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Article . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Article . 2026
License: CC BY
Data sources: Datacite
ZENODO
Article . 2026
License: CC BY
Data sources: Datacite
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Artificial Intelligence In Electoral Behavior Studies: A Study

Authors: Sangeeta Chandrasha Dandoti;

Artificial Intelligence In Electoral Behavior Studies: A Study

Abstract

Artificial intelligence has advanced a lot in the 21st century, making its mark in every field, taking technology to a higher level. This artificial intelligence is also playing a major role in the field of politics. Artificial Intelligence has brought new techniques to the field of Political Science, especially in 'Electoral Behavior Studies', to evaluate, predict and influence voter behavior. The term 'Electoral Behavior' describes how people and groups participate in elections, including their voting decisions, political leanings and levels of participation. In the past, surveys, interviews and statistical tools were widely used in this field. However, researchers and political actors now have access to sophisticated tools that enable deep insights into voter psychology and decision-making processes thanks to the development of artificial intelligence. This paper explores the place of artificial intelligence in electoral behavior research, as well as its uses, benefits, pitfalls, ethical dilemmas and potential.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green