aiml-propaganda-benchmark

To benchmark a multilingual propaganda detection model against SemEval-2023 techniques, run the included benchmark and test scripts against the provided propaganda_samples.json dataset to evaluate technique coverage and report missing labels.

A SemEval-2023 propaganda benchmark evaluates nine specific techniques: loaded_language, name_calling, appeal_to_fear, flag_waving, causal_oversimplification, false_dilemma, whataboutism, appeal_to_authority, and bandwagon across multilingual texts.

Validating multilingual text samples for propaganda classification benchmarks requires enforcing data integrity checks for placeholder absence, text length limits, and the presence of technique-specific markers before running evaluation scripts.

You can use a configurable dataset to test a propaganda classifier for cross-language evaluation, ensuring your input texts pass placeholder absence and length checks to generate reliable benchmarking results.

The best way to identify weaknesses in a multilingual propaganda detector is to run standardized benchmark evaluations across nine techniques and multiple languages, analyzing the reported results for missing techniques and classification gaps.

Your propaganda benchmark script reports missing techniques when the evaluated multilingual detector fails to identify specific markers for all nine SemEval-2023 categories within the provided test samples.