Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 11

The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.

A Review of Artificial Intelligence Research in Peer-Reviewed Communication Journals

Ertem-Eray, Tugce, Cheng, Yang·2025·Applied Sciences
DOI 10.3390/app15031058
Open this paper in CoChat — free account

Abstract

This study analyzes artificial intelligence (AI) research in communication scholarship through a content analysis of published articles between 2006 and 2022. It aims to understand the status of AI research between 2006 and 2022 and identify directions for future inquiry. Findings indicate that the number of articles about AI has increased over the years and scholars should continue applying existing theoretical frameworks or proposing new ones to investigate diverse topics across cultural and sociopolitical contexts.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/app15031058

Cites

13 of 13 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 13 references

Research works printed but not linked: 81

  1. Akhtar, 2022, Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions, Ann. Oper. Res., 2, 633
  2. Moran, 2022, Robots in the news and newsrooms: Unpacking meta-journalistic discourse on the use of artificial intelligence in journalism, Digit. J., 10, 1756
  3. Tejedor, 2021, Exo journalism: A conceptual approach to a hybrid formula between journalism and artificial intelligence, J. Media, 4, 830
  4. Campbell, 2021, Preparing for an era of deepfakes and ai-generated ads: A framework for understanding responses to manipulated advertising, J. Advert., 1, 22
  5. Brewer, 2022, Media use, interpersonal communication, and attitudes toward artificial intelligence, Sci. Commun., 44, 559, 10.1177/10755470221130307 · DOI
  6. Tomasello, 2001, The status of Internet-based research in five leading communication journals, 1994–1999, J. Mass Commun. Q., 78, 659
  7. Coombs, 2021, What is it about humanity that we can’t give away to intelligent machines? A European perspective, Int. J. Inf. Manag., 58, 102311, 10.1016/j.ijinfomgt.2021.102311 · DOI
  8. Hancock, 2020, AI-mediated communication: Definition, research agenda, and ethical considerations, J. Comput.-Mediat. Commun., 25, 89, 10.1093/jcmc/zmz022 · DOI · Paper page
  9. Hepp, 2020, Artificial companions, social bots and work bots: Communicative robots as research objects of media and communication studies, Media Cult. Soc., 7, 1410, 10.1177/0163443720916412 · DOI
  10. Hohenstein, J., Kizilcec, R.F., DiFranzo, D., Aghajari, Z., Mieczkowski, H., Levy, K., Naaman, M., Hancock, J., and Jung, M.F. (2023). Artificial intelligence in communication impacts language and social relationships. Sci. Rep., 13. · DOI
  11. Sundar, 2022, Rethinking communication in the era of artificial intelligence, Hum. Commun. Res., 48, 379, 10.1093/hcr/hqac014 · DOI
  12. Guzman, 2020, Artificial intelligence and communication: A Human-Machine Communication research agenda, New Media Soc., 22, 70, 10.1177/1461444819858691 · DOI · Paper page
  13. Xie, 2022, Does artificial intelligence satisfy you? A meta-analysis of gratifications and user experience of AI-powered chatbots, Int. J. Hum.-Comput. Interact., 40, 613, 10.1080/10447318.2022.2121458 · DOI
  14. Broussard, 2019, Artificial intelligence and journalism, J. Mass Commun. Q., 3, 673
  15. Trattner, 2021, Responsible media technology and AI: Challenges and research directions, AI Ethics, 4, 585
  16. Ceron, 2021, Artificial intelligence in news media: Current perceptions and future outlook, J. Media, 1, 13
  17. Cheng, 2021, Customer–brand relationship in the era of artificial intelligence: Understanding the role of chatbot marketing efforts, J. Prod. Brand Manag., 30, 615
