.png){kind=icon}
Return
Preventing rumor spread with deep learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2022.116688.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The spreading of false rumors through online media is a serious social problem. The current process of fact checking mostly relies on the responses of crowds or journalists to perform investigations, which can be performed after a rumor has widely spread. This study proposes a bi-directional encoder representations from transformers (BERT)-based model that only takes a claim sentence of a rumor, which can be used to identify false rumors before it goes viral. By evaluating the proposed model with the rumor dataset that is collected from Snopes and Politifact, this study demonstrates the effectiveness of the model that can accurately identify false rumors with only a given claim text. We also reveal that the performance of the models that are trained from the rumors in specific categories (e.g., business, politics) can be improved by transfer learning. Transfer learning uses the model parameters that are trained from a category as an initial state of the model for another category. Our analysis shows that a pre-trained model from a category that deals with a broad range of topics (e.g., fauxtography) is a useful source that can be transferred to other categories (e.g., entertainment). We believe that the proposed model can help mitigate potential social risks such as social turmoil or monetary chaos that are caused by false rumors, as the rumors can be detected before they go viral.
Keywords:
Deep learning
Fact-checking
Rumor detection
Transfer learning
Journal
IF:
7.5
Papers:
3.0W
Citations:
10.2W
Organization
View more organizations
Cited Papers
ShareSaveArsenic removal from aqueous solutions by adsorption using novel MIL-53(Fe) as a highly efficient adsorbent.png)
RSC Advances
RSC AdvancesIF0
PREAI
ShareSaveShareSave
ShareSaveTransport towarów w projekcie One Belt and One Road jako component globalnego łańcucha dostaw
OAAI
IF0
OAAIShareSaveGraphene Oxide‐Hyperbranched Polyethyleneimine Fabricated and Stabilized AuNPs Nanocomposites for Colorimetric Detection of Silver Ions Based on a Non‐Aggregation MechanismChemNanoMat
ChemNanoMatIF0
PREAI
ShareSaveShareSave View more