Algorithmic amplification of politics on Twitter.
Algorithmic amplification of politics on Twitter.
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DOI：
10.1073/pnas.2025334119
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发表时间：
2022-01-04
影响因子：
11.1
通讯作者：
Hardt M
中科院分区：
文献类型：
--
作者：
Huszár F;Ktena SI;O'Brien C;Belli L;Schlaikjer A;Hardt M
社交媒体在政治话语中的作用一直是学术界和公众激烈辩论的话题。来自各方的政治家和评论员声称，Twitter的算法放大了对手的声音，或者让他们沉默。因此，政策制定者和研究人员呼吁提高算法如何影响平台上政治内容曝光的透明度。基于一项涉及数百万Twitter用户的大规模实验，对七个国家的政党进行了细粒度分析，并在美国分享了620万篇新闻文章，这项研究对算法推荐系统及其对政治内容的影响进行了最全面的审计。结果显示，政治权利享有更高的放大比政治左。Twitter主页时间轴上的内容由个性化算法选择和排序。通过不断地将某些内容排名靠前，这些算法可能会放大一些信息，同时降低其他信息的可见性。公众和学术界一直在激烈地争论，一些政治团体可能比其他团体从算法放大中受益更多。我们从Twitter平台上的一个长期运行的、大规模的随机实验中提供了定量证据，该实验将一个随机对照组（包括近200万个每日活跃账户）提交给一个没有算法个性化的逆时间顺序内容提要。我们提出了两套调查结果。首先，我们研究了七个国家主要政党当选议员的推文。我们的研究结果揭示了一个非常一致的趋势：在所研究的七个国家中，有六个国家的主流政治右翼比主流政治左翼享有更高的算法放大率。与这一总体趋势一致，我们研究美国媒体格局的第二组发现显示，算法放大有利于右倾新闻来源。我们进一步研究了算法是否比温和派更能放大极左和极右的政治团体;与普遍的公众信念相反，我们没有找到支持这一假设的证据。我们希望我们的研究结果将有助于对个性化算法在塑造政治内容消费方面所起作用的循证辩论。
The role of social media in political discourse has been the topic of intense scholarly and public debate. Politicians and commentators from all sides allege that Twitter’s algorithms amplify their opponents’ voices, or silence theirs. Policy makers and researchers have thus called for increased transparency on how algorithms influence exposure to political content on the platform. Based on a massive-scale experiment involving millions of Twitter users, a fine-grained analysis of political parties in seven countries, and 6.2 million news articles shared in the United States, this study carries out the most comprehensive audit of an algorithmic recommender system and its effects on political content. Results unveil that the political right enjoys higher amplification compared to the political left. Content on Twitter’s home timeline is selected and ordered by personalization algorithms. By consistently ranking certain content higher, these algorithms may amplify some messages while reducing the visibility of others. There’s been intense public and scholarly debate about the possibility that some political groups benefit more from algorithmic amplification than others. We provide quantitative evidence from a long-running, massive-scale randomized experiment on the Twitter platform that committed a randomized control group including nearly 2 million daily active accounts to a reverse-chronological content feed free of algorithmic personalization. We present two sets of findings. First, we studied tweets by elected legislators from major political parties in seven countries. Our results reveal a remarkably consistent trend: In six out of seven countries studied, the mainstream political right enjoys higher algorithmic amplification than the mainstream political left. Consistent with this overall trend, our second set of findings studying the US media landscape revealed that algorithmic amplification favors right-leaning news sources. We further looked at whether algorithms amplify far-left and far-right political groups more than moderate ones; contrary to prevailing public belief, we did not find evidence to support this hypothesis. We hope our findings will contribute to an evidence-based debate on the role personalization algorithms play in shaping political content consumption.
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