What Reddit Reveals About the Reality of Antidepressant Usage
Introduction
For years, I suffered from a chronic skin disease called seborrheic dermatitis. I took appointments with more than a half-dozen dermatologists, but their prescribed treatments had no lasting effect. Out of desperation and searching for answers, I turned to the online community r/sebderm on Reddit. The community is a self-moderated message board where patients trade wisdom, ask questions, and connect through a common struggle.
The information I found on this subreddit was valuable in my own management of the disease. Not every suggestion proffered was effective or scientifically sound. These were, after all, free discussions between untrained professionals, writing only of their lived experiences. However, there was a wealth of practical information not present in UpToDate or in any other authoritative medical resource.
The human capacity to experiment, reflect, and persist is boundless. Members of the community attempted all manner of remedies and reported on those which helped them. These were elevated through the means of the upvote system and then further corroborated or weakened by the experience of others.
It is from my own experience using these forums I arrived at this project idea. Here without any restraints or guardrails was the collected experiences of thousands of patients with various diseases and medications. It was a dataset magnitudes larger than any reasonable clinical trial could ever dream to collect. In addition, with the exception of bots, spam, and astroturfing accounts, the posts were generally honest and authentic.
Although this data had been available for some time, it was impervious to rigorous data analysis. The recent development in large language model technology, however, has opened new possibilities. With clear and thoughtful prompts, LLMs can be used to filter and then analyze posts.
I wanted to use this technique on antidepressant usage for several reasons, among which include my longstanding skepticism toward the liberal prescription practices of these drugs in the United States. In addition, there are so many antidepressants in use today, with no particular drug establishing itself as the gold standard. Furthermore, there is limited guidance on which drug to prescribe and in what order to trial the next one. Providers have unique prescribing practices informed by a mixture of familiarity, availability, and training. One objective of mine was to discover if any such drug would stand out amongst others as being favored by patients.
Approach
First, to acquire the data, I used Reddit’s API. There were three subreddits I expected would have the most discussion regarding antidepressant use: r/mentalhealth, r/depression, and r/anxiety, so I narrowed my search to them.
I further narrowed down the search by collecting posts within the last 10 years that mentioned an antidepressant by name. I then used a large language model, GPT-4o-mini, to filter out posts that were not patient experiences with antidepressants.
Here was the prompt used for filtering:
Does this Reddit post discuss a person's individual experience taking an antidepressant drug?
<post content>
Here are a few examples of posts that would meet this criteria:
Example #1
Title: Has anyone out there been on Citalopram?
Content: Its my first time using them and its making me feel a little weird, i feel light headed and a little bit giddy, is this normal? It said they can take a few weeks to start working for Anxiety and depression so im not sure what im supposed to feel like. Has it helped anyone?
Example #2
Title: Can 10mg of Citalopram a day can cause serotonin syndrome?
Content: Been on it for more than a week. I haven't experienced side effects other than insomnia, but today afternoon I started to feel extremely tired and a bit dizzy. Then a few hours later I felt really hot and weak and sweating like hell (it's not hot at all inside, I have AC running).
I feel a bit better now, but my BP is 145 (usually between 110 and 125 for me) I'm worried as hell and started shaking. Will this shit kill me?
After collecting these candidate posts, I analyzed each with two models. The first model was twitter-XLM-roBERTa-base for Sentiment Analysis, which I used for sentiment analysis. Sentiment analysis describes the emotional valence of a piece of natural language data. The model would produce one of three categorical class labels—“neutral”, “negative”, and “positive”, along with a quantitative confidence score from -1 to 1 (which was not used in the analysis).
Then, I used GPT-4o-mini again to extract structured data from the posts, such as the age, gender, and drugs mentioned (with duration, dosage, and adverse effect data). Not all posts contained such information, so the model was instructed to leave fields blank if needed. To facilitate extraction, I utilized the recently released beta feature by OpenAI called structured outputs, in which you provide JSON schemas describing your desired output and the model responses will reliably conform to it.
All source code can be found on Github.
Results
Many figures show only the top 10 most common drugs by post frequency.
Table 1: Demographics by sentiment
The sentiment of most posts was negative. Most posts did not contain gender data, but among those that did, the split between male and female was roughly equal. Most posts did not contain age information, but among those that did, the patients skewed young.
Table 2: Antidepressant prescriptions compared to post mention frequencies
Bupropion and venlafaxine were mentioned more frequently in posts compared to the proportion of prescriptions.
Table 3: Average sentiment by subreddit
Across all three subreddits, the vast majority of posts had negative sentiment.
Table 4: Sentiment distribution by drug
Citalopram had the largest proportion of posts with neutral and positive sentiment, whereas venlafaxine had the smallest proportion.
Table 5: Average adverse effect per drug
Posts that discussed venlafaxine had the largest average number of adverse effects, with ~1.6 adverse effects per post. This finding is commensurate with venlafaxine posts having the most negative sentiment compared to other drug posts.
Table 7: Adverse effect frequency by selected drugs
Anxiety was consistently the most frequent adverse effect contained within posts for these three most common drugs. It must be noted that this finding could be caused by the LLM miscategorizing a patient’s preexisting symptom of anxiety as an adverse effect of the drug taken. The LLM is explicitly prompted to only collect adverse effects, however, distinguishing these from preexisting clinical symptoms is surely a difficult task.
Discussion
Limitations
There are several limitations to this approach that are immediately evident. Firstly, there is considerable bias informing the distribution of posts available on online discussion forums. For example, individuals with negative experiences are likely more willing to express their frustration online. In addition, there is little assurance of data integrity, uniqueness, or accuracy. A small subset of users with a propensity for sharing personal details online may have generated a disproportionate percentage of total posts, thereby rendering population-level conclusions inappropriate.
In addition, there remains error in the large language model’s ability to filter and subsequently analyze posts. The extent to which this error exists could be calculated through comparison with human evaluators. Unfortunately, this was not able to be completed for this study.
Future work
Despite obvious flaws in the dataset and analytic approach, I believe this methodology has an important role in understanding the effect of therapeutics on patients. As mentioned in the introduction, this dataset online is freely available and growing at an astonishing pace. Additionally, the scale of such self-reported experiences will always be magnitudes greater than those collected in the context of a clinical trial.
Although conclusions drawn from this approach should be surrounded with skepticism, this technique could effectively be used to monitor emerging adverse effects that have gone unnoticed during the pre-launch phases of clinical trials. It can also be used to guide research efforts to understand new diseases.
During the COVID-19 pandemic, for example, many patients documented a persistent set of symptoms following infection, a clinical phenomenon now labeled as long covid. Months before the scientific approach was used to support this new clinical entity, patients informally discussed it on online message boards like Reddit. These discussions can serve as early indicators for public health officials who seek to act quickly to promote the public interest.