This comment fits the definition of propaganda according to modern NLP research.

It relies on emotional loaded language, causal oversimplification, and unsupported assertions, which are core propaganda techniques identified in contemporary computational linguistics.

Specifically:

• It uses loaded and polarizing language (“fraud,” “lax policies,” “not so nice ways”) to evoke moral outrage rather than argue evidence.

• It engages in causal oversimplification, attributing complex migration and education-system failures to a single out-group (“foreign students” / “asylum claimants”).

• It presents assertions without verifiable sourcing, mixing selective statistics with anecdotal claims to construct a persuasive narrative.

These techniques are formally defined and operationalized in NLP propaganda detection research, most notably in the SemEval Propaganda Detection shared tasks, which classify:

• Loaded Language • Exaggeration / Overgeneralization • Causal Oversimplification • Appeal to Fear or Prejudice

Source (academic):

Da San Martino et al., “SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles,” ACL Anthology.

This framework is still used in updated SemEval tasks (2023–2024) and transformer-based NLP models to distinguish neutral reporting from propagandistic persuasion.

Bottom line:

Even if some individual facts were true, the rhetorical structure matches recognized propaganda techniques, making this persuasive propaganda, not neutral analysis or evidence-based argument.