Algorithmic timeline curation is now an integral part of Twitter's platform, affecting information exposure for more than 150 million daily active users. Despite its large-scale and high-stakes impact, especially during a public health emergency such as the COVID-19 pandemic, the exact effects of Twitter's curation algorithm generally remain unknown. In this work, we present a sock-puppet audit that aims to characterize the effects of algorithmic curation on source diversity and content diversity in Twitter timelines. We created eight sock puppet accounts to emulate representative real-world users, selected through a large-scale network analysis. Then, for one month during early 2020, we collected the puppets' timelines twice per day. Broadly, our results show that algorithmic curation increases source diversity in terms of both Twitter accounts and external domains, even though it drastically decreases the number of external links in the timeline. In terms of content diversity, algorithmic curation had a mixed effect, slightly amplifying a cluster of politically-focused tweets while squelching a cluster of tweets focused on COVID-19 fatalities. Finally, we present some evidence that the timeline algorithm may exacerbate partisan differences in exposure to different sources and content. The paper concludes by discussing broader implications in the context of algorithmic gatekeeping.
https://doi.org/10.1145/3449152
The 24th ACM Conference on Computer-Supported Cooperative Work and Social Computing