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Stance Detection Based on User Feature Fusion.

Weidong Huang1, Yuan Wang1, Jinyuan Yang1

  • 1College of Management, Nanjing University of Posts and Telecommunications, Nanjing 210000, China.

Computational Intelligence and Neuroscience
|April 11, 2022
PubMed
Summary
This summary is machine-generated.

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This study introduces a new stance detection model for social media, improving public opinion analysis on false news. The model effectively fuses user features to enhance accuracy in identifying stances on online platforms.

Area of Science:

  • Social Media Analysis
  • Natural Language Processing
  • Public Opinion Research

Background:

  • Social network platforms are widely adopted due to internet growth.
  • Network media significantly influences public opinion and holds social responsibility.
  • Public opinion mining is crucial for enhancing online media content quality and credibility.

Purpose of the Study:

  • To improve the accuracy of stance detection tasks by effectively utilizing user-generated content.
  • To propose a novel stance detection model integrating diverse user features.
  • To analyze public opinion within false news events on social media.

Main Methods:

  • Developed a stance detection model based on user feature fusion.
  • Utilized Weibo netizen comments on false news events as research data.

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  • Integrated user sentiment, cognitive, and text features at the feature layer for training and prediction.

    • Main Results:

    • The proposed model demonstrated improved performance in stance detection.
    • Feature fusion effectively combined user sentiment, cognitive, and text data.
    • Evaluation on a false news microblog comment dataset confirmed the model's effectiveness.

    Conclusions:

    • The user feature fusion approach enhances stance detection accuracy for online media.
    • This method offers a valuable tool for analyzing public opinion on false news.
    • Further research can build upon this model for more robust opinion mining.