Penn researchers use AI to surface unreported GLP-1 side effects in Reddit posts
An AI analysis of more than 400,000 Reddit posts found discussions of menstrual changes, fatigue and temperature-related complaints that may not be fully captured in clinical trials or drug labeling.
image:
A close-up of the process the researchers used to analyze Reddit posts: at left is an example of the type of post the researchers fed into an AI-powered analysis, part of which is shown at right.
view moreCredit: Sylvia Zhang
By using AI to analyze more than 400,000 Reddit posts, Penn researchers have identified patient-reported symptoms associated with GLP-1s, the popular weight-loss and diabetes drugs semaglutide and tirzepatide, that may not be fully captured in clinical trials or regulatory documents.
The new study, published in Nature Health, covers more than half a decade of posts from nearly 70,000 Reddit users and highlights two main classes of symptoms that warrant further study: reproductive symptoms, including irregular menstrual cycles, and temperature-related complaints, such as chills and hot flashes.
“Some of the side effects we found, like nausea, are well known, and that shows that the method is picking up a real signal,” says Sharath Chandra Guntuku, Research Associate Professor in Computer and Information Science (CIS) at Penn Engineering and the study’s senior author. “The underreported symptoms are leads that came from patients themselves, unprompted, and clinicians could potentially pay attention to them.”
“Clinical trials generally identify the most dangerous side effects of drugs,” adds Lyle Ungar, Professor in CIS and a co-author on the study. “But they can fail to find what symptoms patients are most concerned about; even though social media is not necessarily representative, a large collection of posts may reflect additional concerns.”
The researchers caution that their findings are not causal. “We can’t say that GLP-1s are actually causing these symptoms,” notes Neil Sehgal, the study’s first author and a doctoral student in CIS advised by Guntuku and Ungar. “But nearly 4% of the Reddit users in our sample reported menstrual irregularities, which would be even higher in a female-only sample. We think that’s a signal worth investigating.”
Studying Social Media for Health
In 2011, Ungar participated in one of the earliest efforts to mine online, user-created content for information about drugs’ adverse effects.
“Online patient communities work a lot like a neighborhood grapevine,” says Ungar. “People who are living with these medications are swapping notes with each other in real time, sharing experiences that rarely make it into a doctor's office visit or an official report.”
In the years since, social media use has only grown, making data from these platforms increasingly promising as a source of information about the side effects of medications, even as the platforms themselves have made accessing the data more difficult. (Guntuku has also published research on strategies for adapting to changes in platform access.)
“Clinical trials are the gold standard, but by design, they are slow,” says Guntuku. “This is not a replacement for trials, but it can move much faster, and that speed matters when a drug goes from niche to mainstream almost overnight.”
Leveraging AI to Analyze Social Media
Until now, the most challenging part of this process, which Guntuku calls "computational social listening,” has been scale.
Because users vary in how they describe their symptoms, the effort required to map individual social media posts to language in the Medical Dictionary for Regulatory Activities (MedDRA), which clinicians use to describe symptoms, limited the amount of data this approach could handle.
Now, large language models like GPT or Gemini have enabled the systematic analysis of social media posts at unprecedented scale. “Large language models have made it possible to do this kind of analysis much faster with a level of standardization that could be difficult to achieve before,” says Sehgal.
Unreported Symptoms
While the population the researchers studied is admittedly not representative — Reddit users are younger, more likely to be male and disproportionately based in the United States — the symptoms described in their collective accounts largely match the known side effects of semaglutide and tirzepatide: about 44% of users in the study described at least one side effect, most commonly some form of gastrointestinal distress.
What stood out was the nontrivial percentage of users who reported symptoms that may not be fully reflected in current drug labeling or routine adverse-event reporting. Nearly 4% of users who reported side effects described reproductive symptoms, including menstrual changes such as intermenstrual bleeding, heavy bleeding and irregular cycles.
Others reported temperature-related complaints, such as chills, feeling cold, hot flashes and fever-like symptoms
In addition, fatigue ranked as the second most common complaint among Reddit users, despite reaching reporting thresholds in relatively few clinical trials.
“These drugs are thought to work by engaging part of the brain called the hypothalamus, which helps regulate a wide variety of hormones,” says Jena Shaw Tronieri, Senior Research Investigator at Penn’s Center for Weight and Eating Disorders and a co-author of the study. “That doesn’t mean the medications are necessarily causing these symptoms, but it could suggest that reports of menstrual changes and body temperature fluctuations are worth studying more systematically.”
Future Directions
In the near term, the researchers hope their findings will encourage clinicians and researchers to take a closer look at the side effects patients are discussing online. “They’re clearly on patients’ minds, and that’s worth paying attention to,” says Sehgal.
The team also hopes to expand the work beyond Reddit and beyond English-language communities to test whether the same patterns appear across different platforms and populations.
“We don’t really know yet whether what we’re seeing on Reddit reflects the experience of GLP-1 users globally, or whether it’s particular to the kind of person who posts on Reddit in the United States,” Ungar says.
Ultimately, the researchers believe this kind of rapid, AI-assisted social media analysis could become a useful way to spot early warning signs around emerging drugs and wellness trends.
For substances that trend quickly online, especially those sold in loosely regulated or unregulated markets, like injectable peptides, patient discussions on platforms like Reddit and TikTok may offer one of the earliest clues to what users are actually experiencing.
“The whole point of this kind of approach is that it can move quickly, and that’s exactly when it’s most valuable,” says Guntuku.
This study was conducted at the University of Pennsylvania School of Engineering and Applied Science. The authors report no outside funding. Tronieri reports receiving an investigator-initiated grant, on behalf of the University of Pennsylvania, from Novo Nordisk and receiving consulting fees from Currax Pharmaceuticals, LLC. The other authors report no conflicts of interest.
A close-up of the process the researchers used to analyze Reddit posts: at left is an example of the type of post the researchers fed into an AI-powered analysis, part of which is shown at right.
Credit
Sylvia Zhang
Journal
Nature Health
Method of Research
Data/statistical analysis
Subject of Research
Not applicable
Article Title
Self-reported side effects of semaglutide and tirzepatide in online communities
Article Publication Date
10-Apr-2026
COI Statement
Jena Shaw Tronieri reports receiving an investigator-initiated grant, on behalf of the University of Pennsylvania, from Novo Nordisk and receiving consulting fees from Currax Pharmaceuticals, LLC. The other authors report no conflicts of interest.
