This paper addresses the challenge of detecting medical misinformation online by presenting a new labeled dataset of misinformative and non-misinformative comments from a health discussion forum. The dataset was created through information retrieval techniques and a detailed labeling strategy. By employing Recursive Feature Elimination to identify the most relevant features, the study achieved a classification accuracy of 90.1%. This dataset, with a high proportion of non-misinformative comments (85.8%), aims to support the development of machine learning systems for identifying misinformation in user-generated medical content.
-
Kinsora, Alexander, et al. "Creating a labeled dataset for medical misinformation in health forums." 2017 IEEE International Conference on Healthcare Informatics (ICHI). IEEE, 2017.
-
Members
- JoelR
- Chris Anderson
- Myr
- Live Games
- IC Essentials
- Nathan Explosion
- Square Wheels
- bernhara
- Auto Evoke
- opentype
- ReyDev
- send2yoni
- Brian
- A Zayed
- Adriano Faria
- terabyte
- Dilip
- ZLTRGO
- adik
- master963
- DawPi
- eivindsimensen
- envy
- onlyME
- V0RT3X
- GazzaGarratt
- Analog
- Voyage
- Paul Kaiser
- Como
- N700
- Paul
- TracyIsland
- Andy Y
- Omar Barbeytia carretero
- JoeyM
- Ryancoolround
- rainx
- YourSharona
- Kentraiyle Robinson
- MichaelR
- Edward Ellas
- IPS THEME
- aXenDev
- PrettyPixels
- Denis Dyack
- Labis
- DursunKaptan
- MissB
- TheLlamaman
- aLEX49566
- Codepixel
- alsl sndnxnx
- burnyourfeelings
- isvans
- Marius
- Matt
- Thomas Taschler
- Surpac
- JoshB
- Ioannis D
- abobader
- Richard Arch
- bdmusic 24
- Majster87
- TomCat
- Pmw
- Torgeir Rui
- Kammer et
- Nicolas PC
- XwReK
- Claudia999
- Kirill Gromov
- Synergy
- bing11
- Marcin Martyniak
- ArashDev
- ali hagi
- StevenM
- NewVicious
- lukash
- Andhrafriends Admin
- Daffy
- hyprem
- GuitarGathering
- Tripp
- Askancy
- MLK
- Jelly Belly
- eveneme eveneme
- Nomad
- Morphe
- lordi
- shahed
- John Horton
- PayMap
- Serval
- Nomer3
- Dennis Maidon
- Zennuie