Copy the page URI to the clipboard
Tessem, Bjørnar; Nyre, Lars; Mesquita, Michel and Mulholland, Paul
(2022).
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.
Viewing alternatives
Metrics
Public Attention
Altmetrics from AltmetricNumber of Citations
Citations from Dimensions- Request a copy from the author This document will be available to download from 4 May 2024
Item Actions
Export
About
- Item ORO ID
- 83754
- Item Type
- Book Section
- ISBN
- 3-030-95072-7, 978-3-030-95072-9
- Keywords
- Artificial intelligence; automated news; local journalism; news literacy; social cohesion; empathy; public sphere; deep learning
- Academic Unit or School
-
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM) - Copyright Holders
- © 2022 The Editors
- Depositing User
- Paul Mulholland