Peering into the algorithmic black box
Algorithmic governance and information heterogeneity on digital platforms
Bibliographic Data
| ID | 21226531 |
|---|---|
| Authors | Heqing Liu (0000-0002-6977-1458, Huazhong University of Science and Technology, corresponding author), Yucheng Liang (0000-0001-9917-5625, Sun Yat-sen University) |
| Year | 2026 |
| Volume | 13 |
| Issue | 1 |
| Publication date | 2026-07-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | The Journal of Chinese Sociology (JOURNAL) |
| Journal identifiers | ISSN: 2198-2635 • E-ISSN: 2198-2635 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1186/s40711-026-00264-4 |
| OpenAlex | W7166734446 |
| Language | EN |
| References cited | 27 |
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
Argument (complex analysis) · Context (archaeology) · Corporate governance · Filter (signal processing) · Information governance · Peering · Semantics (computer science) · Situated · Ethics and Social Impacts of AI · Privacy, Security, and Data Protection · Social Media and Politics
We Are Data
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Exposure diversity as a design principle for recommender systems
How We Became Our Data
Reassembling the Social
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What kind of news gatekeepers do we want machines to be? Filter bubbles, fragmentation, and the normative dimensions of algorithmic recommendations
Word Embeddings for the Analysis of Ideological Placement in Parliamentary Corpora
Thinking critically about and researching algorithms
The ethics of algorithms
Algorithms as culture
Reassembling Social Science Methods
The Society of Algorithms
| Highly cited | No |
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