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Stance Detection Based on User Feature Fusion.

Weidong Huang1, Yuan Wang1, Jinyuan Yang1

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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.

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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.
  • 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.