  18. Cheng, 2024, Using a chatbot to combat misinformation: Exploring gratifications, chatbot satisfaction and engagement, and relationship quality, Int. J. Hum.-Comput. Interact., 1, 1
  19. Goncalves, 2023, Artificial Intelligence and Its Ethical Implications for Marketing, Emerg. Sci. J., 7, 313, 10.28991/ESJ-2023-07-02-01 · DOI
  20. Cho, 2006, The state of internet-related research in communications, marketing, and advertising: 1994–2003, J. Advert., 35, 143, 10.2753/JOA0091-3367350309 · DOI
  21. Ki, 2022, The status of diversity research in public relations: An analysis of published articles, Prism, 17, 1
  22. Riffe, 1997, A content analysis of content analyses: Twenty-five years of Journalism Quarterly, J. Mass Commun. Q., 74, 873
  23. Mikki, 2009, Google Scholar compared to Web of Science: A literature review, Nord. J. Inf. Lit. High. Educ., 1, 41
  24. Kitchenham, 2013, A systematic review of systematic review process research in software engineering, Inf. Softw. Technol., 55, 2049, 10.1016/j.infsof.2013.07.010 · DOI · Paper page
  25. Zhao, 2006, Humanoid social robots as a medium of communication, New Media Soc., 8, 401, 10.1177/1461444806061951 · DOI
  26. Ha, 2014, Has crisis communication been studied using an interdisciplinary approach? A 20-year content analysis of communication journals, Int. J. Strateg. Commun., 8, 29, 10.1080/1553118X.2013.850694 · doi:10.1080/1553118x.2013.850694
  27. Lee, 2017, The status of corporate social responsibility research in public relations: A content analysis of published articles in eleven scholarly journals from 1980 to 2015, Public Relat. Rev., 43, 211, 10.1016/j.pubrev.2016.10.001 · doi:10.1016/j.pubrev.2016.10.001
  28. Krippendorff, K. (2018). Content Analysis: An Introduction to Its Methodology, Sage Publications. · DOI · Paper page
  29. Men, 2022, Harnessing the power of chatbot social conversation for organizational listening: The impact on perceived transparency and organization-public relationships, J. Public Relat. Res., 34, 20, 10.1080/1062726X.2022.2068553 · DOI
  30. Carlson, 2021, Conjecturing fearful futures: Journalistic discourses on deepfakes, J. Pract., 15, 803
  31. Calice, 2023, Polarized platforms? How partisanship shapes perceptions of “algorithmic news bias”, New Media Soc., 25, 2833, 10.1177/14614448211034159 · DOI
  32. Cardon, 2021, Recorded business meetings and AI algorithmic tools: Negotiating privacy concerns, psychological safety, and control, Int. J. Bus. Commun., 60, 1095, 10.1177/23294884211037009 · DOI
  33. Xu, 2022, Smart speakers require smart management: Two routes from user gratifications to privacy settings, Int. J. Commun., 16, 192
  34. Lim, S., and Shim, H. (2022). No secrets between the two of us: Privacy concerns over using AI agents. Cyberpsychol. J. Psychosoc. Res. Cyberspace, 16. · DOI
  35. Hamilton, 2021, Putting the “Me” in endorsement: Understanding and conceptualizing dimensions of self-endorsement using intelligent personal assistants, New Media Soc., 23, 1506, 10.1177/1461444820912197 · DOI
  36. Panda, 2019, Artificial intelligence: A strategic disruption in public relations, J. Creat. Commun., 14, 196, 10.1177/0973258619866585 · DOI
  37. Galloway, 2018, Public relations and artificial intelligence: It’s not (just) about robots, Public Relat. Rev., 44, 734, 10.1016/j.pubrev.2018.10.008 · DOI
  38. Wu, 2022, Understanding Twitter conversations about artificial intelligence in advertising based on natural language processing, Int. J. Advert., 41, 685, 10.1080/02650487.2021.1920218 · DOI
  39. Nilsson, N.J. (2010). The Quest for Artificial Intelligence: A History of Ideas and Achievements, Cambridge University Press.
  40. Brennen, 2022, What to expect when you’re expecting robots: Futures, expectations, and pseudo artificial general intelligence in UK news, Journalism, 23, 22, 10.1177/1464884920947535 · DOI
