David Broniatowski
@broniatowski.bsky.social
2.2K followers 46 following 48 posts
Professor at The George Washington University in Engineering Management and Systems Engineering.
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David Broniatowski @broniatowski.bsky.social · Jan 14
🧵 New preprint alert with @mdredze.bsky.social

dx.doi.org/10.21203/rs....

"Explaining Twitter's Inability to Reduce Vaccine Misinformation During the COVID-19 Pandemic" finds that Twitter’s moderation efforts failed and explains why.

Spoiler:
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David Broniatowski @broniatowski.bsky.social · May 8
Thanks for the note! Your work absolutely inspired ours and the “one size fits all” threshold refers to another paper by another set of authors.
David Broniatowski @broniatowski.bsky.social · May 8
Proud to work with an amazing team: @yunkangyang.bsky.social , Ramesh Paudel, Jordan McShan, @matthindman.bsky.social , and Howie Huang.

And grateful to the Knight Foundation for supporting work on transparent methods for information integrity.

Full paper: doi.org/10.1038/s415...
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David Broniatowski @broniatowski.bsky.social · May 8
This dual-detection strategy is key to spotting the more subtle stuff. This paper builds on our broader interest in platform dynamics and misinformation. But the method is applicable to any kind of content diffusion—hashtags, images, even AI-generated text.
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David Broniatowski @broniatowski.bsky.social · May 8
📊 About 23% of pages showed signs of coordination—some of it expected (e.g., news syndication), but a lot of it less transparent.

The method helped us identify both overt and covert networks. Some shared links every few seconds. Others did it slowly but repeatedly, evading the usual red flags.
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David Broniatowski @broniatowski.bsky.social · May 8
Using mixture models, we derive empirically grounded thresholds that tell us when the pattern is too consistent to be chance.

No guesswork. No black box.

We applied this to over 11 million Facebook posts from 16K high-engagement pages.
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David Broniatowski @broniatowski.bsky.social · May 8
Most detection methods use arbitrary cutoffs (like “shared within 10 seconds”), which don’t hold up across platforms or contexts.

We do better.

Our method looks at two things:
1️⃣ How fast two pages share the same link
2️⃣ How often they do it over time
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David Broniatowski @broniatowski.bsky.social · May 8
Our approach is platform-agnostic, scalable, and designed to support transparency and accountability. Why does this matter?

Coordinated sharing—whether by media groups, advocacy networks, or covert actors—can shape what people see online.
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David Broniatowski @broniatowski.bsky.social · May 8
Just published: our new paper in Scientific Reports
📄 “Coordinated Link Sharing on Facebook”
doi.org/10.1038/s415...

We introduce a statistically grounded, human-interpretable method to detect coordination on social media.
Coordinated link sharing on Facebook - Scientific Reports
Scientific Reports - Coordinated link sharing on Facebook
doi.org
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Reposted by David Broniatowski
jbakcoleman.bsky.social
Joe Bak-Coleman @jbakcoleman.bsky.social · Feb 24
The primary donor to #openscience seems very supportive of massive cuts to scientific funding that jeopardize research. I don’t think it’s edgy at this point to say that reform is being weaponized and the community needs to stand up against it.
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Reposted by David Broniatowski
mdredze.bsky.social
Mark Dredze @mdredze.bsky.social · Feb 19
Please read and share this excellent FAQ on University indirect costs by my friend @broniatowski.bsky.social

He explains why these funds are essential and a critical investment for research in the United States.

www.linkedin.com/posts/david-...
David Broniatowski on LinkedIn: NIH has just announced that it will save $4 billion by capping university… | 61 comments
NIH has just announced that it will save $4 billion by capping university indirect costs on federal grants. Will this actually save money? Bottom line: No. It… | 61 comments on LinkedIn
www.linkedin.com
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Reposted by David Broniatowski
lorien-abroms.bsky.social
lorien-abroms.bsky.social @lorien-abroms.bsky.social · Feb 3
Our new publication asking whether ChatGPT chatbots can reliably give advice to help people quit smoking? We examined chatbots including WHO's Sarah in providing assistance on quitting smoking. @who.int @broniatowski.bsky.social
David Broniatowski @broniatowski.bsky.social · Jan 31
🚨 New paper alert! 🚨

Ever wondered if AI chatbots actually follow public health guidelines when giving smoking cessation advice? 🤖🚭

Our latest study in J Med Internet Res evaluates ChatGPT-based chatbots on this! 🧵👇

📜 Paper: www.jmir.org/2025/1/e66896
Assessing the Adherence of ChatGPT Chatbots to Public Health Guidelines for Smoking Cessation: Content Analysis
Background: Large language model (LLM) artificial intelligence chatbots using generative language can offer smoking cessation information and advice. However, little is known about the reliability of ...
www.jmir.org
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David Broniatowski @broniatowski.bsky.social · Jan 31
This project -- led by
@lorien.bsky.social , in collaboration with
Artin Yousefi, Christina Wysota, and Tien-Chin Wu -- was supported by a TRAILS seed grant.

