Journalism, Media, and Artificial Intelligence: Let Us Define the Journey

A special issue of Journalism and Media (ISSN 2673-5172).

Deadline for manuscript submissions: 31 May 2024 | Viewed by 23240

Special Issue Editors


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Guest Editor
Department of Communication, Philosophy and Politics, University of Beira Interior, 6200-001 Covilhã, Portugal
Interests: links between journalism and new technologies; internet; portable devices; social networks; artificial intelligence; blockchain

Special Issue Information

Dear Colleagues,

We live in an information age and, ironically, meeting the core function of journalism, i.e., to provide people access to unbiased information, has never been more difficult. Herman and Chomsky conceptualized the “propaganda model” in their book "Manufacturing Consent: The Political Economy of the Mass Media" (Herman and Chomsky 1988), ​"A propaganda model focuses on this inequality of wealth and power and its multilevel effects on mass-media interests and choices. It traces the routes by which money and power are able to filter out the news fit to print, marginalize dissent, and allow the government and dominant private interests to get their messages across to the public". UN Secretary-General António Guterres expressed, “at a time when disinformation and mistrust of the news media are growing, a free press is essential for peace, justice, sustainable development, and human rights” (UN News 2019).

Journalism has failed to achieve this goal of providing people access to unbiased information for a variety of reasons including, difficulties in maintaining media organizations' freedom and impartiality, funding challenges, and technology-induced disruptions to journalism. The lack of unbiased information for the public led to mistrust in governments and phenomena such as populism, partisanship, and kleptocracy prevailed.

We believe the core issues in expectations from journalism are related to the perception of the public that it is the responsibility of others, not themselves, to provide impartial information and good governance. Moreover, the world and information are increasingly complex requiring new methods for journalism.

This Special Issue calls for artificial intelligence (AI) based approaches for next-generation journalism and media with a particular focus on ways to improve access to unbiased information for everyone. This involves the development of AI-based approaches for the whole of the journalism lifecycle, news gathering, production, and distribution. AI is already being used in journalism, both academic research and industry though its use in AI is incremental and relatively limited, see, e.g., (Canavilhas 2022; Beckett 2019). Another related work is on deep journalism (Mehmood 2022; Ahmad et al. 2022; Alswedani et al. 2022; Alqahtani et al. 2022; Alaql, AlQurashi, and Mehmood 2022) that can make impartial, cross-sectional, and multi-perspective information available to everyone, can bring rigour to journalism by making it easy to generate information using deep learning, and can make tools and information available so anyone can uncover information about matters of public importance. We seek research articles and review papers in all these areas. Manuscripts that bring together research in computer science and communication sciences are especially welcome.

References

Ahmad, Istiak, Fahad Alqurashi, Ehab Abozinadah, and Rashid Mehmood. 2022. “Deep Journalism and DeepJournal V1.0: A Data-Driven Deep Learning Approach to Discover Parameters for Transportation.” Sustainability (Switzerland) 14 (9): 5711. https://doi.org/10.3390/SU14095711.

Alaql, Abeer Abdullah, Fahad AlQurashi, and Rashid Mehmood. 2022. “Data-Driven Deep Journalism to Discover Age Dynamics in Multi-Generational Labour Markets from LinkedIn Media,” October. https://doi.org/10.20944/PREPRINTS202210.0472.V1.

Alqahtani, Eman, Nourah Janbi, Sanaa Sharaf, and Rashid Mehmood. 2022. “Smart Homes and Families to Enable Sustainable Societies: A Data-Driven Approach for Multi-Perspective Parameter Discovery Using BERT Modelling.” Sustainability 2022, Vol. 14, Page 13534 14 (20): 13534. https://doi.org/10.3390/SU142013534.

Alswedani, Sarah, Iyad Katib, Ehab Abozinadah, and Rashid Mehmood. 2022. “Discovering Urban Governance Parameters for Online Learning in Saudi Arabia During COVID-19 Using Topic Modeling of Twitter Data.” Frontiers in Sustainable Cities 4 (June): 1–24. https://doi.org/10.3389/FRSC.2022.751681.

Beckett, Charlie. 2019. “New Powers, New Responsibilities. A Global Survey of Journalism and Artificial Intelligence | | Polis.” London. https://blogs.lse.ac.uk/polis/2019/11/18/new-powers-new-responsibilities/.

