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

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

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 68618

Special Issue Editors

Prof. Dr. Rashid Mehmood

E-Mail Website
Guest Editor
High Performance Computing Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Interests: high-performance computing (HPC); big data; AI and IoT with applications in smart cities, healthcare, transportation, logistics, and toxicology
Special Issues, Collections and Topics in MDPI journals
Dr. João Canavilhas

E-Mail Website
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

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. 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

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (12 papers)

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Research

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22 pages, 680 KiB  
Article
Generative AI and Media Content Creation: Investigating the Factors Shaping User Acceptance in the Arab Gulf States
by Mahmoud Sayed Mohamed Ali, Khaled Zaki AbuElkhair Wasel and Amr Mohamed Mahmoud Abdelhamid
Journal. Media 2024, 5(4), 1624-1645; https://doi.org/10.3390/journalmedia5040101 - 6 Nov 2024
Viewed by 737
Abstract
This article aims to investigate the factors that affect behavioural intention (BI) and user behaviour (UB) among Arabian users of generative artificial intelligence (GenAI) applications in the context of media content creation. The study’s theoretical framework is grounded in the unified theory of [...] Read more.
This article aims to investigate the factors that affect behavioural intention (BI) and user behaviour (UB) among Arabian users of generative artificial intelligence (GenAI) applications in the context of media content creation. The study’s theoretical framework is grounded in the unified theory of acceptance and use of technology (UTAUT2). A sample of 496 users was analysed using the partial least squares structural equation modelling technique (PLS-SEM). The results revealed that BI is significantly influenced by performance expectancy, effort expectancy, social influence, hedonic motivation, habit, and user trust, with hedonic motivation having the greatest impact. In terms of UB, facilitation conditions, habit, user trust, and BI were all found to have a positive and significant impact. This study contributes to the existing theory on the utilisation of GenAI applications by organising findings pertaining to the use of AI technology for media content creation. Full article
(This article belongs to the Special Issue Journalism, Media, and Artificial Intelligence: Let Us Define the Journey)
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20 pages, 1189 KiB  
Article
Investigating the Role of Artificial Intelligence to Measure Consumer Efficiency: The Use of Strategic Communication and Personalized Media Content
by Saud Binlibdah
Journal. Media 2024, 5(3), 1142-1161; https://doi.org/10.3390/journalmedia5030073 - 21 Aug 2024
Cited by 1 | Viewed by 1808
Abstract
This study examines the relationships between strategic communication, personalized media content, AI, and consumer service efficiency in social marketing companies in Saudi Arabia. The study used a cluster sampling technique with a quantitative research design. The study targeted 498 responses via distributing the [...] Read more.
This study examines the relationships between strategic communication, personalized media content, AI, and consumer service efficiency in social marketing companies in Saudi Arabia. The study used a cluster sampling technique with a quantitative research design. The study targeted 498 responses via distributing the survey links on social media platforms. Using the SEM analysis in Smart PLS 4, this research tested the research hypotheses. The findings showed that strategic communication significantly improves personalized media content and consumer service efficiency, confirming its importance in business customer interactions and outcomes. Customized media content does not significantly improve consumer service efficiency, suggesting other mediating factors may be involved. AI mediates this relationship, bridging strategic inputs and service outcomes. AI boosts strategic communication and personalized content, improving consumer service efficiency. The results showed that AI fully mediates strategic communication and personalized media content into improved service efficiency, demonstrating its transformative potential in business communications and operations. The study shows that AI supports and improves digital marketing communication strategies. It is statistical evidence and confidence intervals that exclude zero, AI-enabled the application of personalized content and strategic directives to improve service efficiency in the mediation analysis. Full article
(This article belongs to the Special Issue Journalism, Media, and Artificial Intelligence: Let Us Define the Journey)
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13 pages, 724 KiB  
Article
The Effects of Assumed AI vs. Human Authorship on the Perception of a GPT-Generated Text
by Angelica Lermann Henestrosa and Joachim Kimmerle
Journal. Media 2024, 5(3), 1085-1097; https://doi.org/10.3390/journalmedia5030069 - 20 Aug 2024
Cited by 1 | Viewed by 1810
Abstract
Artificial Intelligence (AI) has demonstrated its ability to undertake writing tasks, including automated journalism. Prior studies suggest no differences between human and AI authors regarding perceived message credibility. However, research on people’s perceptions of AI authorship on complex topics is lacking. In a [...] Read more.
Artificial Intelligence (AI) has demonstrated its ability to undertake writing tasks, including automated journalism. Prior studies suggest no differences between human and AI authors regarding perceived message credibility. However, research on people’s perceptions of AI authorship on complex topics is lacking. In a between-groups experiment (N = 734), we examined the effect of labeled authorship on credibility perceptions of a GPT-written science journalism article. The results of an equivalence test showed that labeling a text as AI-written vs. human-written reduced perceived message credibility (d = 0.36). Moreover, AI authorship decreased perceived source credibility (d = 0.24), anthropomorphism (d = 0.67), and intelligence (d = 0.41). The findings are discussed against the backdrop of a growing availability of AI-generated content and a greater awareness of AI authorship. Full article
(This article belongs to the Special Issue Journalism, Media, and Artificial Intelligence: Let Us Define the Journey)
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15 pages, 322 KiB  
Article
Rethinking the Relation between Media and Their Audience: The Discursive Construction of the Risk of Artificial Intelligence in the Press of Belgium, France, Portugal, and Spain
by Cristian González-Arias and Xosé López-García
Journal. Media 2024, 5(3), 1023-1037; https://doi.org/10.3390/journalmedia5030065 - 23 Jul 2024
Viewed by 980
Abstract
