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<p>Drawing on experimental and reverse-engineering approaches, this study situates algorithmic governance within the broader context of digital society and reflects on results of an experiment where multiple virtual accounts were constructed that engage in long-term, realistic interactions with digital platforms in order to penetrate the politicized “black box” of algorithms. It examines how algorithmic governance shapes the heterogeneity of information acquisition among users. Empirical findings reveal that in the digital era, algorithmic governance has become highly complex, sophisticated and concealed. At the information <i>theme</i> level, algorithms increase users’ exposure to diverse topical content. However, at the level of information <i>semantics</i>, algorithms reinforce the filter bubble effect, leading to the narrowing and enclosure of information flows. This study reveals how different individuals are situated by algorithms within relatively bounded positions in semantic vector spaces, and receive information confined to specific semantic dimensions.</p>

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Peering into the algorithmic black box: algorithmic governance and information heterogeneity on digital platforms

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月1日

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  • Heqing Liu,
  • Yucheng Liang

摘要

Drawing on experimental and reverse-engineering approaches, this study situates algorithmic governance within the broader context of digital society and reflects on results of an experiment where multiple virtual accounts were constructed that engage in long-term, realistic interactions with digital platforms in order to penetrate the politicized “black box” of algorithms. It examines how algorithmic governance shapes the heterogeneity of information acquisition among users. Empirical findings reveal that in the digital era, algorithmic governance has become highly complex, sophisticated and concealed. At the information theme level, algorithms increase users’ exposure to diverse topical content. However, at the level of information semantics, algorithms reinforce the filter bubble effect, leading to the narrowing and enclosure of information flows. This study reveals how different individuals are situated by algorithms within relatively bounded positions in semantic vector spaces, and receive information confined to specific semantic dimensions.