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David A. Broniatowski

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Reposted by: David A. Broniatowski

by David A. Broniatowski

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.

by David A. Broniatowski

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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by David A. Broniatowski

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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by David A. Broniatowski

📊 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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by David A. Broniatowski

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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by David A. Broniatowski

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
a cartoon horse is sitting in the back seat of a car and says do you think we 're maybe going too fast
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by David A. Broniatowski

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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by David A. Broniatowski

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 A. Broniatowski

by Ethan Porter — Reposted by: David A. Broniatowski

Reposted by: David A. Broniatowski

Reposted by: David A. Broniatowski

Reposted by: David A. Broniatowski

jbakcoleman.bsky.social
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 A. Broniatowski

mdredze.bsky.social
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 A. Broniatowski

by David A. Broniatowski

Reposted by: David A. Broniatowski

by David A. Broniatowski — Reposted by: David A. Broniatowski

🚨 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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Reposted by: David A. Broniatowski

by David A. Broniatowski

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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by David A. Broniatowski

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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by David A. Broniatowski

6️⃣ Surprisingly, the chatbots resisted adversarial attacks!

Even with tricky prompts, they stayed on topic & avoided harmful advice.
an old man with a beard is talking to a robot and says `` your jedi mind tricks don t work on me '' .
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by David A. Broniatowski

5️⃣ Queries on quitting "cold turkey" or with vapes, gummies, or hypnosis? The chatbots STRUGGLED. 🥴
a robot with the number 11 on its back
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by David A. Broniatowski

4️⃣ Bad news: Some responses contained unsubstantiated claims (22%) 😱

For example, an AI recommended using a necklace to quit smoking. 😭
a man is holding a key in his mouth and smoking a cigarette .
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by David A. Broniatowski

3️⃣ Good news: Chatbots mostly recommended professional help (80%) and used clear language (97%).
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by David A. Broniatowski

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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by David A. Broniatowski

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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by David A. Broniatowski

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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by David A. Broniatowski

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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References

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