In this paper, we present UKTwitNewsCor, a comprehensive dataset for understanding the content production, dissemination, and audience engagement dynamics of online local media in the UK. It comprises over 2.5 million online news articles published between January 2020 and December 2022 from 360 local outlets. The corpus represents all articles shared on Twitter by the social media accounts of these outlets. We augment the dataset by incorporating social media performance metrics for the articles at the tweet level. We further augment the dataset by creating metadata about content duplication across domains. Alongside the article dataset, we supply three additional datasets: a directory of local media web domains, one of UK Local Authority Districts, and one of digital local media providers, providing statistics on the coverage scope of UKTwitNewsCor. Our contributions enable comprehensive, longitudinal analysis of UK local media, news trends, and content diversity across multiple platforms and geographic areas. In this paper, we describe the data collection methodology, assess the dataset geographic and media ownership diversity, and outline how researchers, policymakers, and industry stakeholders can leverage UKTwitNewsCor to advance the study of local media.

Abstract In the online information ecosystem, a content label is an attachment to a piece of content intended to contextualize that content for the viewer. Content labels are information about information, such as fact‐checks or sensitive content warnings. Research into content labeling is nascent, but growing; researchers have made strides toward understanding labeling best practices to deal with issues such as disinformation, and misleading content that may affect everything from voting to health. To make this review tractable, we focus on compiling the literature that can contextualize labeling effects and consequences. This review summarizes the central labeling literature, highlights gaps for future research, discusses considerations for social media, and explores definitions toward a taxonomy. Specifically, this article discusses the particulars of content labels, their presentation, and the effects of various labels. The current literature can guide the usage of labels on social media platforms and inform public debate over platform moderation.