Record Details

Rumor detection over varying time windows.

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Rumor detection over varying time windows.
 
Identifier https://doi.org/10.7910/DVN/BFGAVZ
 
Creator Kwon, Sejeong
Cha, Meeyoung
Jung, Kyomin
 
Publisher Harvard Dataverse
 
Description 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 relative strength was compared from near-complete date of Twitter. Our contribution is at providing deep insight into the cumulative spreading patterns of rumors over time as well as at tracking the precise changes in predictive powers across rumor features. Statistical analysis finds that structural and temporal features distinguish rumors from non-rumors over a long-term window, yet they are not available during the initial propagation phase. In contrast, user and linguistic features are readily available and act as a good indicator during the initial propagation phase. Based on these findings, we suggest a new rumor classification algorithm that achieves competitive accuracy over both short and long time windows. These findings provide new insights for explaining rumor mechanism theories and for identifying features of early rumor detection.
 
Subject Computer and Information Science
Social Sciences
Rumor detection
time windows
Social media
Machine learning
Rumor theory
 
Language English
 
Contributor Kwon, Sejeong