Political Disinformation in the Age of Artificial Intelligence: A Systematic Literature Review
DOI:
https://doi.org/10.53905/Veritas.v2i01.3Keywords:
political disinformation, artificial intelligence, generative AI, deepfakes, electoral integrity, democratic governanceAbstract
Purpose of the study: The proliferation of artificial intelligence (AI) technologies — including large language models (LLMs), generative adversarial networks (GANs), social bots, and algorithmic micro-targeting systems — has fundamentally reshaped the production, dissemination, and detection of political disinformation. This study conducts a systematic literature review (SLR) to map how scholarship on AI-driven political disinformation has evolved, to identify the dominant conceptual frameworks and technologies implicated, to assess documented impacts on democratic governance and electoral integrity, and to synthesize proposed mitigation strategies.
Methodology: Following the PRISMA 2020 guidelines, a systematic search was conducted across Scopus, Web of Science, and Google Scholar for peer-reviewed journal articles, conference proceedings, and policy-relevant reports published between January 2016 and June 2026. A Boolean search string combining disinformation-related and AI-related terms was applied to titles, abstracts, and keywords. Records were screened against pre-defined eligibility criteria, and data were extracted using a structured matrix covering methodology, geographic scope, AI technology, and thematic focus.
Results: Of 452 records identified through database searching and 21 through supplementary sources, 37 studies met the inclusion criteria and were retained for narrative synthesis. Research output increased markedly from 2020 onward, accelerating sharply after the public release of generative AI chatbots in late 2022. Five dominant thematic clusters emerged: AI as a source of disinformation, AI as a countermeasure, regulatory and governance frameworks, deepfake-specific scholarship, and algorithmic/media literacy. Deepfakes, LLM-generated text, and social bots were the most frequently studied technologies. Documented impacts on democratic governance included erosion of institutional trust, reputational attacks on candidates, and heightened epistemic uncertainty ("liar's dividend"), although causal evidence of vote-switching remained limited. Mitigation strategies clustered around automated detection and fact-checking, platform governance, regulatory instruments (e.g., the EU AI Act), and media literacy interventions, each showing partial but incomplete effectiveness.
Conclusions: AI-driven political disinformation constitutes a rapidly consolidating, interdisciplinary research field characterized by a dual-use paradigm in which the same technologies that generate disinformation are repurposed to counter it. Current evidence favors layered, multi-stakeholder responses over single-point technical fixes. The review identifies persistent gaps in cross-national comparative research, longitudinal causal designs, and multilingual detection capability, and offers a research agenda for scholars, platforms, and policymakers.
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