批判算法研究视角下微博“热搜”的把关标准考察

王茜

国际新闻界 ›› 2020, Vol. 42 ›› Issue (7) : 26-48.

PDF(11324 KB)
PDF(11324 KB)
国际新闻界 ›› 2020, Vol. 42 ›› Issue (7) : 26-48.
本期话题:算法研究

批判算法研究视角下微博“热搜”的把关标准考察

  • 王茜
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Investigating the Gatekeeping Criteria of Weibo “Hot Search”: From A Critical Algorithms Studies Perspective

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摘要

算法分发已成为网络新闻主要的分发模式,而微博“热搜”是该模式在社会化媒体 的典型应用。本文采用批判算法研究的视角考察了微博“热搜”的把关标准。基于对微 博年度报告、新闻稿、专利、官方发布以及2400余条“最热搜”上榜词的分析,本文提 出了微博“热搜”算法价值的四个要素:时新性、流行性、互动性和导向正确。这些要 素内嵌于代码的编写与设计之中,微博的价值观、假设和偏见被推向了“上游”阶段。 微博使用一系列策略来使“热搜”生成过程看起来浑然天成且不可避免,包括人气值的 实时更新、搜索框提示“大家正在搜”、以及“呈现”“反映”等修辞的使用,以此建 构算法把关的合法性话语。

Abstract

Algorithmic distribution has become the main distribution mode of online news. And Weibo “Hot Search” is a typical application of the algorithmic distribution mode in social media platforms. This article, from the perspective of Critical Algorithm Studies, seeks to investigate the gatekeeping criteria of Weibo “Hot Search”. Based on the analysis of Weibo’s annual reports, press releases, patents, offcial statements as well as 2400 pieces of “Hottest Searches”, this study identifes a set of four algorithmic values: instantaneousness, popularity, user engagement, and correct orientation, which are embedded in the code writing and design. Therefore, Weibo’s values, assumptions, and biases are moved “upstream”. In order to construct the discursive legitimization of algorithmic gatekeeping, Weibo uses a series of tactics to present the generation of “Hot Search” as natural and inevitable, including the realtime update of the search index, the indication of “people are searching” in the search box, and the rhetorics of “presentations” and “re?ections”.

关键词

把关 / 算法价值 / 社会化媒体 / 平台 / 人工智能

Key words

Gatekeeping / algorithmic values / social media / platform / Artifcial Intelligence

引用本文

导出引用
王茜. 批判算法研究视角下微博“热搜”的把关标准考察[J]. 国际新闻界. 2020, 42(7): 26-48
Investigating the Gatekeeping Criteria of Weibo “Hot Search”: From A Critical Algorithms Studies Perspective[J]. Chinese Journal of Journalism & Communication. 2020, 42(7): 26-48

基金

本文系教育部人文社会科学研究青年基金项目“智能算法驱动下的新闻分发变革研 究”(项目编号18YJC860031)成果的一部分。

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