Rumor Detection over Varying Time Windows.
This study determines the major difference between rumors and non-rumors and explores rumor classification performance levels over varying time windows-from the first three days to nearly two months. A comprehensive set of user, structural, linguistic, and temporal features was examined and their re...
Main Authors: | Sejeong Kwon, Meeyoung Cha, Kyomin Jung |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2017-01-01
|
Series: | PLoS ONE |
Online Access: | http://europepmc.org/articles/PMC5230768?pdf=render |
Similar Items
-
Learning to Detect Incongruence in News Headline and Body Text via a Graph Neural Network
by: Seunghyun Yoon, et al.
Published: (2021-01-01) -
Time-Varying Window Length for Correlation Forecasts
by: Yoontae Jeon, et al.
Published: (2017-12-01) -
An Adaptive Deep Transfer Learning Model for Rumor Detection without Sufficient Identified Rumors
by: Meicheng Guo, et al.
Published: (2020-01-01) -
The Third-Person Effect on Internet Rumors – Political Rumors vs. Showbiz Rumors
by: CHIEN, MENG-HSIEN, et al.
Published: (2017) -
Detecting and Mitigating Rumors in Social Media
by: Islam, Mohammad Raihanul
Published: (2020)