Better AI can help people make more informed, healthier decisions! 🚭🤖
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David Broniatowski @broniatowski.bsky.social · Jan 31
7️⃣ So, what’s the takeaway?

🔹 Chatbots can be helpful, but they need better training.
🔹 WHO’s Sarah outperformed the others.
🔹 AI can easily be prompted to give weird advice. 😵‍💫
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David Broniatowski @broniatowski.bsky.social · Jan 31
6️⃣ Surprisingly, the chatbots resisted adversarial attacks!

Even with tricky prompts, they stayed on topic & avoided harmful advice.
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David Broniatowski @broniatowski.bsky.social · Jan 31
5️⃣ Queries on quitting "cold turkey" or with vapes, gummies, or hypnosis? The chatbots STRUGGLED. 🥴
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David Broniatowski @broniatowski.bsky.social · Jan 31
4️⃣ Bad news: Some responses contained unsubstantiated claims (22%) 😱

For example, an AI recommended using a necklace to quit smoking. 😭
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David Broniatowski @broniatowski.bsky.social · Jan 31
3️⃣ Good news: Chatbots mostly recommended professional help (80%) and used clear language (97%).
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David Broniatowski @broniatowski.bsky.social · Jan 31
2️⃣ Across chatbots, responses followed guidelines only 57% of the time.

Sarah led with 72% adherence, while BeFreeGPT & BasicGPT lagged at ~50%. 😬
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David Broniatowski @broniatowski.bsky.social · Jan 31
1️⃣ First, we tested three AI chatbots: WHO’s Sarah, BeFreeGPT, and BasicGPT.

Do they give reliable quitting advice? Let’s find out! 🔎
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David Broniatowski @broniatowski.bsky.social · Jan 31
🚨 New paper alert! 🚨

Ever wondered if AI chatbots actually follow public health guidelines when giving smoking cessation advice? 🤖🚭

Our latest study in J Med Internet Res evaluates ChatGPT-based chatbots on this! 🧵👇

📜 Paper: www.jmir.org/2025/1/e66896
Assessing the Adherence of ChatGPT Chatbots to Public Health Guidelines for Smoking Cessation: Content Analysis
Background: Large language model (LLM) artificial intelligence chatbots using generative language can offer smoking cessation information and advice. However, little is known about the reliability of ...
www.jmir.org
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David Broniatowski @broniatowski.bsky.social · Jan 14
For more insights into platform design, systems engineering, and what this means for the future of online content, read our full preprint: dx.doi.org/10.21203/rs....
#SystemsEngineering #SocialMedia
Explaining Twitter’s inability to reduce vaccine misinformation during the COVID-19 pandemic
Users dissatisfied with exposure to objectionable online content have begun to migrate en masse to new social media platforms. These new platforms share architectural features with legacy platforms, b...
dx.doi.org
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David Broniatowski @broniatowski.bsky.social · Jan 14
Platforms cannot tack on moderation policies after the fact and expect success. It’s like trying to build the roof before the foundation.
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David Broniatowski @broniatowski.bsky.social · Jan 14
The risks:
🕒 Best case? Wasted time and money.
⚖️ Worst case?
- Liability
- Reputational harm
- Undermines users’ rights & agency through bad information
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David Broniatowski @broniatowski.bsky.social · Jan 14
Why does this matter now?

Because other platforms (such as this one!) aim to replicate Twitter’s architecture while promising moderation. Spoiler: They may face the same issues.
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David Broniatowski @broniatowski.bsky.social · Jan 14
Key findings:
📉 Policies reduced misinformation in specific clusters .
📈 Platform-wide, vaccine-skeptical content increased, became more viral, and linked to low-quality sources.
🔍 Deplatforming reduced content in some clusters but virality and low-quality links increased.
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