Fair and Balanced? Quantifying Media Bias through Crowdsourced Content Analysis
Fair and Balanced? Quantifying Media Bias through Crowdsourced Content Analysis
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DOI：
10.1093/poq/nfw007
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发表时间：
2016-01-01
影响因子：
3.4
通讯作者：
Rao, Justin M.
中科院分区：
文献类型：
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作者：
Budak, Ceren;Goel, Sharad;Rao, Justin M.
人们普遍认为，新闻机构表现出意识形态偏见，但严格量化这种倾向已被证明在方法上具有挑战性。通过机器学习和众包技术的结合，我们调查了美国15个主要新闻媒体对政治问题的选择和框架。从12个月内发表的803，146篇新闻报道开始，我们首先使用监督学习算法来识别与政治事件有关的14%的文章。然后，我们招募了749名在线人类法官，根据主题和意识形态立场对这些政治文章中的10，502个随机子集进行分类。我们的分析产生了一个与先前工作一致的出口的意识形态排序。然而，新闻媒体比通常认为的更相似。具体来说，除了政治丑闻之外，主要新闻机构在很大程度上以无党派的方式呈现话题，既不以特别有利或不利的方式来描述民主党人也不以特别不利的方式来描述共和党人。此外，除了政治丑闻之外，几乎没有证据表明故事选择存在系统性差异，所有主要新闻媒体都报道各种各样的话题，其频率与媒体的意识形态立场基本无关。最后，新闻机构表达其意识形态偏见的方式不是直接支持某个政党，而是不成比例地批评一方，这一惯例进一步缓和了总体分歧。
It is widely thought that news organizations exhibit ideological bias, but rigorously quantifying such slant has proven methodologically challenging. Through a combination of machine-learning and crowdsourcing techniques, we investigate the selection and framing of political issues in fifteen major US news outlets. Starting with 803,146 news stories published over twelve months, we first used supervised learning algorithms to identify the 14 percent of articles pertaining to political events. We then recruited 749 online human judges to classify a random subset of 10,502 of these political articles according to topic and ideological position. Our analysis yields an ideological ordering of outlets consistent with prior work. However, news outlets are considerably more similar than generally believed. Specifically, with the exception of political scandals, major news organizations present topics in a largely nonpartisan manner, casting neither Democrats nor Republicans in a particularly favorable or unfavorable light. Moreover, again with the exception of political scandals, little evidence exists of systematic differences in story selection, with all major news outlets covering a wide variety of topics with frequency largely unrelated to the outlet's ideological position. Finally, news organizations express their ideological bias not by directly advocating for a preferred political party, but rather by disproportionately criticizing one side, a convention that further moderates overall differences.