Researchers from Chiang Mai University and Phetchabun Rajabhat University have characterized the structure of illicit drug marketing across multiple social media platforms in Thailand. Published in Frontiers in Digital Health, the mixed-methods netnographic study analyzed 6,446 publicly accessible posts collected between January and December 2025. The findings reveal a digital environment where coded communication, convenience-focused operational cues, and cross-platform transition signals coexist to facilitate engagement with illicit substance markets.
The study aimed to move beyond single-platform analyses by examining how drug marketing practices operate across a broader digital ecosystem. The research team utilized a mixed-methods netnographic approach, combining quantitative descriptive content analysis with qualitative coding. This method allowed for the systematic observation of online communities and digital cultures without direct interaction with users or sellers. The observational design ensured that the data reflected naturally occurring marketing behaviors rather than researcher-induced activities.
Data collection involved a structured hybrid search strategy across nine digital platforms, including X, Facebook, Instagram, LINE, TikTok, Telegram, Discord, Threads, and Shopee. A curated Thai-language lexicon was developed to identify relevant content, incorporating drug names, slang terms, euphemisms, coded expressions, and emoji-based representations. This lexicon was refined iteratively through preliminary screening to capture evolving linguistic adaptations. Posts were identified through targeted keyword searches and algorithmically recommended content, followed by manual screening for relevance and inclusion criteria.
The dataset was dominated by posts on X, which accounted for 84.7% of the total analyzed content. Facebook represented 8.2% of posts, while LINE and Instagram made up 4.4% and 1.2%, respectively. A small proportion of posts, 1.5%, were identified on other digital sources such as TikTok and Telegram. This distribution highlights the prominence of specific platforms in the Thai digital drug market, although the multi-platform approach captures the broader network of dissemination and cross-referencing that characterizes these online environments.
Stimulants were the most frequently identified substance category, comprising 70.7% of the posts. New psychoactive substances followed, representing 22.2% of the content, while depressants and hallucinogens accounted for 10.4% and 1.6%, respectively. The dominance of stimulant-related content provides context for the composition of the observed digital drug market. The study noted that substance categories were not mutually exclusive, meaning a single post could reference multiple substances, which complicates simple categorical breakdowns but reflects the mixed nature of online sales.
Marketing characteristics were coded across four analytical domains: operational, psychological, conversion, and obfuscation. Indicators of convenience and accessibility were present in 68.7% of the posts, suggesting a low-friction digital environment designed for rapid engagement. These cues included references to express delivery, meet-up arrangements, and small-quantity retail sales. Urgency and scarcity cues, such as limited-time offers or last-lot availability, were observed in 25.9% of posts, indicating the use of time-sensitive promotional strategies to encourage immediate action.
Cross-platform transition cues were identified in 41.8% of the posts, highlighting the role of platform switching in facilitating transactions. These cues included references to LINE IDs, inbox requests, and link redirection instructions. The presence of these signals suggests that public-facing platforms often serve as entry points for broad visibility, while private communication channels are used for subsequent interaction and transaction arrangements. This pattern underscores the importance of considering the broader digital ecosystem rather than individual platforms in isolation when examining illicit drug-market activities.
Coded communication was widespread, with keyword or slang substitutions observed in 74.2% of the posts. This was the most prevalent feature overall, reflecting adaptive communication practices designed to reduce reliance on explicit drug references. Emoji-based representations were identified in 28.6% of posts, further illustrating the use of symbolic language to convey drug-related information. The high prevalence of these obfuscation strategies highlights the limitations of surveillance approaches that rely on static keyword-based detection, as ongoing linguistic adaptation may reduce detection sensitivity over time.
Temporal analysis revealed that the largest proportion of posts, 28.3%, occurred during evening hours between 18:01 and 24:00 Thailand local time. However, the study emphasized that these temporal differences were examined descriptively, and no inferential statistical tests were conducted to evaluate whether this pattern represents a non-uniform distribution or specific behavioral factors. The findings should be interpreted as a descriptive characteristic of the observed dataset rather than evidence of causal mechanisms.
The study acknowledges several limitations, including its reliance on publicly accessible content, which may not capture transaction-related activities occurring within private channels. The observational netnographic design limits the ability to assess user characteristics, engagement behaviors, or transaction outcomes. Additionally, the focus on Thai-language content may have underrepresented English-language drug marketing targeting international or tourist populations. Cross-platform duplicates were retained to reflect dissemination patterns, which may overrepresent the frequency of certain content across platforms.
Despite these limitations, the study provides empirical insights into illicit drug marketing practices within real-world digital environments. The integration of quantitative content analysis with qualitative coding allowed for the identification of co-occurring operational, promotional, and communicative features. The inclusion of multiple social media platforms and the analysis of cross-platform transition cues offer novel insights into how public-facing and private communication channels are interconnected. The use of a structured coding framework, supported by independent coding and consensus procedures, enhances the transparency and reliability of the analytical approach.
