@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}

}