Algorithmic Media Literacy / 算法媒介素养
Algorithmic media literacy is the practical ability to notice how platforms shape what one sees, feels, reacts to, and feeds back into the system. 164.算法的“兔子洞”：为什么你总在看完新闻后滑向娱乐？｜对谈黄圣淳教授 frames this as the ordinary user’s response to recommendation systems: do not reduce algorithms to good or bad, but recognize when a feed is training attention, emotion, and behavior.
The concept turns media critique into practice. If a user clicks, watches, comments, likes, searches, or keeps scrolling, they are giving signals to Algorithmic Prediction Loop / 算法预判循环 and Platform Feedback Loop / 平台反馈循环. Literacy therefore includes deciding when not to react, when to cross-check sources, and when to curate inputs before the feed becomes the default picture of reality.
167.柏拉图、卢梭、哈耶克、阿伦特四大哲学家会如何解释算法时代？｜串台独树不成林 adds a philosophical version of the same practice: the user may not leave the algorithmic cave, but can recognize that the shadows are projected, ask how selection works, and compare filters before treating a feed as the world itself.
Key Claims
- Algorithmic literacy begins with awareness of one’s own feedback behavior.
- Not commenting on rage bait can be an active intervention rather than passivity.
- Users should compare platforms because different [[PlatformAffordance|affordances]] and ranking incentives reveal different blind spots.
- Literacy includes civic judgment: ask whether public information is being displaced by Algorithmic Entertainment Redirect / 算法娱乐重定向 or distorted by Algorithmic Amplification / 算法放大.
- Episode 167 adds that literacy includes knowing that one is inside a cave-like media structure and resisting the temptation to mistake ranking for reality or judgment.
Connections
- Feed Curation and Autonomy Under Information Flow / 信息流中的自主性 — adjacent user-side agency practices.
- Attention Industrialization and Addictive Interaction Design — attention environments literacy responds to.
- News Finds Me / 新闻找到我, Filter Bubble / 过滤气泡, and Incidental Exposure / 偶然暴露 — source evaluation and exposure context.
- Platform Feedback Loop / 平台反馈循环, Platform Affordance / 平台可供性, and Algorithmic Prediction Loop / 算法预判循环 — mechanisms users need to understand.
- Algorithmic Cave Allegory / 算法洞穴隐喻, Algorithmic Reason Outsourcing / 算法理性外包, and Algorithmic Diversity Dividend / 算法多样性红利 — episode 167’s cave, judgment, and filter-comparison branch.