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In the digital age, the main power over how information is spread is moving away from professional editors and toward smart algorithms; this shows a big shift, from editors acting as gatekeepers to algorithms acting as gatekeepers. This paper takes that shift, from editors deciding what gets through to algorithms deciding what gets through, as its main topic, and it also uses examples such as Douyin and Jinri Toutiao to talk about how these mechanisms work and what social effects they have. The results show that, when algorithms act as gatekeepers, they match information efficiently and precisely and clearly lower the cost for people to get information, so personalization and user experience are improved; at the same time, however, recommendation methods that rely on user engagement tend to favor content that grabs attention, stirs emotions, or looks very similar, and this may weaken information diversity and public discussion. To deal with these problems, this study puts forward three ways to handle them: a balanced recommendation approach that combines "accuracy and diversity," a responsibility system that can be explained, checked, and audited, and shared supervision by editors and algorithms. The study also points out that algorithms and the public good are not necessarily opposites; the key is to use rule design to keep the desire for efficiency inside a framework, so platforms can balance efficiency and trust, and so the study can offer theoretical and practical references for improving the information environment.
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Michu Yu (2026) studied this question.
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