When you think of tools for studying drug use and addiction, social media sites like Reddit, TikTok, and YouTube are probably not the first things that come to mind. But stories shared on social media platforms are providing unprecedented insight into the world of drug use.
Until now, researchers studying people’s experiences with addiction have relied primarily on clinical observations and self-report surveys. However, only about 5% of people diagnosed with a substance use disorder seek formal treatment. They represent a small portion of the population with substance use disorders, but until recently there was no easy way to understand the experiences of the other 95%.
Today, millions of people openly discuss their experiences with drugs online, creating a vast collection of first-hand stories about drug use. As a PhD student in informatics with a background in public health, I am using this material to better understand how drug users describe their lives and make sense of their experiences, especially regarding stigma.
These online conversations are reshaping the way researchers think about drug use, addiction, and recovery. Advances in artificial intelligence have made it possible to understand these conversations on a scale not previously possible.
hidden population
The vast majority of people diagnosed with substance use disorders deal with their problems informally, by seeking support from the community, friends, and family, by self-medicating, or by doing nothing at all. However, some people choose to post about their drug use in dedicated online communities such as group forums, often with a level of candor that is difficult to capture in a clinical interview.
Their social media posts provide a window into unscripted, real-time conversations about drug use. For example, Reddit, which is made up of topical communities called subreddits, includes more than 150 interconnected communities dedicated to different aspects of drug use.
In 2024, my colleagues and I analyzed how participants in Reddit’s drug-related forums connected and interacted. We found that they focused on the chemistry and pharmacology of substances, support for drug users, recreational experiences such as festivals and book clubs, recovery support, and harm reduction strategies. We then selected some of the most active communities and developed a system to classify different types of personal information by labeling 500 Reddit posts.
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Policy makers and public health experts have expressed concern that social media is promoting dangerous drug use. Although our study did not assess that issue, it supports the notion that platforms like Reddit and TikTok often serve as a lifeline for people seeking just-in-time support when they need it most.
Using machine learning to analyze an additional 1,000 posts, we found that most users in the forums we focused on were looking for actionable safety information. Posters often asked questions such as how much of a substance is safe to ingest, what interactions to avoid, and how to recognize the signs of trouble.
We observed that these forums functioned as informal harm reduction spaces. People not only share experiences, but also warnings, safety procedures, and genuine concern for each other’s health. When a community member dies from an overdose, their reactions reveal deep sadness and a renewed determination to keep others safe. This is the everyday reality of how people navigate drug use outside of medical settings, and it involves far more nuance and mutual support than critics might expect.
We also investigated TikTok and analyzed over 350 videos from drug-related communities. The most common was pro-recovery content, depicted in 33.9% of the videos analyzed. Only 6.5% of the videos showed active drug use. Similar to Reddit, I frequently saw people stressing safety and consideration.
Why AI is a game changer
Platforms like Reddit, TikTok, and YouTube host millions of posts, videos, and comments, many filled with slang, sarcasm, regional vernacular, or emotional stories. Analyzing this content manually is time-consuming, inconsistent, and virtually impossible to do at scale.
That’s where AI comes in. Traditional machine learning approaches often rely on fixed word lists or keyword matching, which can miss important contextual clues. In contrast, new models, especially large-scale language models like OpenAI’s GPT-5, can understand the nuances, tone, and even underlying intent of messages. This makes it particularly useful when studying complex issues such as drug use and stigma. People often communicate through implication, coded language, or emotional nuance rather than direct speech.
These models can identify patterns across thousands of posts and flag emerging trends. For example, researchers used them to detect changes in how Canadians on social media site X, formerly known as Twitter, discussed cannabis as legalization approached, capturing changes in public attitudes that traditional surveys might have missed.
In another study, researchers found that monitoring Reddit discussions helped predict opioid-related overdose rates. Official government data like the Centers for Disease Control and Prevention typically has a lag of at least six months. But adding near-real-time Reddit data to predictive models could significantly improve its ability to predict overdose deaths and allow public health officials to respond more quickly to emerging crises.
focus on stigma
One of the most difficult aspects of substance use to study and address is stigma.
It is deeply personal, often invisible, and shaped by a person’s identity, relationships, and environment. Researchers have long recognized that stigma, especially when internalized, can undermine self-esteem, worsen mental health, and prevent people from seeking help. However, it is notoriously difficult to understand using traditional research methods.
Most clinical research is based on surveys and interviews conducted on a regular basis. While these snapshots are useful, they can miss how bias plays out in everyday life. Stigma researchers emphasize that to fully understand its effects, we need to pay attention to how people talk about themselves and their experiences over time.
On social media platforms, people often organically discuss stigma in their own words and based on their lived experiences. They may describe being judged by a health care provider, express shame about their own drug use, or reflect on how stigma shapes their relationships. Even when posts do not directly label the experience as stigma, they reveal how stigma is internalized, challenged, and reinforced.
Using large-scale language models, researchers can track these patterns at scale and identify linguistic signals such as expressions of shame, guilt, and despair. In a recent study, my colleagues and I showed that the stigma expressed on Reddit closely aligns with long-standing stigma theory. This suggests that what people share on social media is not fundamentally new or different from what researchers have been studying for years, but instead reflects recognizable stigma processes.
This is important because stigma is one of the biggest barriers to treatment for people with substance use disorders. Understanding how people who use drugs talk about stigma, harm, recovery, and survival in their own words can complement research and clinical research and help inform better public health responses.
By taking these everyday expressions seriously, researchers, clinicians, and policy makers can begin to respond to substance use as it is lived: as messy, evolving, and very human.
