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Rumor Detection over Varying Time Windows.

Sejeong Kwon1, Meeyoung Cha1, Kyomin Jung2

  • 1Graduate School of Culture Technology, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.

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This study differentiates rumors from non-rumors using user, structural, linguistic, and temporal features. Findings reveal distinct feature effectiveness over time, aiding early rumor detection and classification.

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Area of Science:

  • Computational Social Science
  • Information Science
  • Network Science

Background:

  • Rumors spread rapidly on social media platforms like Twitter.
  • Distinguishing between rumors and non-rumors is crucial for information veracity.
  • Understanding rumor propagation dynamics over time is an ongoing research challenge.

Purpose of the Study:

  • To identify key differences between rumors and non-rumors.
  • To analyze rumor classification performance across different time windows.
  • To investigate the evolving predictive power of various rumor features.

Main Methods:

  • Analysis of user, structural, linguistic, and temporal features of Twitter data.
  • Comparison of feature effectiveness for rumor classification over short (3 days) and long (2 months) timeframes.
  • Development and evaluation of a novel rumor classification algorithm.

Main Results:

  • Structural and temporal features effectively distinguish rumors in the long term but are absent early on.
  • User and linguistic features are strong early indicators of rumor propagation.
  • The proposed algorithm demonstrates competitive accuracy across both short and long time windows.

Conclusions:

  • Rumor detection strategies should adapt based on feature availability over time.
  • Early detection relies on user and linguistic cues, while long-term analysis benefits from structural and temporal patterns.
  • This research offers insights into rumor mechanisms and early detection feature identification.