Deep Learning to Encourage Citizen Involvement in Local Journalism

Tessem, Bjørnar; Nyre, Lars; Mesquita, Michel and Mulholland, Paul (2022). Deep Learning to Encourage Citizen Involvement in Local Journalism. In: Manninen, V.J.E.; Niemi, M.K. and Ridge-Newman, A. eds. Futures of Journalism. Palgrave Macmillan, Cham, pp. 211–226.

DOI: https://doi.org/10.1007/978-3-030-95073-6_14

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 and information about the location and 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, news tips from readers are often of low technical quality; containing little information and poor photos. 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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