Social radar Doppler: multi-scenario validation ofsocial media-based crisis early warning

Hetian CHEN; Lili FAN; Guangyu SHI

doi:10.1631/ENG.ITEE.2026.0120

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Social radar Doppler: multi-scenario validation ofsocial media-based crisis early warning

Regular Papers | Updated：2026-10-08

- Social radar Doppler: multi-scenario validation ofsocial media-based crisis early warning
- Social radar Doppler: multi-scenario validation ofsocial media-based crisis early warning
- ENGINEERING Information Technology & Electronic Engineering Vol. 27, Issue 9, (2026)
- Affiliations：
  
  School of Software, Xinjiang University, Urumqi 830046, China
  School of AI, Beijing Institute of Technology, Beijing 100081, China
  School of AI, Beijing Institute of Technology, Zhuhai 519088, China
- Author bio：
  
  ✉Lili FAN, lilifan@bit.edu.cn
- Funds：
- DOI：10.1631/ENG.ITEE.2026.0120
  CLC： TP277;TP391
- Received：27 April 2026，
  
  Revised：2026-07-07，
  
  Online First：14 August 2026，
  
  Published：01 September 2026
- Accepted：
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Hetian CHEN, Lili FAN, Guangyu SHI. Social radar Doppler: multi-scenario validation ofsocial media-based crisis early warning[J]. ENGINEERING Information Technology & Electronic Engineering, 2026, 27(9): 260120-11.
DOI：

Hetian CHEN, Lili FAN, Guangyu SHI. Social radar Doppler: multi-scenario validation ofsocial media-based crisis early warning[J]. ENGINEERING Information Technology & Electronic Engineering, 2026, 27(9): 260120-11. DOI： 10.1631/ENG.ITEE.2026.0120.

摘要

Abstract

Social media is an essential data source for risk perception of complex social events. However

existing early warning methods mainly rely on popularity peaks

task-specific semantic labels

or observable propagation structures

often ignoring propagation rhythm changes prior to a crisis. This paper proposes an unsupervised online framework for social media crisis early warning. Aggregating heterogeneous behaviors into a unified intensity sequence

it detects anomalously accelerated propagation phases relative to historical baselines through causal smoothing

rate-of-change extraction

rolling quantile thresholds

persistence constraints

and cooling mechanisms. Compared to intensity-driven methods

it focuses on “whether the system is accelerating toward a risk state” rather than “whether it has reached a high popularity state;” compared to semantic- and structure-driven methods

it has weaker dependencies on annotations

ontologies

and complete propagation graphs. Strict causal replay experiments on the Russia–Ukraine crisis Weibo dataset and the Douban Movie Short Comments dataset demonstrate that the framework captures aggregative rising processes before key events early with low false alarm interference

exhibiting structural reusability across scenarios. Results indicate that detecting propagation dynamics changes offers a lightweight

interpretable

and transferable path for online early warning in complex systems.

关键词

Keywords

references

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