论文标题
政治受众的多样性和新闻可靠性在算法排名中
Political audience diversity and news reliability in algorithmic ranking
论文作者
论文摘要
新闻源算法经常扩大错误信息和其他低质量的内容。社交媒体平台如何更有效地促进可靠的信息？现有的方法难以扩展，并且容易受到操纵。在本文中，我们建议将网站受众的政治多样性作为质量信号。使用来自域专家的新闻来源可靠性评级和来自6,890名美国公民样本的Web浏览数据，我们首先表明具有更极端和较少政治观众的网站具有较低的新闻标准。然后，我们将受众多样性纳入标准的协作过滤框架中，并表明我们的改进算法增加了建议用户建议的网站的可信度，尤其是那些最常消耗错误信息的网站，同时保持建议相关。这些发现表明，党派观众的多样性是更高新闻标准的宝贵信号，应将其纳入算法排名决策中。
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 U.S. citizens, 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.
