AI-Generated Misinformation: A Literature Review

Rafharum Fatimah; Auziah Mumtaz; Fauzan Muhammad Fahrezi; Diky Zakaria

doi:10.24014/ijaidm.v7i2.26455

AI-Generated Misinformation: A Literature Review

Rafharum Fatimah, Auziah Mumtaz, Fauzan Muhammad Fahrezi, Diky Zakaria

Abstract

The expansion of artificial intelligence (AI) technologies has signaled an entirely new era in which the creation and sharing of information, both correct and misleading, are becoming increasingly automated. This research of the literature explores the landscape of AI-generated misinformation, including its various manifestations, underlying technology, societal impact, and detection tools. This paper reviews articles from the Google Scholar database related to AI-Generated Misinformation focusing on the following research questions: the types, content distribution, detector variations, differences among the various tools, and strategies for developing AI-based tools. The result is to provide an absolute comprehension of this topic, underlining the importance of interdisciplinary collaboration, robust detection methods, and media literacy with the intention to solve the ethical and societal issues it poses in the age of digital technology.

Keywords

AI-generated; Artificial Intelligence; Detection Tools; Literature Review; Misinformation

References

S. Bhbosale, V. Pujari, and Z. Multani, “Advantages and Disadvantages of Artificial Intelligence,” in Aayushi International Interdisciplinary Research Journal, 2020, pp. 227–230. [Online]. Available: https://www.researchgate.net/profile/Vinayak-Pujari-2/publication/344584269_Advantages_And_Disadvantages_Of_Artificial_Intellegence/links/5f81b70192851c14bcbc1d96/Advantages-And-Disadvantages-Of-Artificial-Intellegence.pdf%0Awww.aiirjournal.com

Y. Duan, J. S. Edwards, and Y. K. Dwivedi, “Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda,” Int. J. Inf. Manage., vol. 48, pp. 63–71, 2019, doi: 10.1016/j.ijinfomgt.2019.01.021.

N. Berente, B. Gun, J. Recker, and R. Santhanam, “MANAGING ARTIFICIAL INTELLIGENCE,” vol. 45, no. September 2021, pp. 1–18, 2021, doi: 10.25300/MISQ/2021/16274.

Y. Yang, Y. Zhuang, and Y. Pan, “Multiple knowledge representation for big data artificial intelligence: framework, applications, and case studies,” Front. Inf. Technol. Electron. Eng., vol. 22, no. 12, pp. 1551–1558, 2021, doi: 10.1631/FITEE.2100463.

J. Paschen, “Investigating the emotional appeal of fake news using artificial intelligence and human contributions,” J. Prod. Brand Manag., vol. 29, no. 2, pp. 223–233, 2020, doi: 10.1108/JPBM-12-2018-2179.

M. Masood, M. Nawaz, K. M. Malik, A. Javed, A. Irtaza, and H. Malik, “Deepfakes generation and detection: state-of-the-art, open challenges, countermeasures, and way forward,” Appl. Intell., vol. 53, no. 4, pp. 3974–4026, 2023, doi: 10.1007/s10489-022-03766-z.

M. Salah, H. Al Halbusi, and F. Abdelfattah, “May the force of text data analysis be with you: Unleashing the power of generative AI for social psychology research,” Comput. Hum. Behav. Artif. Humans, vol. 1, no. 2, p. 100006, 2023, doi: 10.1016/j.chbah.2023.100006.

Z. Bahroun, C. Anane, V. Ahmed, and A. Zacca, “Transforming Education: A Comprehensive Review of Generative Artificial Intelligence in Educational Settings through Bibliometric and Content Analysis,” Sustain., vol. 15, no. 17, p. 12983, 2023, doi: 10.3390/su151712983.

A. Adadi, M. Lahmer, and S. Nasiri, “Artificial Intelligence and COVID-19: A Systematic umbrella review and roads ahead,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 8, pp. 5898–5920, 2022, doi: 10.1016/j.jksuci.2021.07.010.

