The influence of big data on monitoring the factual quality of digital media in Southern Africa.

Abstract

This research study will explore how big data can drive innovation in response to dynamic change and aid society in establishing an advantage when fact-checking/monitoring new media and dealing with false information. The study emphasises that big data might answer questions and offer insights society never had access to before. In the current news media environment, the services that enable the sharing and production of large amounts of data are not sufficient to combat increasing fake news, ongoing public mistrust, and false, partisan media content for capital gains from gaining more influence in society. There is an urgent need for intervention, which big data innovation can provide. There are, however, some myths regarding the use of big data that need to be dispelled, such as the idea that an analysis of the data will ensure transparency and reliable content distribution from the developers of big data systems to the audience consuming the data. Innovating and obtaining an advantage from data is more complex than just collecting lots of data; a look at the impact big data will have on a society is vital in leveraging big data. The study explores this notion by looking at the Digital Data Genesis Capability Model. The model guides the structure and how the case study will be conducted in the media fact-checking sector. The development of the big data initiative is built on fundamental expertise. According to the findings, highly skilled employees with knowledge of both proprietary and open-source tools are essential in the development of big data systems. Furthermore, there is a high level of compatibility with the existing web environment standard and the tools being used when deploying a big data system in the web. As a result, development of a big data initiative by a technology focused organisation is only limited by their ability to implement an effective big data workflow. However, this requires detailed planning, cloud computing for hardware; software; outsourced third party services; the work on data structure built in-house; and the use of docker containers that enable mobility in the development process and the adoption of new technology when implementing the searching and querying of large datasets and streams. There was a deviation from the existing model noted. The context of the study exposed that it is possible to implement big data initiatives among more than one company as a partnership, if the companies share some business traits or the same philosophy: thus, changing the dynamic of routines and responsibility in the existing landscape.