Canavilhas, João. 2022. “Artificial Intelligence and Journalism: Current Situation and Expectations in the Portuguese Sports Media.” Journalism and Media 3 (3): 510–20. https://doi.org/10.3390/JOURNALMEDIA3030035.

Herman, ES, and N. Chomsky. 1988. Manufacturing Consent : The Political Economy of the Mass Media. New York: Pantheon Books. https://worldcat.org/title/17877574.

Mehmood, Rashid. 2022. “‘Deep Journalism’ Driven by AI Can Aid Better Government.” The Mandarin, 2022. https://www.themandarin.com.au/201467-deep-journalism-driven-by-ai-can-aid-better-government/.

UN News. 2019. “A Free Press Is ‘Cornerstone’ for Accountability and ‘Speaking Truth to Power’: Guterres.” 2019. https://news.un.org/en/story/2019/05/1037741.

Prof. Dr. Rashid Mehmood
Dr. João Canavilhas
Guest Editors

Manuscript Submission Information

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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. Journalism and Media is an international peer-reviewed open access quarterly 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 1000 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

  • AI, news and information gathering
  • AI and news production
  • AI and news distribution
  • personalized vs. informed and responsible news distribution
  • AI for journalism lifecycle enhancements
  • next-generation transformational approaches for journalism and media
  • AI, ethics, and editorial integrity in journalism and media
  • AI strategies for journalism and media organizations
  • AI strategies to provide unbiased information for everyone
  • AI-based approaches to address financial challenges in journalism
  • machine and deep learning approaches to journalism
  • AI, journalism and innovation
  • open source tools for journalism

Published Papers (4 papers)