  41. Igartua, J.-J., González-Vázquez, A., and Arcila-Calderón, C. (2022). Using testimonial narratives to persuade people about artificial intelligence: The role of attitudinal similarity with the protagonist of the message. Prof. Inf., 31. · DOI
  42. Bingaman, 2021, “Siri, show me scary images of AI”: Effects of text-based frames and visuals on support for artificial intelligence, Sci. Commun., 43, 388, 10.1177/1075547021998069 · DOI
  43. Qin, 2019, The impact of AI on the advertising process: The Chinese experience, J. Advert., 48, 338, 10.1080/00913367.2019.1652122 · DOI
  44. Vakratsas, 2021, Artificial intelligence in advertising creativity, J. Advert., 50, 39, 10.1080/00913367.2020.1843090 · DOI
  45. Goutier, 2022, Invisible friend or foe? How journalists use and perceive algorithmic-driven tools in their research process, Digit. J., 10, 1775
  46. Jones, 2019, Public service chatbots: Automating conversation with BBC News, Digit. J., 7, 1032
  47. Lewis, 2019, Automation, journalism, and human–machine communication: Rethinking roles and relationships of humans and machines in news, Digit. J., 7, 409
  48. 2021, From data journalism to artificial intelligence: Challenges faced by La Nación in implementing computer vision in news reporting, Palabra Clave, 24, e2437
  49. Owren, 2021, The non-substitutability of local news? Advertising and the decline of journalism’s umbrella market model, Nord. Rev., 42, 1, 10.2478/nor-2021-0001 · DOI
  50. Kothari, 2022, Artificial intelligence and journalism: An agenda for journalism research in Africa, Afr. J. Stud., 43, 17
  51. Xi, 2022, Reconstruction of news production driven by artificial intelligence in China, J. Media Commun. Res., 14, 29
  52. 2021, Impact of Artificial Intelligence on Journalism: Transformations in the company, products, contents and professional profile, Commun. Soc., 34, 177, 10.15581/003.34.1.177-193 · DOI
  53. Schwartz, 2021, Facebook use as a communicative relation: Exploring the relation between Facebook users and the algorithmic news feed, Inf. Commun. Soc., 24, 1041, 10.1080/1369118X.2020.1718179 · DOI
  54. Shin, 2022, The perception of humanness in conversational journalism: An algorithmic information-processing perspective, New Media Soc., 24, 2680, 10.1177/1461444821993801 · DOI
  55. Kang, 2022, AI agency vs. human agency: Understanding human–AI interactions on TikTok and their implications for user engagement, J. Comput.-Mediat. Commun., 27, zmac014, 10.1093/jcmc/zmac014 · DOI
  56. Sun, 2022, Public perceptions and attitudes towards the application of artificial intelligence in journalism: From a China-based survey, J. Pract., 18, 548
  57. Bastian, 2021, Safeguarding the journalistic DNA: Attitudes towards the role of professional values in algorithmic news recommender designs, Digit. J., 9, 835
  58. Yu, 2021, Friend or foe? Human journalists’ perspectives on artificial intelligence in Chinese media outlets, Chin. J. Commun., 14, 409, 10.1080/17544750.2021.1915832 · DOI
  59. Berger, C.R., and Roloff, M.E. (2016). Expectations violation theory. The International Encyclopedia of Interpersonal Communication, John Wiley & Sons, Inc.
  60. Duffy, 2022, The inexorable rise of the robots: Trade journals’ framing of machinery in the workplace, Journalism, 23, 409, 10.1177/1464884920969078 · DOI
  61. Nguyen, 2022, A ‘New Arms Race’? Framing China and the U.S.A. in A.I. News Reporting: A Comparative Analysis of the Washington Post and South China Morning Post, Glob. Media China, 7, 58, 10.1177/20594364221078626 · DOI
  62. Moon, 2022, How can we fight partisan biases in the COVID-19 pandemic? AI source labels on fact-checking messages reduce motivated reasoning, Mass Commun. Soc., 26, 646, 10.1080/15205436.2022.2097926 · DOI
  63. Santini, 2020, Making up audience: Media bots and the falsification of the public sphere, Commun. Stud., 71, 466, 10.1080/10510974.2020.1735466 · DOI
