The Epistemology of Fine-Grained News Classification

Tracking #: 3768-4982

Authors: 
Enrico Motta
Enrico Daga
Aldo Gangemi
Maia Lunde Gjelsvik
Francesco Osborne
Angelo Salatino

Responsible editor: 
Cogan Shimizu

Submission type: 
Full Paper
Abstract: 
The process of news digitalization over the past decades has released massive amounts of news content, revolutionizing consumer access to news and disrupting traditional business models. These radical changes have also introduced new opportunities for media content analysis, potentially opening up new scenarios for ambitious large-scale media analyt-ics initiatives, which can go well beyond the relatively small-scale studies currently carried out by media scholars and practitioners. However, take-up of computational methods to support media content analysis activities has been rather modest, reflecting a degree of disconnect between the needs of scholars and practitioners for task-specific and usable software solutions and the state of the art in computational techniques for news media analysis. In this paper we per-form an initial step towards bridging this gap, by looking in detail at the task of fine-grained news classification. In particular, we propose a typology of news topics, which is formally specified and realised into a family of reusable on-tologies. The proposed model has been validated empirically, through an analysis of a multilingual news corpus, as well as formally, in terms of the functional and logical properties of the ontologies. Our analysis brings together the media and computer science literature, connecting the formal definitions provided in this paper to the concepts used by media scholars.
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Tags: 
Reviewed

Decision/Status: 
Accept

Solicited Reviews:
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Review #1
Anonymous submitted on 17/Oct/2024
Suggestion:
Accept
Review Comment:

The authors properly solved the doubts of my first review and updated the paper were needed.
I still suggest to include in the paper the details about the annotators as in their reply.
Apart from this minor comment, I recommend acceptance

Review #2
Anonymous submitted on 22/Oct/2024
Suggestion:
Accept
Review Comment:

I am conducting a follow-up review having reviewed the original paper. I see that the authors have updated the paper to address many of the review comments. In particular, I appreciate the addition of Figure 1 which provides a good overview of the many concepts (classes) in the news formalisation and how they relate to one another. I find the paper now meets the required standard for publication.