A. R. Vargas-Murillo, I. N. M. de la Asuncion Pari-Bedoya, and F. de Jesús Guevara-Soto, “Challenges and Opportunities of AI-Assisted Learning: A Systematic Literature Review on the Impact of ChatGPT Usage in Higher Education,” Int. J. Learn. Teach. Educ. Res., vol. 22, no. 7, pp. 122–135, 2023, doi: 10.26803/ijlter.22.7.7.

J. Guerrero and I. Alsmadi, “Synthetic Text Detection: Systemic Literature Review,” pp. 1–9, 2022, [Online]. Available: http://arxiv.org/abs/2210.06336

A. Bhattacharjee, T. Kumarage, R. Moraffah, and H. Liu, “ConDA: Contrastive Domain Adaptation for AI-generated Text Detection,” pp. 1–13, 2023, [Online]. Available: http://arxiv.org/abs/2309.03992

K. Dalkir, “Fake news and AI: Fighting fire with fire?,” CEUR Workshop Proc., vol. 2942, pp. 112–115, 2021.

Y. R. Fung et al., “InfoSurgeon: Cross-media fine-grained information consistency checking for fake news detection,” in ACL-IJCNLP 2021 - 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Proceedings of the Conference, 2021, pp. 1683–1698. doi: 10.18653/v1/2021.acl-long.133.

M. Heumann and T. Kraschewski, “ChatGPT and GPTZero in Research and Social Media: A Sentiment-and Topic-based Analysis Optimal end-of-life strategies for aging wind turbines View project,” 2023.

M. Iceland, “How Good Are SOTA Fake News Detectors,” pp. 1–13, 2023, [Online]. Available: http://arxiv.org/abs/2308.02727

J. J. Koplin, “Dual-use implications of AI text generation,” Ethics Inf. Technol., vol. 25, no. 2, pp. 1–11, 2023, doi: 10.1007/s10676-023-09703-z.

S. Kreps, R. M. McCain, and M. Brundage, “All the News That’s Fit to Fabricate: AI-Generated Text as a Tool of Media Misinformation,” J. Exp. Polit. Sci., vol. 9, no. 1, pp. 104–117, 2022, doi: 10.1017/XPS.2020.37.

P. Kulkarni, Z. Ji, Y. Xu, M. Neskovic, and K. Nolan, “Exploring Semantic Perturbations on Grover,” pp. 1–15, 2023, [Online]. Available: http://arxiv.org/abs/2302.00509

T. Kumarage, J. Garland, A. Bhattacharjee, K. Trapeznikov, S. Ruston, and H. Liu, “Stylometric Detection of AI-Generated Text in Twitter Timelines,” pp. 1–13, 2023, [Online]. Available: http://arxiv.org/abs/2303.03697

C. Baecker, G. P. Yogiputra, T. D. Nguyen, and O. Alabbadi, “Threats provided by artificial intelligence that could disrupt the democratic system,” 2023.

B. Alerie and R. McCreight, “The Ethics of Generative AI in Tax Practice,” 2023.

D. Shu et al., “A Pilot Study Investigating STEM Learners’ Ability to Decipher AI-generated Video,” in ASEE Annual Conference and Exposition, Conference Proceedings, 2021, pp. 1–20. doi: 10.18260/1-2--36601.

S. Wang, N. Cooper, M. Eby, and E. S. Jo, “From Human-Centered to Social-Centered Artificial Intelligence: Assessing ChatGPT’s Impact through Disruptive Events,” pp. 1–23, 2023, [Online]. Available: http://arxiv.org/abs/2306.00227

K.-C. Yang and F. Menczer, “Anatomy of an AI-powered malicious social botnet,” pp. 1–27, 2023, [Online]. Available: http://arxiv.org/abs/2307.16336

J. Zhou, Y. Zhang, Q. Luo, A. G. Parker, and M. De Choudhury, “Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions,” Conf. Hum. Factors Comput. Syst. - Proc., pp. 1–20, 2023, doi: 10.1145/3544548.3581318.

DOI: http://dx.doi.org/10.24014/ijaidm.v7i2.26455

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