  64. Lin, 2022, The one thing journalistic AI just might do for democracy, Digit. J., 10, 1627
  65. Ng, 2022, Exploring the association between use of conversational artificial intelligence and social capital: Survey evidence from Hong Kong, New Media Soc., 26, 1429, 10.1177/14614448221074047 · DOI
  66. Ibiti, 2022, In search of the Global South: Assessing attitudes of Latin American journalists to artificial intelligence in journalism, J. Stud., 23, 1197
  67. Gunkel, 2012, Communication and artificial intelligence: Opportunities and challenges for the 21st century, Futures Commun., 1, 1
  68. Singh, 2023, Implications & impact of artificial intelligence in digital media: With special focus on social media marketing, E3S Web Conf., 399, 07006, 10.1051/e3sconf/202339907006 · DOI
  69. Shah, 2020, Research trends on the usage of machine learning and artificial intelligence in advertising, Augment. Hum. Res., 5, 19, 10.1007/s41133-020-00038-8 · DOI
  70. Zhao, L. (2022, January 27–29). Efficiency enhancement and mode change of intelligent media communication under the interaction of artificial intelligence technology. Proceedings of the ICMSSP ’22: Proceedings of the 2022 7th International Conference on Multimedia Systems and Signal Processing, Shenzhen China. · doi:10.1145/3545822.3545834
  71. Guzman, 2024, What Generative AI Means for the Media Industries, and Why it Matters to Study the Collective Consequences for Advertising, Journalism, and Public Relations, Emerg. Media, 2, 347, 10.1177/27523543241289239 · DOI
  72. Huang, 2017, Does research on digital public relations indicate a paradigm shift? An analysis and critique of recent trends, Telemat. Inform., 34, 1364, 10.1016/j.tele.2016.08.012 · DOI
  73. Khang, 2012, Social media research in advertising, communication, marketing, and public relations, 1997–2010, J. Mass Commun. Q., 89, 279
  74. Wibawa, 2024, Advancements in natural language processing: Implications, challenges, and future directions, Telemat. Inform. Rep., 16, 100173, 10.1016/j.teler.2024.100173 · DOI
  75. Lundberg, E., and Mozelius, P. (2024). The potential effects of deepfakes on news media and entertainment. AI Soc. · DOI
  76. Porlezza, 2024, AI ethics in journalism (studies): An evolving field between research and practice, Emerg. Media, 2, 356, 10.1177/27523543241288818 · DOI
  77. Chuan, C.-H., Tsai, W.H.S., and Yang, J. (2023). Artificial Intelligence, advertising, and society. Advert. Soc. Q., 24, Available online: https://muse-jhu-edu.auth.lib.niu.edu/article/911198. · DOI
  78. Huh, 2023, ChatGPT, AI advertising, and advertising research and education, J. Advert., 52, 477, 10.1080/00913367.2023.2227013 · DOI
  79. Bowen, 2024, “If it can be done, it will be done:” AI Ethical Standards and a dual role for public relations, Public Relat. Rev., 50, 102513, 10.1016/j.pubrev.2024.102513 · DOI · Paper page
  80. Pompper, D., Place, K.R., and Weaver, C.K. (2023). Artificial intelligence and public relations: Growing opportunities, questions, and concerns. The Routledge Companion to Public Relations, Routledge. · doi:10.4324/9781003131700
  81. Yue, 2024, Public Relations Meets Artificial Intelligence: Assessing Utilization and Outcomes, J. Public Relat. Res., 36, 513, 10.1080/1062726X.2024.2400622 · DOI

Cited by

Metadata

Authors
Ertem-Eray, T., & Cheng, Y.
Affiliations
Department of Communication, North Carolina State University, Raleigh, NC 27695, USA
ORCID
Venue
Applied Sciences
Publisher
MDPI AG
Published
2025-01-22
Volume
15
Issue
3
Pages
1058
ISSN
2076-3417
DOI
10.3390/app15031058
Cited by
11
References
13
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
Global International Seed Grant, North Carolina State University

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.