Comment on A Comprehensive Data Repository for Fake Health News Detection by king99
comments By Enyan Dai and Suhang Wang, PennState. You may not be surprised that 81.5% of the U.S. population search for health information online. Actually, around 70% of them treat the internet as the first source to get healthcare information.
- ▪comments By Enyan Dai and Suhang Wang, PennState.
- ▪You may not be surprised that 81.5% of the U.S. population search for health information online.
- ▪Actually, around 70% of them treat the internet as the first source to get healthcare information.
KDnuggets » Comments Feed files mainly under ai. We currently carry 9 of its stories.
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | KDnuggets » Comments Feed |
| Canonical URL | https://www.kdnuggets.com/2020/03/data-repository-fake-health-news.html#comment-182591 |
| Publication time | Sat, 25 Jul 2026 01:00:55 +0000 |
| Retrieval time | 2026-07-25T01:12:55.855Z |
| Last seen | 2026-07-25T01:12:55.855Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | qtLfPtvWeDHm |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
comments By Enyan Dai and Suhang Wang, PennState. You may not be surprised that 81.5% of the U.S. population search for health information online. Actually, around 70% of them treat the internet as the first source to get healthcare information. However, a large number of fake health news is poisoning the online environment. For example, a popular health news piece, “Ginger is 10,000x more effective at killing cancer than chemo,” which generated around 1 million engagements on Facebook, turned out to be a misleading claim. These statements are threatening public health. One of the solutions is developing machine learning models to automatically detect the fake news and even present explanations of the detection results.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at KDnuggets » Comments Feed.