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Machine Learning Methods to Predict Social Media Disaster Rumor Refuters.

Shihang Wang1, Zongmin Li2, Yuhong Wang3

  • 1Business School, Sichuan University, Chengdu 610064, China. 18280446697@163.com.

International Journal of Environmental Research and Public Health
|April 27, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a method to identify individuals who counter disaster rumors online. Analyzing microblog content with natural language processing and machine learning significantly improves prediction accuracy for anti-rumor spreaders.

Keywords:
NLPXGBoostdisaster-relatedgroup behaviormachine learningrumor refutation

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Area of Science:

  • Social Network Analysis
  • Computational Social Science
  • Information Science

Background:

  • Disaster-related rumors spread rapidly on social media.
  • Identifying individuals who actively refute these rumors is crucial for mitigation.
  • Existing methods often overlook the content and sentiment of user communications.

Purpose of the Study:

  • To develop a robust methodology for distinguishing disaster-related anti-rumor spreaders.
  • To enhance prediction models by incorporating microblog content analysis.
  • To evaluate the effectiveness of various machine learning techniques for this task.

Main Methods:

  • Collected microblog data from 3793 Sina Weibo users.
  • Applied natural language processing (NLP) for sentiment and short text similarity analysis.
  • Compared logistic regression, support vector machines, random forest, and extreme gradient boosting (XGBoost) models.

Main Results:

  • The XGBoost model demonstrated superior performance in predicting anti-rumor spreaders.
  • NLP-derived features (sentiment, similarity) significantly improved prediction precision and robustness.
  • The number of user microblogs was a valuable predictive feature.

Conclusions:

  • The proposed methodology effectively identifies anti-rumor spreaders by analyzing microblog content.
  • This approach offers a novel and effective decision support system for combating online rumors.
  • Future work will focus on validating and optimizing the methodology for closed-loop communication channels like WeChat.