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2020
Arne Berven; Ole A. Christensen; Sindre Moldeklev; Andreas L. Opdahl; Kjetil A. Villanger
A knowledge-graph platform for newsrooms Journal Article
In: Computers in Industry, vol. 123, no. 103321, 2020, (Pre SFI).
Abstract | BibTeX | Tags: Computational journalism, Journalistic knowledge platforms, Knowledge graphs, Machine learning (ML), Natural-language processing (NLP), Newsroom systems, Ontology, OWL, RDF, Semantic technologies, WP3: Media Content Production and Analysis | Links:
@article{Berven2020,
title = {A knowledge-graph platform for newsrooms},
author = {Arne Berven and Ole A. Christensen and Sindre Moldeklev and Andreas L. Opdahl and Kjetil A. Villanger },
url = {https://reader.elsevier.com/reader/sd/pii/S0166361520305558?token=F8A21A513C97BFF598C2755575B3C89174B3D404E2EDDD23EC37966A2754ACA1700011EBBCF52ADE2845ADBC12D40041},
doi = {https://doi.org/10.1016/j.compind.2020.103321},
year = {2020},
date = {2020-12-01},
urldate = {2020-12-01},
journal = {Computers in Industry},
volume = {123},
number = {103321},
abstract = {Journalism is challenged by digitalisation and social media, resulting in lower subscription numbers and reduced advertising income. Information and communication techniques (ICT) offer new opportunities. Our research group is collaborating with a software developer of news production tools for the international market to explore how social, open, and other data sources can be leveraged for journalistic purposes. We have developed an architecture and prototype called News Hunter that uses knowledge graphs, natural-language processing (NLP), and machine learning (ML) together to support journalists. Our focus is on combining existing data sources and computation and storage techniques into a flexible architecture for news journalism. The paper presents News Hunter along with plans and possibilities for future work.},
note = {Pre SFI},
keywords = {Computational journalism, Journalistic knowledge platforms, Knowledge graphs, Machine learning (ML), Natural-language processing (NLP), Newsroom systems, Ontology, OWL, RDF, Semantic technologies, WP3: Media Content Production and Analysis},
pubstate = {published},
tppubtype = {article}
}
Enrico Motta; Enrico Daga; Andreas L. Opdahl; Bjørnar Tessem
Analysis and design of computational news angles Journal Article
In: IEEE Access, vol. 8, pp. 120613-120626, 2020, (Pre SFI).
Abstract | BibTeX | Tags: Computational journalism, Data schema, Knowledge representation, News angles, Ontology, Reasoning components., WP3: Media Content Production and Analysis | Links:
@article{Motta2020,
title = {Analysis and design of computational news angles},
author = {Enrico Motta and Enrico Daga and Andreas L. Opdahl and Bjørnar Tessem},
url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9127417},
doi = {10.1109/ACCESS.2020.3005513},
year = {2020},
date = {2020-06-29},
urldate = {2020-06-29},
journal = {IEEE Access},
volume = {8},
pages = {120613-120626},
abstract = {A key skill for a journalist is the ability to assess the newsworthiness of an event or situation. To this purpose journalists often rely on news angles, conceptual criteria that are used both i) to assess whether something is newsworthy and also ii) to shape the structure of the resulting news item. As journalism becomes increasingly computer-supported, and more and more sources of potentially newsworthy data become available in real time, it makes sense to try and equip journalistic software tools with operational versions of news angles, so that, when searching this vast data space, these tools can both identify effectively the events most relevant to the target audience, and also link them to appropriate news angles. In this paper we analyse the notion of news angle and, in particular, we i) introduce a formal framework and data schema for representing news angles and related concepts and ii) carry out a preliminary analysis and characterization of a number of commonly used news angles, both in terms of our formal model and also in terms of the computational reasoning capabilities that are needed to apply them effectively to real-world scenarios. This study provides a stepping stone towards our ultimate goal of realizing a solution capable of exploiting a library of news angles to identify potentially newsworthy events in a large journalistic data space.},
note = {Pre SFI},
keywords = {Computational journalism, Data schema, Knowledge representation, News angles, Ontology, Reasoning components., WP3: Media Content Production and Analysis},
pubstate = {published},
tppubtype = {article}
}