Practicing Local Data Journalism: Opportunities and Obstacles

Introduction

Since the introduction of information and communication technologies in journalism at the end of the previous century, substantial changes occurred in the profession that have resulted in its significant transformation. This is mainly fueled by the digitalization of the journalistic workflow and the introduction of additional dissemination channels for the news (Siapera & Veglis, 2012). Journalism has traditionally been seen as closely related to technology. It is worth noting radio and television that their introduction created new opportunities for the media organization that were based solely on printed editions to distribute the news content. But of course, news gathering, creation, distribution, and consumption were completely transformed with the introduction of the internet and its services (Spyridou, et al., 2013). This also resulted in the emergence of many new journalistic specialties (multimedia journalism, drone journalism, algorithmic journalism, etc.) (Diakopoulos, 2019; Ntalakas, et al., 2017; Tu, 2015) among which data journalism (Bounegru, & Gray, 2021; Gray, et al., 2012; Veglis & Bratsas, 2017), which require journalists to possess specific skills and use specialized information processing tools. At the same time, citizen journalism has increased competition, and traditional journalism organizations are under constant pressure to deliver more news faster (Allan and Thorsen, 2009). This means that journalists have to be very competitive in a very demanding market.

Data journalism, which emerged and began to develop in the early 21st century, is now accepted as a recognized journalism specialty (Veglis & Bratsas, 2021). Many factors have contributed to the accession of data journalism, among which are the availability of data in digital format and also the abundance of data visualization and data management applications (Veglis & Bratsas, 2017). It has received a lot of attention recently in academic literature and the area of cutting-edge innovations in digital news production (Hermida & Young, 2017; Loosen et al., 2020; Weber et al., 2018). According to Gray et al. (2012) and Bounegru & Gray (2021), it is regarded as a journalistic discipline that illuminates the crucial role that numerical data plays in the creation and dissemination of news in the digital age.

Another type of journalism that is significantly related to data journalism is local journalism whose readership has declined in recent years (Wadbring & Bergström, 2017). Local journalism can be defined as news coverage in a local context (focusing on local issues and events) that interests people that live in a particular geographical region (Franklin, 2006). Local journalism topics typically include regional weather, politics, business, and human-interest stories (Meyer & Tang, 2015).

The connection between local journalism and data is not a recent phenomenon (Stalph, Hahn & Liewehr, 2022); during the previous 15 years, data journalism practices have been adopted by local media (Ali, 2014) supported by the advancement of a vigorous ecosystem of hyper-local data journalism (Stalph & Borges-Rey, 2018). Before the digitization and institutionalization of data journalism, local journalism organizations were fertile ground for data-driven practices (Coddington 2015; Meyers, 2002). Similarly, Boyles (2020) supports the advantages offered by the utilization of data storytelling in framing significant local issues for the audience. The problem is that local media face significant barriers to developing local data journalism that could have significant local but also hyper-local potential (Borges-Rey, 2020).