This article introduces a text classification framework designed to identify helpful user-generated content within online knowledge-sharing communities. Built upon the Knowledge Adoption Model, this framework incorporates dimensions of argument quality and source credibility, significantly enhancing traditional text classification techniques that rely solely on lexical features. Empirical evaluations using data from a popular online community confirm the effectiveness of including these multidimensional factors in improving content helpfulness identification.
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Wang, Gang, Xiaomo Liu, and Weiguo Fan. "A knowledge adoption model based framework for finding helpful user-generated contents in online communities." (2011).
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Members
- Voyage
- GazzaGarratt
- rainx
- Adriano Faria
- opentype
- Dilip
- Nathan Explosion
- YourSharona
- JoelR
- DawPi
- Live Games
- bernhara
- Kentraiyle Robinson
- IC Essentials
- Myr
- Chris Anderson
- Como
- Brian
- MichaelR
- Edward Ellas
- Square Wheels
- IPS THEME
- aXenDev
- PrettyPixels
- Analog
- Denis Dyack
- A Zayed
- Labis
- DursunKaptan
- V0RT3X
- MissB
- TheLlamaman
- Andy Y
- envy
- eivindsimensen
- JoeyM
- aLEX49566
- adik
- Codepixel
- alsl sndnxnx
- burnyourfeelings
- N700
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- Marius
- Matt
- terabyte
- Thomas Taschler
- Surpac
- JoshB
- Ioannis D
- abobader
- ReyDev
- Richard Arch
- bdmusic 24
- Majster87
- TomCat
- Paul
- Pmw
- Torgeir Rui
- Kammer et
- Nicolas PC
- XwReK
- Claudia999
- Kirill Gromov
- Synergy
- bing11
- Marvin Moore
- ArashDev
- onlyME
- ali hagi
- StevenM
- NewVicious
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- TracyIsland
- Andhrafriends Admin
- Daffy
- hyprem
- GuitarGathering
- Tripp
- Askancy
- MLK
- Jelly Belly
- eveneme eveneme
- Nomad
- Morphe
- lordi
- shahed
- John Horton
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- Nomer3
- Dennis Maidon
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- wulfx01
- Matthew Hawley
- Verto
- George Anderssen
- Toby
- Cheryl