论文标题
了解人们如何使用网络流量数据消耗低质量和极端新闻
Understanding how people consume low quality and extreme news using web traffic data
论文作者
论文摘要
为了减轻虚假新闻的传播,研究人员需要了解谁访问了伪造的新网站,将人们带到这些网站,访客来自哪里以及他们喜欢消费的内容。在本文中,我们分析了网关专家(TGP)的网络流量数据,这是一个受欢迎的极右翼网站,该网站以反复共享已使其网络流量可用于通用大众的虚假信息而闻名。我们在一个月内收集了6800万个网站访问该网站的数据,并分析人们如何通过多个功能消费新闻。我们的流量分析表明,搜索引擎和社交媒体平台是流量的主要驱动力。我们的地理位置分析表明,TGP在2020年投票赞成特朗普的县更受欢迎。我们的主题分析表明,阴谋文章比事实文章获得更多的访问。 由于无法观察直接网站流量,现有研究使用替代数据源,例如社交媒体帖子的参与信号。为了验证社交媒体参与信号是否与实际的网络访问计数相关,我们在同一时期收集了来自TGP的所有Facebook和Twitter帖子。我们表明,所有参与信号都与网络访问数量呈正相关,但相关优势不同。基于Facebook帖子的指标比基于Twitter的指标更好。我们独特的网络流量数据集和见解可以帮助研究人员更好地衡量极右翼和虚假新闻网址对社交媒体平台的影响。
To mitigate the spread of fake news, researchers need to understand who visit fake new sites, what brings people to those sites, where visitors come from, and what content they prefer to consume. In this paper, we analyze web traffic data from The Gateway Pundit (TGP), a popular far-right website that is known for repeatedly sharing false information that has made its web traffic available to the general public. We collect data on 68 million web traffic visits to the site over a month period and analyze how people consume news via multiple features. Our traffic analysis shows that search engines and social media platforms are main drivers of traffic; our geo-location analysis reveals that TGP is more popular in counties that voted for Trump in 2020; and our topic analysis shows that conspiratorial articles receive more visits than factual articles. Due to the inability to observe direct website traffic, existing research uses alternative data source such as engagement signals from social media posts. To validate if social media engagement signals correlate with actual web visit counts, we collect all Facebook and Twitter posts with URLs from TGP during the same time period. We show that all engagement signals positively correlate with web visit counts, but with varying correlation strengths. Metrics based on Facebook posts correlate better than metrics based on Twitter. Our unique web traffic data set and insights can help researchers to better measure the impact of far-right and fake news URLs on social media platforms.