Rumor Detection over Varying Time Windows

TLDR

The study investigates how rumor and non‑rumor characteristics differ and tracks how classification performance evolves across time windows from three days to two months, providing deep insight into cumulative spreading patterns and predictive power shifts. The authors examined a comprehensive set of user, structural, linguistic, and temporal Twitter features and compared their relative predictive strengths. Structural and temporal features distinguish rumors only after a long window, whereas user and linguistic features are effective early; based on this, the authors propose a new algorithm that performs competitively across short and long windows, offering insights into rumor mechanisms and early detection.

Abstract

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.