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Research

17 pages, 1599 KiB  
Article
Artificial Intelligence (AI) in Brazilian Digital Journalism: Historical Context and Innovative Processes
by Moisés Costa Pinto and Suzana Oliveira Barbosa
Journal. Media 2024, 5(1), 325-341; https://doi.org/10.3390/journalmedia5010022 - 12 Mar 2024
Viewed by 1065
Abstract
This article investigates the historical uses and types of artificial intelligence (AI) systems and resources in Brazilian journalistic products. It is a work anchored in critically analyzing the literature on the subject, mapping and observing cases, seeking to identify uses and innovative processes, [...] Read more.
This article investigates the historical uses and types of artificial intelligence (AI) systems and resources in Brazilian journalistic products. It is a work anchored in critically analyzing the literature on the subject, mapping and observing cases, seeking to identify uses and innovative processes, and analyzing AI projects for journalism. A search was carried out in web repositories, specifically Google, Google Scholar, and Scopus, using the terms: “inteligência artificial” + “jornalismo”, “bot + jornalismo”, “Geração de linguagem natural [NLG] + jornalismo”, “aprendizado de máquina [machine learning] + jornalismo”, and “algoritmos + jornalismo”. The corpus analysis (N = 45) includes the evaluation of the impacts of AI on the production and distribution of news in the context of Brazilian digital journalism. We try to answer questions about the uses of databases, approximation with platforms, uses of shared codes, connections with other Ais, and sources of funding, and whether they are backend or frontend initiatives. In a parallel investigation, we try to identify if Brazilian newsrooms are officially using ChatGPT, a generative AI. The findings point to advances in using low-cost and low-impact AI, with the predominance of bots. The great availability of this kind of AI in web repositories is believed to facilitate native digital media to incorporate innovative processes in using these technologies. Full article
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20 pages, 619 KiB  
Article
Consumer Trust in AI–Human News Collaborative Continuum: Preferences and Influencing Factors by News Production Phases
by Steffen Heim and Sylvia Chan-Olmsted
Journal. Media 2023, 4(3), 946-965; https://doi.org/10.3390/journalmedia4030061 - 11 Sep 2023
Viewed by 3135
Abstract
AI has become increasingly relevant to the media sector, especially for news media companies considering the integration of this technology into their production processes. While the application of AI promises productivity gains, the impact on consumers’ perceptions of the resulting news and the [...] Read more.
AI has become increasingly relevant to the media sector, especially for news media companies considering the integration of this technology into their production processes. While the application of AI promises productivity gains, the impact on consumers’ perceptions of the resulting news and the level of AI integration accepted by the market has not been well studied. Our research focused on the analysis of news consumers’ preferred level of AI integration, AI news trust, and AI news usage intentions linked to the application of the technology in the discovery/information-gathering and writing/editing phases. By connecting a comprehensive set of factors influencing the perception of news and AI, we approached this gap through structural equation modeling, presenting an overview of consumers’ responses to AI integration into news production processes. Our research showed that while participants generally prefer lower levels of AI integration into both phases of production, news trust and usage intention can even increase as AI enters the production process—as long as humans remain in the lead. These findings provide researchers and news media managers with a first overview of consumers’ responses to news production augmentation and its implications for news perception in the market. Full article
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9 pages, 266 KiB  
Article
Artificial Intelligence in Automated Detection of Disinformation: A Thematic Analysis
by Fátima C. Carrilho Santos
Journal. Media 2023, 4(2), 679-687; https://doi.org/10.3390/journalmedia4020043 - 03 Jun 2023
Cited by 6 | Viewed by 14260
Abstract
The increasing prevalence of disinformation has led to a growing interest in leveraging artificial intelligence (AI) for detecting and combating this phenomenon. This article presents a thematic analysis of the potential benefits of automated disinformation detection from the perspective of information sciences. The [...] Read more.
The increasing prevalence of disinformation has led to a growing interest in leveraging artificial intelligence (AI) for detecting and combating this phenomenon. This article presents a thematic analysis of the potential benefits of automated disinformation detection from the perspective of information sciences. The analysis covers a range of approaches, including fact checking, linguistic analysis, sentiment analysis, and the utilization of human-in-the-loop systems. Furthermore, the article explores how the combination of blockchain and AI technologies can be used to automate the process of disinformation detection. Ultimately, the article aims to consider the integration of AI into journalism and emphasizes the importance of ongoing collaboration between these fields to effectively combat the spread of disinformation. The article also addresses ethical considerations related to the use of AI in journalism, including concerns about privacy, transparency, and accountability. Full article
26 pages, 3743 KiB  
Article
Data-Driven Deep Journalism to Discover Age Dynamics in Multi-Generational Labour Markets from LinkedIn Media
by Abeer Abdullah Alaql, Fahad AlQurashi and Rashid Mehmood
Journal. Media 2023, 4(1), 120-145; https://doi.org/10.3390/journalmedia4010010 - 22 Jan 2023
Cited by 4 | Viewed by 3522
Abstract
We live in the information age and, ironically, meeting the core function of journalism—i.e., to provide people with access to unbiased information—has never been more difficult. This paper explores deep journalism, our data-driven Artificial Intelligence (AI) based journalism approach to study how the [...] Read more.
We live in the information age and, ironically, meeting the core function of journalism—i.e., to provide people with access to unbiased information—has never been more difficult. This paper explores deep journalism, our data-driven Artificial Intelligence (AI) based journalism approach to study how the LinkedIn media could be useful for journalism. Specifically, we apply our deep journalism approach to LinkedIn to automatically extract and analyse big data to provide the public with information about labour markets; people’s skills and education; and businesses and industries from multi-generational perspectives. The Great Resignation and Quiet Quitting phenomena coupled with rapidly changing generational attitudes are bringing unprecedented and uncertain changes to labour markets and our economies and societies, and hence the need for journalistic investigations into these topics is highly significant. We combine big data and machine learning to create a whole machine learning pipeline and a software tool for journalism that allows discovering parameters for age dynamics in labour markets using LinkedIn data. We collect a total of 57,000 posts from LinkedIn and use it to discover 15 parameters by Latent Dirichlet Allocation algorithm (LDA) and group them into 5 macro-parameters, namely Generations-Specific Issues, Skills and Qualifications, Employment Sectors, Consumer Industries, and Employment Issues. The journalism approach used in this paper can automatically discover and make objective, cross-sectional, and multi-perspective information available to all. It can bring rigour to journalism by making it easy to generate information using machine learning, and can make tools and information available so that anyone can uncover information about matters of public importance. This work is novel since no earlier work has reported such an approach and tool and leveraged it to use LinkedIn media for journalism and to discover multigenerational perspectives (parameters) for age dynamics in labour markets. The approach could be extended with additional AI tools and other media. Full article
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