It is believed that the way in which media speak about emerging technologies can influence the public perception of their benefits and risks. Risk statements highlight the possible negative effects, real or imaginary, that a particular event could have on audiences. Just as [...] Read more.
It is believed that the way in which media speak about emerging technologies can influence the public perception of their benefits and risks. Risk statements highlight the possible negative effects, real or imaginary, that a particular event could have on audiences. Just as journalism varies over space and time, what is considered a risk is deeply rooted in specific social, economic, and technological contexts. This variability implies that journalistic practices are neither universal nor static; instead, they change and adapt according to circumstance. Moreover, technological advances have allowed the press to better understand their audiences and adhere to their demands. In this context, the discursive construction of the risk of artificial intelligence was studied in the press of four European countries: Belgium, Spain, France, and Portugal. In total, 290 texts published in January 2024 were examined. Mentions of “artificial intelligence” were found in the following newspapers: Le Soir, El País, Le Figaro, and Público. Fourteen risk categories and seven groups of voices responsible for their enunciation were identified, with significant variations between the studied newspapers. It was concluded that national contexts make it possible to differentiate the way in which the press communicates the risks associated with artificial intelligence. Although these results do not directly reflect public awareness of the risks in each of these countries, they open a line of research on the possible influences of the progressive monitoring and knowledge of audiences in the construction of the media agenda. Full article
(This article belongs to the Special Issue Journalism, Media, and Artificial Intelligence: Let Us Define the Journey)
19 pages, 2015 KiB  
Article
Artificial Intelligence in Journalism: A Ten-Year Retrospective of Scientific Articles (2014–2023)
by Fabia Ioscote, Adriana Gonçalves and Claudia Quadros
Journal. Media 2024, 5(3), 873-891; https://doi.org/10.3390/journalmedia5030056 - 29 Jun 2024
Cited by 2 | Viewed by 11281
Abstract
Academic interest in AI in journalism has been growing since 2018. Through a systematic review of the literature from 2014 to 2023, this study discusses the evolution of research in the field and how AI has changed journalism. The aim is to understand [...] Read more.
Academic interest in AI in journalism has been growing since 2018. Through a systematic review of the literature from 2014 to 2023, this study discusses the evolution of research in the field and how AI has changed journalism. The aim is to understand the impact of AI on journalism, based on a review of academic papers and a qualitative analysis of the most cited articles. This study combines: a systematic review of scientific articles extracted from Web of Science and Scopus (n = 699) and a qualitative approach with categorical content analysis of those with more than 50 citations (n = 59). The results (n = 699) highlight the prominence of authors from the Universities of Amsterdam and Santiago de Compostela. The United States has the largest number of authorships: 261 distributed across 99 institutions. The categorical content analysis (n = 59) shows a focus on issues like the work of the journalist, because AI is replacing journalists with repetitive and monotonous tasks, raising several questions about the role of the journalist. The findings show the rise of computational methods, highlighting the pervasiveness of AI in research, which has not been explored in previous work. Ethics, regulation, and journalism education remain under-discussed in research. Full article
(This article belongs to the Special Issue Journalism, Media, and Artificial Intelligence: Let Us Define the Journey)
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12 pages, 1981 KiB  
Article
Bibliometric and Content Analysis of the Scientific Work on Artificial Intelligence in Journalism
by Alem Febri Sonni, Vinanda Cinta Cendekia Putri and Irwanto Irwanto
Journal. Media 2024, 5(2), 787-798; https://doi.org/10.3390/journalmedia5020051 - 17 Jun 2024
Cited by 2 | Viewed by 2493
Abstract
This paper presents a comprehensive bibliometric review of the development of artificial intelligence (AI) in journalism based on the analysis of 331 articles indexed in the Scopus database between 2019 and 2023. This research combines bibliometric approaches and quantitative content analysis to provide [...] Read more.
This paper presents a comprehensive bibliometric review of the development of artificial intelligence (AI) in journalism based on the analysis of 331 articles indexed in the Scopus database between 2019 and 2023. This research combines bibliometric approaches and quantitative content analysis to provide an in-depth conceptual and structural overview of the field. In addition to descriptive measures, co-citation and co-word analyses are also presented to reveal patterns and trends in AI- and journalism-related research. The results show a significant increase in the number of articles published each year, with the largest contributions coming from the United States, Spain, and the United Kingdom, serving as the most productive countries. Terms such as “fake news”, “algorithms”, and “automated journalism” frequently appear in the reviewed articles, reflecting the main topics of concern in this field. Furthermore, ethical aspects of journalism were highlighted in every discussion, indicating a new paradigm that needs to be considered for the future development of journalism studies and professionalism. Full article
(This article belongs to the Special Issue Journalism, Media, and Artificial Intelligence: Let Us Define the Journey)
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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
Cited by 3 | Viewed by 3253
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
(This article belongs to the Special Issue Journalism, Media, and Artificial Intelligence: Let Us Define the Journey)
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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
Cited by 3 | Viewed by 5579
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
(This article belongs to the Special Issue Journalism, Media, and Artificial Intelligence: Let Us Define the Journey)
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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 - 3 Jun 2023
Cited by 12 | Viewed by 24686
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
(This article belongs to the Special Issue Journalism, Media, and Artificial Intelligence: Let Us Define the Journey)
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 6 | Viewed by 4796
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
(This article belongs to the Special Issue Journalism, Media, and Artificial Intelligence: Let Us Define the Journey)
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Review

