Rumor Detection over Varying Time Windows

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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.
Publisher
PUBLIC LIBRARY SCIENCE
Issue Date
2017-01
Language
English
Article Type
Article
Keywords

COMPLEX NETWORK; SOCIAL MEDIA; PSYCHOLOGY; PATTERNS; BEHAVIOR; SERIES; FLOW

Citation

PLOS ONE, v.12, no.1

ISSN
1932-6203
DOI
10.1371/journal.pone.0168344
URI
http://hdl.handle.net/10203/220911
Appears in Collection
CS-Journal Papers(저널논문)
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