Artificial Intelligence in the Media Industry: Applications, Innovations and Challenges

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information and Communications Technology".

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 8035

Special Issue Editors


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Guest Editor
NOVA University of Lisbon – School of Social Sciences and Humanities (NOVA FCSH), Lisbon, Portugal
Interests: digital media; digital journalism; media innovation

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Guest Editor
NOVA University of Lisbon – School of Social Sciences and Humanities (NOVA FCSH), Lisbon, Portugal
Interests: digital media; science communication; journalism

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Guest Editor
NOVA University of Lisbon – Faculty of Sciences and Technologies (FCT NOVA), Almada, Portugal
Interests: multimedia information processing; multimodal interaction; digital media; machine learning

Special Issue Information

Dear Colleagues,

In recent years, innovation in information technology (IT) has been a crosscutting issue in many areas. In this context, research has been addressing not only the impact of artificial intelligence (AI) in the IT sector itself but also the way AI, seen as a subfield of IT, is transforming other industries. However, there is still a lack of studies regarding how AI in its various branches – from machine learning to data mining – is impacting the media industry, both on the editorial (journalism) and on the creative (entertainment) side. How is AI being applied to counter fake news? How are newsrooms using it to engage, create interactivity, or generate news through big data? How is AI enhancing storytelling? What challenges does it bring to the media industry? These are a few examples of the potential of research that this Special Issue addresses.

The aim of this Special Issue is to bring together contributions that relate AI with the media industry in the context of applications, innovations, and challenges.

Potential topics of the Special Issue include but are not limited to the following:

  • applications of AI or related methods in media platforms (for example, written press, cinema, radio, television, and advertising);
  • applications of AI or related innovations in newsrooms routines and practices;
  • applications of AI or related innovations in enhanced storytelling and emergent digital formats;
  • applications of AI or related innovations in dealing with misinformation and fake news;
  • applications of AI and related methods in social media, including qualitative and quantitative analysis of information and interactions;
  • applications of AI or related innovations in engaging with the user;
  • applications of AI or related methods in media education;
  • ethical, economical, political, social, and other challenges related to the application of AI in the media industry;
  • evaluation of AI and related methods in media contexts (for example, performance and experience, value, credibility, and utility).

Research articles which describe original work, including methods, techniques, applications, tools, or survey papers, are welcome. We encourage the inclusion of comparisons with alternative methods. Authors may also wish to provide a short publicly accessible video.

Dr. Dora Santos-Silva
Dr. António Granado
Dr. Nuno Manuel Robalo Correia
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • media industry
  • machine learning
  • data mining
  • journalism
  • digital media
  • ethics of artificial intelligence

Published Papers (1 paper)

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Research

16 pages, 487 KiB  
Article
Towards the Detection of Fake News on Social Networks Contributing to the Improvement of Trust and Transparency in Recommendation Systems: Trends and Challenges
by Oumaima Stitini, Soulaimane Kaloun and Omar Bencharef
Information 2022, 13(3), 128; https://doi.org/10.3390/info13030128 - 3 Mar 2022
Cited by 13 | Viewed by 6451
Abstract
In the age of the digital revolution and the widespread usage of social networks, the modalities of information consumption and production were disrupted by the shift to instantaneous transmission. Sometimes the scoop and exclusivity are just for a few minutes. Information spreads like [...] Read more.
In the age of the digital revolution and the widespread usage of social networks, the modalities of information consumption and production were disrupted by the shift to instantaneous transmission. Sometimes the scoop and exclusivity are just for a few minutes. Information spreads like wildfire throughout the world, with little regard for context or critical thought, resulting in the proliferation of fake news. As a result, it is preferable to have a system that allows consumers to obtain balanced news information. Some researchers attempted to detect false and authentic news using tagged data and had some success. Online social groups propagate digital false news or fake news material in the form of shares, reshares, and repostings. This work aims to detect fake news forms dispatched on social networks to enhance the quality of trust and transparency in the social network recommendation system. It provides an overview of traditional techniques used to detect fake news and modern approaches used for multiclassification using unlabeled data. Many researchers are focusing on detecting fake news, but fewer works highlight this detection’s role in improving the quality of trust in social network recommendation systems. In this research paper, we take an improved approach to assisting users in deciding which information to read by alerting them about the degree of inaccuracy of the news items they are seeing and recommending the many types of fake news that the material represents. Full article
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