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13 pages, 740 KiB  
Review
How Generative AI Is Transforming Journalism: Development, Application and Ethics
by Yi Shi and Lin Sun
Journal. Media 2024, 5(2), 582-594; https://doi.org/10.3390/journalmedia5020039 - 10 May 2024
Cited by 4 | Viewed by 6964
Abstract
Generative artificial intelligence (GAI) is a technology based on algorithms, models, etc., that creates content such as text, audio, images, videos, and code. GAI is deeply integrated into journalism as tools, platforms and systems. However, GAI’s role in journalism dilutes the power of [...] Read more.
Generative artificial intelligence (GAI) is a technology based on algorithms, models, etc., that creates content such as text, audio, images, videos, and code. GAI is deeply integrated into journalism as tools, platforms and systems. However, GAI’s role in journalism dilutes the power of media professionals, changes traditional news production and poses ethical questions. This study attempts to systematically answer these ethical questions in specific journalistic practices from the perspectives of journalistic professionalism and epistemology. Building on the review of GAI’s development and application, this study identifies the responsibilities of news organizations, journalists and audiences, ensuring that they realize the potential of GAI while adhering to journalism professionalism and universal human values to avoid negative technological effects. Full article
(This article belongs to the Special Issue Journalism, Media, and Artificial Intelligence: Let Us Define the Journey)
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Other

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20 pages, 1922 KiB  
Systematic Review
Recommender Systems and Over-the-Top Services: A Systematic Review Study (2010–2022)
by Paulo Nuno Vicente and Catarina Duff Burnay
Journal. Media 2024, 5(3), 1259-1278; https://doi.org/10.3390/journalmedia5030080 - 2 Sep 2024
Viewed by 1188
Abstract
Artificial intelligence (AI) technologies have been increasingly developed and applied in the audiovisual sector. Over-the-top (OTT) services, directly distributed to viewers via the Internet, are associated with a shift towards automation through algorithmic mediation in audiovisual content led by digital platforms. However, scientific [...] Read more.
Artificial intelligence (AI) technologies have been increasingly developed and applied in the audiovisual sector. Over-the-top (OTT) services, directly distributed to viewers via the Internet, are associated with a shift towards automation through algorithmic mediation in audiovisual content led by digital platforms. However, scientific knowledge regarding algorithmic recommender systems and automation in OTT services is not yet systemized; researchers, practitioners, and the public thus lack full awareness about the still largely opaque phenomena. To address this gap, we conduct a systematic literature review in the communication domain (2010–2022) and answer four key research questions: What research objectives have been pursued? What concepts have been developed and/or applied? What methodologies have been privileged? Which OTT platforms have received the most research attention? Challenges and opportunities are highlighted, and an agenda for future research is advanced. Full article
(This article belongs to the Special Issue Journalism, Media, and Artificial Intelligence: Let Us Define the Journey)
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