I don’t have good suggestions for names, but I reports of suspected fraud should get peer reviewed before publication instead of blogged, just as any other serious research product does. Preregistering criteria for identifying data anomalies seems reasonable as well.
Jan 11, 2025 at 7:44 PM
“Paging Dr. Fraud”: The Fake Publishers That Are Ruining Science
For years, spurious journals have proliferated online, promising academic credibility in exchange for cash.
Fakebook: Using Data Science to Spot Fake News
Katika Kühnreich (@Katika@chaos.social)
Attached: 1 image #SocialMedia of #realnameregistration , oh sorry, they call it real #humans , that advertises with " #AI " #slop #wsocial is more than problematic And already has #bots crawling it's #network Now they show that they are problematic concerning #aesthetics as well & advertise with tasteless #aislop @bildoperationen@tldr.nettime.org #realnameregistration is highly dangerous & if the company that wants to have your most private data can't save there net from bots they're not very trustworthy, are they? #tech
thebigtradeoff
thebigtradeoff - the new blog in LiveJournal. There should be new interesting records soon.
Fakebook: Using Data Science to Spot Fake News
As fake news permeates throughout social media outlets (like Facebook), it's more important than ever to identify what is fact vs. fiction. This is especially true in the age of COVID where the spread of misinformation can impact the spread of the virus. Data scientists are emerging on the front lines of this fight, using programs to help them identify fake news sources. Led by Dr. Teon Brooks, Josh Modeste, and Steven Azeka from the Gotham Data Clinic (fka CiELabs), this workshop will explore the “life cycle” of scientific research and how scientists come to consensus about how we interpret data. We’ll examine what to look for in credible news sources, and the consequences of leaning on not-so-trusty pseudoscientific sources. Then you’ll put that knowledge to work by developing a rubric for identifying fake news. No experience necessary! https://www.eventbrite.com/e/fakebook-using-data-science-to-spot-fake-news-tickets-137833492687
[109] Data Falsificada (Part 1): "Clusterfake" - Data Colada
This is the introduction to a four-part series of posts detailing evidence of fraud in four academic papers co-authored by Harvard Business School Professor
Let's normalize debunking each other
The spread of misinformation has become an epidemic, but information hygiene can help.
Librarian finds ‘preposterous number’ of fake references in paper from Springer Nature journal
Gunnar Ridderström/Pexels As a hospital librarian, Jessica Waite is typically successful at tracking down elusive articles for clinicians at Royal Hallamshire Hospital in England. So when a colleag…
Science discussions of retracted articles on Bluesky: public scrutiny or misinformation spreading?
Post-publication peer review (PPPR) has emerged as an important supplement to traditional peer review, with social media playing a growing role in publicising potential problems in published research. However, it remains unclear whether social media discussions of retracted articles primarily reflect good practices, such as exposing flaws and acknowledging retraction status, or bad practices, such as overlooking retractions and continuing to disseminate scientific misinformation. In this study, we collected Bluesky posts referencing scholarly articles from Altmetric and retrieved metadata for the referenced articles using OpenAlex. The final dataset included 284 retracted articles with 79 pre-retraction posts and 857 post-retraction posts, 59 retraction notices with 186 posts, and 609,461 non-retracted articles with 1,344,756 posts. We manually coded Bluesky posts discussing retracted articles to identify instances of good and bad practice. The results show that posts demonstrating good practice (89.9%) substantially outnumbered those demonstrating bad practice (10.1%). Posts reflecting good practice also had more user engagement. In the pre-retraction phase, good practice posts constituted a slight minority (43.0%), whereas in the post-retraction phase they were dominant (94.2%). Most negative posts in the pre-retraction phase (90.0%) had good practice while only 17.3% positive posts in the post-retraction phase showed bad practice. Thus, sentiment analysis can be helpful to filter posts that could flag potential flaws before retraction, but it may struggle to accurately identify the spread of misinformation after retraction. More broadly, this study highlights the potential of Bluesky to support responsible scientific communication, public scrutiny, and research integrity.
Science discussions of retracted articles on Bluesky: public scrutiny or misinformation spreading?
Post-publication peer review (PPPR) has emerged as an important supplement to traditional peer review, with social media playing a growing role in publicising potential problems in published research. However, it remains unclear whether social media discussions of retracted articles primarily reflect good practices, such as exposing flaws and acknowledging retraction status, or bad practices, such as overlooking retractions and continuing to disseminate scientific misinformation. In this study, we collected Bluesky posts referencing scholarly articles from Altmetric and retrieved metadata for the referenced articles using OpenAlex. The final dataset included 284 retracted articles with 79 pre-retraction posts and 857 post-retraction posts, 59 retraction notices with 186 posts, and 609,461 non-retracted articles with 1,344,756 posts. We manually coded Bluesky posts discussing retracted articles to identify instances of good and bad practice. The results show that posts demonstrating good practice (89.9%) substantially outnumbered those demonstrating bad practice (10.1%). Posts reflecting good practice also had more user engagement. In the pre-retraction phase, good practice posts constituted a slight minority (43.0%), whereas in the post-retraction phase they were dominant (94.2%). Most negative posts in the pre-retraction phase (90.0%) had good practice while only 17.3% positive posts in the post-retraction phase showed bad practice. Thus, sentiment analysis can be helpful to filter posts that could flag potential flaws before retraction, but it may struggle to accurately identify the spread of misinformation after retraction. More broadly, this study highlights the potential of Bluesky to support responsible scientific communication, public scrutiny, and research integrity.
This is a FRAUDULENT paper, AI-generated. My name was used as an author and I had nothing to do with it, never saw it until today e-pubmed.co.uk/journals/digital-health-imple… The "Editors" Angelo Rossi Mori, David Mensah, and Zarnie Khadjesari and this "Journal" should be reported.
This is a FRAUDULENT paper, AI-generated. My name was used as an author and I had nothing to do with it, never saw it until today e-pubmed.co.uk/journals/digital-health-imple… The "Editors" Angelo Rossi Mori, David Mensah, and Zarnie Khadjesari and this "Journal" should be reported.
