Abstract
We discuss the potential of a mobile app for news tips to local newspapers to be augmented with artificial intelligence. It can be designed to encourage deliberative, consensus-oriented contributions from citizens. We presume that such an app will generate news stories from multi-modal data in the form of photos, videos, text elements, location information, and the identity of the contributor. Three scenarios are presented to show how image recognition, natural language processing, narrative construction, and other AI technologies can be applied. The scenarios address three interrelated challenges for local journalism. First, text and photos in tips are often of low quality for journalism purposes. Second, peer-to-peer dialogue about local news takes place in social media instead of in the newspaper. Third, readers lack news literacy and are prone to confrontational debates and trolling. We show how advances in deep learning technology makes it possible to propose solutions to these problems.
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Change history
07 June 2022
The Author has provided belated corrections to the affiliation of one of the co-authors of this chapter. The corrections to the affiliation have been carried out as follows:
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Funding
This work was supported by the Norwegian Media Authority and the regional Norwegian innovation fund UH-nett Vest. It was also supported by industry partners and the Research Council of Norway through MediaFutures: Research Centre for Responsible Media Technology and Innovation, project number 309339.
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Tessem, B., Nyre, L., d. S. Mesquita, M., Mulholland, P. (2022). Deep Learning to Encourage Citizen Involvement in Local Journalism. In: Manninen, V.J.E., Niemi, M.K., Ridge-Newman, A. (eds) Futures of Journalism. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-95073-6_14
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