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Related Experiment Video

Updated: Feb 22, 2026

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation
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User emotion for modeling retweeting behaviors.

Jinpeng Chen1, Yu Liu2, Ming Zou2

  • 1School of Software Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Neural Networks : the Official Journal of the International Neural Network Society
|September 26, 2017
PubMed
Summary

User emotion significantly impacts retweet behavior on social media. This study introduces a novel framework that predicts retweets by incorporating emotional signals, improving accuracy over existing methods.

Keywords:
EmotionFactor analysisRetweeting behaviorSocial networkTwitter

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Area of Science:

  • Social Network Analysis
  • Computational Social Science
  • Information Science

Background:

  • Microblogs like Twitter are key information dissemination channels.
  • Understanding retweet dynamics is crucial for information spread.
  • Existing research often overlooks the role of user emotion in retweeting.

Purpose of the Study:

  • To investigate the influence of user emotion on retweet behavior.
  • To develop a predictive framework for retweets incorporating emotional status.
  • To determine if users retweet content aligning with their current mood.

Main Methods:

  • Constructed an emotion detection model using dual emotional signals.
  • Extracted potential retweeted friends and relevant tweets.
  • Employed a Learn-to-Rank method to obtain Top-N retweets.

Main Results:

  • The proposed framework significantly outperforms conventional retweet prediction approaches.
  • Demonstrated an average Precision increase of 15.2% and 11.2% in the Top-N stage on Twitter data.
  • Validated findings on both Twitter and Obama-McCain Debate datasets.

Conclusions:

  • User emotion is a vital factor influencing retweetability.
  • Incorporating emotional status enhances the accuracy of retweet prediction.
  • The developed framework offers a more nuanced understanding of social media information spread.