After getting dogpiled on Reddit (intentionally, for research), I formalized what I observed into a framework called IDDS — Identity-Driven Discourse Systems.
The core insight: escalation is not random. It follows predictable state transitions driven by identity layer activation. The key innovation in 2.1 is the D_flag modifier — Identity Activation only accelerates escalation when disagreement is already present. This means someone sharing their identity in a friendly thread (D_flag=0) behaves completely differently from the same disclosure in an adversarial thread (D_flag=1).
States: Neutral → Disagreement → Identity Activation → Personalization → Ad Hominem → Dogpile
New in 2.1:
- MPF (Moral Protective Framing): "protecting children" as ethical cover for escalation — invisible to sentiment analysis, requires contextual state awareness
- Adversarial Seeding: threads born escalated at T=0 before the first reply
- Silence Bypass: block/mute only terminates the local thread, not the conflict
- Transient Dogpile Groups: the group never fully resets D_flag between targets
Validated across Reddit, Threads, WhatsApp in English and Portuguese. Building a Playwright scraper + ML classifier next.
Paper:https://github.com/JohannaWeb/Monarch/releases/tag/2.1.paper
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