Hi,
I’ve been working on a framework to model how online discussions escalate into conflict, and I’m exploring whether it can be framed as a classification / sequence modeling problem.
The core idea is to treat discourse as a state machine with observable transitions.
States (proposed)
- Neutral (information exchange)
- Disagreement
- Identity Activation
- Personalization
- Ad Hominem
- Dogpile (multi-user targeting, non-recoverable)
Each comment can be labeled as a local state, while threads also have a global state that evolves over time.
Signals / Features
Some features I’m considering:
- Linguistic:
- increase in second-person pronouns (“you”)
- sentiment shift
- insult / toxicity markers
- Structural:
- number of unique users replying to one user
- reply velocity (bursts)
- depth of thread
- Contextual:
- topic sensitivity (proxy via keywords)
- prior state transitions in thread
Additional dimension
I’m also experimenting with a second layer:
- Personal identity activation
- Ideological identity activation
- Group identity activation
The hypothesis is that simultaneous activation of multiple identity layers correlates with rapid escalation.
Dataset plan
- Collect threads from public platforms (Reddit, etc.)
- Build a labeled dataset using the state taxonomy above
- Start with a small manually annotated dataset
- Train a classifier (baseline: heuristic → ML model)
Questions
- Does this framing make sense as a sequence classification / state transition problem?
- Would you model this as:
- per-comment classification, or
- sequence modeling (e.g., HMM / RNN / transformer over thread)?
- Any suggestions on:
- labeling guidelines to reduce ambiguity between states?
- existing datasets that approximate this (beyond toxicity classification)?
- Would you treat “dogpile” as a class or as an emergent property of the graph structure?
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