Rumor Detection over Varying Time Windows

PLoS One. 2017 Jan 12;12(1):e0168344. doi: 10.1371/journal.pone.0168344. eCollection 2017.


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.

MeSH terms

  • Humans
  • Information Dissemination*
  • Models, Theoretical*

Grants and funding

M.C. is with the Graduate School of Culture Technology at KAIST, Korea, and was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (R0184-15-1037) and the Brain Korea 21 Plus Project. K.J. is with the Department of Electrical and Computer Engineering, ASRI, Seoul National University, Korea, and was supported by the National Research Foundation of Korea grant (2016R1A2B2009759) funded by the Korea government (MSIP) and the Brain Korea 21 Plus Project. M.C. and K.J. were jointly supported by the Ministry of Trade, Industry & Energy (MOTIE, Korea) under Industrial Technology Innovation Program (No.10073144).