Political audience diversity and news reliability in algorithmic ranking
Political audience diversity and news reliability in algorithmic ranking
复制标题
DOI：
10.1038/s41562-021-01276-5
复制
发表时间：
2022-02-03
影响因子：
29.9
通讯作者：
Nyhan, Brendan
中科院分区：
文献类型：
--
作者：
Bhadani, Saumya;Yamaya, Shun;Nyhan, Brendan
Bhadani等人利用调查和互联网浏览数据以及专家评级发现，将党派受众的多样性纳入新闻网站的算法排名，可以提高他们推荐的网站的可信度，并保持相关性。新闻推送算法经常放大错误信息和其他低质量内容。社交媒体平台如何更有效地推广可靠信息？现有的方法难以扩展，容易被操纵。在本文中，我们建议使用网站的观众的政治多样性作为质量信号。使用来自领域专家的新闻来源可靠性评级和来自6，890名美国居民的不同样本的网络浏览数据，我们首先表明，具有更极端和更少政治多元化受众的网站具有较低的新闻标准。然后，我们将观众的多样性纳入一个标准的协同过滤框架，并表明，我们改进的算法增加了可信度的网站建议给用户，特别是那些谁最经常消费的错误信息，同时保持相关的建议。这些发现表明，党派观众的多样性是一个有价值的信号，更高的新闻标准，应纳入算法排名的决定。
Using survey and internet browsing data and expert ratings, Bhadani et al. find that incorporating partisan audience diversity into algorithmic rankings of news websites increases the trustworthiness of the sites they recommend and maintains relevance.Newsfeed algorithms frequently amplify misinformation and other low-quality content. How can social media platforms more effectively promote reliable information? Existing approaches are difficult to scale and vulnerable to manipulation. In this paper, we propose using the political diversity of a website's audience as a quality signal. Using news source reliability ratings from domain experts and web browsing data from a diverse sample of 6,890 US residents, we first show that websites with more extreme and less politically diverse audiences have lower journalistic standards. We then incorporate audience diversity into a standard collaborative filtering framework and show that our improved algorithm increases the trustworthiness of websites suggested to users-especially those who most frequently consume misinformation-while keeping recommendations relevant. These findings suggest that partisan audience diversity is a valuable signal of higher journalistic standards that should be incorporated into algorithmic ranking decisions.