine the types of substances, marketing strategies, and public health risks linked to drugs advertised on social media. Objective: This study aimed to inductively explore the content of tweets on the social media platform X (formerly known as Twitter) advertising drugs in the Thai language. Methods: Tweets advertising psychoactive substances in the Thai language were collected manually between April and July 2024. A qualitative content analysis was performed on the collected tweets. Tweets were coded based on 5 themes: types of substances advertised, marketing strategies, delivery methods, number of substances per tweet, and location references. The intercoder reliability for each theme was assessed using Krippendorff a, achieving substantial agreement across most codes. Results: A total of 3832 tweets advertising drugs were collected and analyzed. Most tweets (2424/3832, 63.26%) mentioned 5 or more substances, with depressants such as opioids (2807/3832, 73.25%), antihistamines (2394/3832, 62.47%), and benzodiazepines (2009/3832, 52.42%) being the most frequently advertised. Common marketing techniques included direct contact information (2848/3832, 74.32%) and fast delivery (1216/3832, 31.73%). Delivery methods primarily involved courier services but generally offered multiple options. Tweets that mentioned at least 1 sex-performance enhancer were frequently (422/543, 77.7%) advertised in combination with benzodiazepine. Conclusions: The results of this study suggest the presence of a large number of substances advertised for sale on the X platform in the Thai language. This digital form of drug trading is facilitated by possible direct messaging and the large number of courier services existing in Thailand. Our findings call for the development of real-time monitoring systems that harness drug-related data from social media to inform public health practitioners about emerging substances and trends and address the challenges posed by the digital drug trade.