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Research Paper Volume 9 Issue 2 2488 - 2497 April 28, 2026

Social Media, Artificial Intelligence and Election Credibility: A Global Perspective

Lead author · Corresponding
Y. P. Singh
Principal at Government Polytechnic College, Sagar, Madhya Pradesh, India
View PDF Full text DOIhttps://doij.org/10.10000/IJLMH.1111794
Abstract

The rapid expansion of social media and AI has transformed electoral communication and reshaped how citizens access information, engage in political discourse and evaluate the credibility of elections. In this paper, the way digital platforms impact electoral integrity is analyzed through political content diffusion, misinformation circulation and evolving campaign strategies. Using global examples from North America, South America, Europe, Africa, Asia and Oceania-including India-it shows how digital ecosystems can catalyze participation on the one hand and diminish democratic trust on the other. Conceptual models are used to illustrate relationships among the use of social media, AI-generated content and voter perceptions. Evidence is synthesized from theories in communication, behavioral research and policy reports by various international organizations such as International IDEA, UNESCO, UNDP and national election authorities. It argues that the credibility of elections in the digital age depends not only on technological safeguards but also on institutional transparency, public digital literacy and platform accountability. In ending, it summarizes some policy implications for election management bodies and governments from around the world.

Type
Research Paper
Information
International Journal of Law Management and Humanities, Volume 9, Issue 2, Page 2488 - 2497
DOI: https://doij.org/10.10000/IJLMH.1111794
Creative Commons
CC BY-NC 4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution–NonCommercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/), which permits remixing, adapting, and building upon the work for non-commercial use, provided the original work is properly cited.
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Copyright © IJLMH 2026
Disclaimer
The views and opinions expressed in this manuscript are those of the author(s) alone and do not reflect the views, policies, or position of the Journal.

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