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This study analyzed Sina Weibo's Hot Search List (HSL), revealing how hashtag popularity dynamics are influenced by circadian rhythms and platform interventions. International politics hashtags were over-represented at anchored ranks, suggesting potential public opinion manipulation.

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Area of Science:

  • Social Media Analytics
  • Computational Social Science
  • Information Science

Background:

  • Microblogging platforms like Sina Weibo are crucial for information dissemination and public opinion formation.
  • Trending topics are typically presented via ranking lists, reflecting user engagement and platform dynamics.

Purpose of the Study:

  • To investigate public attention dynamics on Sina Weibo's Hot Search List (HSL).
  • To characterize hashtag rank dynamics, including time spent, appearance times, rank diversity, and trajectories.
  • To identify platform intervention patterns and their potential impact on public opinion.

Main Methods:

  • Analysis of hashtag ranking dynamics on Sina Weibo's HSL.
  • Characterization using time spent, appearance time, rank diversity, and ranking trajectories.
  • Application of machine learning clustering to categorize rank trajectories.
  • Development of a model to explain ranking anchoring effects.

Main Results:

  • Circadian rhythms significantly affect hashtag popularity.
  • Distinct categories of hashtag rank trajectories were identified using machine learning.
  • Anomalies in ranking dynamics suggest platform provider intervention, specifically hashtag anchoring.
  • International politics hashtags showed over-representation at anchored ranks on the HSL.

Conclusions:

  • Hashtag popularity on microblogging platforms exhibits complex dynamics influenced by user behavior and platform algorithms.
  • Platform provider interventions, such as anchoring, can manipulate the visibility of certain topics.
  • The over-representation of international politics hashtags at anchored ranks raises concerns about potential public opinion manipulation.