Simona Bisiani

This is replication data for "Mapping News Geography: A Computational Framework for Classifying Local Media Through Geographic Coverage Patterns". Instructions for usage and code can be found at the following GitHub repository: https://github.com/simonabisiani/geographic-local-media-classifier

In the last decade, data journalism has established itself as a thriving field. Recently, COVID-19 has boosted the demand for data-driven reporting to make sense of the pandemic, increasing the importance of studying the evolution of this rapidly evolving and technology-bounded practice. However, the number of efforts to map and systematically measure the data journalism industry are few. This paper analyses the findings of The State of the Data Journalism Survey 2021, currently the most extensive study on the characteristics surrounding the workforce producing and contributing to the data journalism industry. The outcome is an understanding of an expanding workforce with a geographically uneven distribution, which is still homogeneous in terms of tools and educational paths. Self-taught, resourceful, and multi-skilled, data journalists often work in isolation but share pressures of limited resources, time limitations, and access to quality data. The pandemic appears to have directly increased those struggles, although data journalists agree that the field's reputation has ultimately benefited from it.

This dataset contains several spreadsheets within which four public datasets of print and digital local news outlets in the UK (JICREG, ABC, PINF, and MRC) are triangulated and combined. In addition, the observations from these four datasets have been manually verified to flag obsolete observations. This helped generate a novel, powerful list of print and digital local news outlets (this can be found in sheet "Stage 2 - clean df with enhancements" and includes any observations marked as 1 under the "Baseline" column). The script where manipulation of these datasets occur can be found here: https://github.com/simonabisiani/Local-News-Datasets-Triangulation. The dataset was used to carry out research which resulted in the following journal article: https://www.mdpi.com/2673-5172/4/4/77.

Local journalism is fundamental for a thriving democracy, yet the UK faces a decline in the number of print and digital local news outlets. Large-scale mappings of the surviving outlets offer invaluable insights to policymakers designing interventions to strengthen the sector. Due to the lack of a comprehensive national directory of UK print and digital local news outlets, researchers have resorted to datasets such as circulation auditors’ databases, which have been noted to be incomplete and outdated. A lack of understanding of the magnitude of these data limitations hinders researchers from selecting optimal datasets. This study evaluates four commonly used local news databases, uncovering significant variations in their currentness and comprehensiveness. Thereafter, statistical analyses demonstrate the significant effect of each dataset’s shortcomings on findings in local news research. To address this issue, triangulation and manual verification are employed to create a more comprehensive and robust dataset. This procedure generates a new national dataset of print and digital local news outlets that can be used in future research, alongside a framework for leveraging public data to build an independent research dataset. This work paves the way for more rigorous research in data-driven local news provision studies. Concluding remarks stress the importance of setting definitions and establishing clear data pipelines in an increasingly diversified and dynamic sector.