Simona Bisiani
Academic and research departments
Surrey Institute for People-Centred Artificial Intelligence (PAI), Faculty of Engineering and Physical Sciences.About
My research project
AI-assisted journalismMy research uses a variety of methods, ranging from qualitative (e.g., interviews) to quantitative (e.g., text mining) to dissect the state of local news in the UK. Particularly, I am interested in the spatial distribution of local news across the UK, and the intersection of local news inequality with other forms of inequality. I believe my research will support policymakers to better understand which communities are underserved when it comes to local news in the UK, and why it matters.
Supervisors
My research uses a variety of methods, ranging from qualitative (e.g., interviews) to quantitative (e.g., text mining) to dissect the state of local news in the UK. Particularly, I am interested in the spatial distribution of local news across the UK, and the intersection of local news inequality with other forms of inequality. I believe my research will support policymakers to better understand which communities are underserved when it comes to local news in the UK, and why it matters.
Publications
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.
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.