The findings have implications for drug policy, digital surveillance, and public health responses. Policy responses may benefit from approaches that consider how accessibility, communication strategies, and platform features interact, rather than addressing these elements in isolation. Monitoring efforts focused on a single platform may not fully capture how transactions occur, suggesting that coordinated approaches considering interactions between public and private channels are necessary. The high prevalence of coded communication highlights the need for adaptive and context-sensitive detection methods to identify evolving patterns in digital drug markets.
The study contributes to a growing body of evidence indicating that illicit drug-related activities are increasingly embedded within mainstream digital platform environments. By characterizing these features within the same multi-platform dataset, the research extends existing evidence on social media drug markets within the Thai digital context. The findings support the adoption of adaptive, platform-aware approaches to surveillance and prevention, emphasizing the importance of considering interactions across digital platforms in public health and policy responses.
To collect naturalistic material while reducing algorithmic bias, researchers standardized retrieval protocols using a dedicated study account without prior browsing history. Operating under Thai language and location settings, the investigation collected public posts alongside linked algorithmic recommendations, including suggested accounts, visible comment threads, and repost networks. Processing was conducted using Python version 3.12.13 alongside NumPy and Pandas. To protect privacy, all personally identifiable information, account names, phone numbers, direct URLs, and post identifiers were removed from the analytical dataset, while engagement metrics were recorded descriptively without serving as inclusion criteria.
Rigorous measurement consistency was established through independent evaluation of a randomly selected 10% subsample comprising 645 posts. Two trained coders evaluated these records across substance categories, operational characteristics, psychological cues, conversion indicators, and obfuscation techniques. Agreement was formally quantified using Cohen’s kappa statistics, which ranged between 0.75 and 0.80 across major analytical domains, demonstrating acceptable inter-coder reliability. Any ambiguous observations or classification disagreements were subsequently reviewed and reconciled through team consensus discussions to maintain coding uniformity.
Substance classifications reflected pragmatic market groupings rather than rigid pharmacological taxonomy. Depressants encompassed heroin, opium, morphine, and methadone, while hallucinogens were represented by LSD. Within coded posts, explicit substance names were substituted with street jargon such as Sai-deed for stimulant users, Num for ecstasy candy, and Pae for white heroin powder, alongside operational phrases such as Song-duan for express delivery, Nut-rub for meet-ups, and Baeng-khai for retail portions.
Geographic metadata was explicitly identifiable in 5,535 posts, representing 85.9% of the sample, while 911 posts or 14.1% contained no province-level indicators. Beyond leading networks, peripheral platforms accounted for distinct subsets of activity, comprising 56 posts on TikTok, 16 on Telegram, eight on Shopee, eight on dedicated external websites, three on Threads, and one on Discord. Across substance distributions, stimulants on X constituted 4,187 posts, or 64.96% of the overall dataset, compared with 208 posts on Facebook, 153 on LINE, nine across other sources, and a single post on Instagram. Meanwhile, new psychoactive substances generated 979 posts on X, 253 on Facebook, 127 on LINE, 36 on Instagram, and 35 across other digital sources.
The observational protocol operated under institutional ethical oversight, receiving a formal Certificate of Exemption from the Human Experimentation Committee at Chiang Mai University’s Research Institute for Health Sciences under Exemption Number 73/2025. In accordance with Thailand’s Personal Data Protection Act and international ethical standards for observational online research, participant consent was not required because all examined posts were published in publicly accessible spaces without researcher intervention. The authors noted that Thailand’s historical drug policies have focused predominantly on law enforcement and supply suppression, arguing that these empirical findings warrant integrating dynamic online monitoring and digital literacy programs to counter evolving promotional tactics across interconnected digital networks.
Subject of Research: Illicit drug marketing on social media in Thailand: a mixed-methods netnography and implications for drug policy
Article Title: Illicit drug marketing on social media in Thailand: a mixed-methods netnography and implications for drug policy
Article References: Thaikla, K., Oo, M. Z., Kaewkamthong, P., & Kaewserm, T. (2026). Illicit drug marketing on social media in Thailand: a mixed-methods netnography and implications for drug policy. Frontiers in Digital Health, 8, Article 1908783. https://doi.org/10.3389/fdgth.2026.1908783
Image Credits: AI Generated
DOI: 10.3389/fdgth.2026.1908783
Keywords: Illicit, drug, marketing, social, media, Thailand, mixed-methods, netnography, implications, policy
News Source: Ophelia Keating. (November 13, 2026). Thai Study Maps Coded Drug Marketing Across Social Media. Scienmag.
