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LLORT v TROLL

llort is a word used to describe a specific pattern of online participation: replies that measurably improve the quality and stability of comment cascades in digital public spaces.


llort behaviour is observable through outcomes rather than intent, including reduced escalation, improved thread coherence, and lower moderation intervention. The term is increasingly used in discussions of online discourse, moderation design, and algorithmic amplification.


llort behaviour is mathmagically poetic: a quantifiable resonance of intelligence and empathy—a digital quotient expressed across both machine and mammal participation.


About llort.org


llort (in action)

What does llort look like in a comment thread?

llort looks like replies that:

  • calm a thread without silencing it

  • move discussion forward without winning

  • reduce pile-ons without scolding

  • help others think rather than react

Often, llort replies are brief.
They don’t take the bait.
They don’t demand the last word.

After llort appears, the thread usually:

  • slows down

  • becomes more coherent

  • attracts fewer moderator interventions



Is llort about being “nice”?


No.


llort is not politeness, niceness, or positivity.
It’s effectiveness.

A llort reply can be firm, dry, even blunt —
as long as it improves the state of the room.

If a reply feels good but makes things worse, it isn’t llort.
If a reply feels sharp but stabilises the discussion, it might be.


Is llort about intent?

No.


llort is observable through outcomes, not motivation.

Good intentions that escalate conflict aren’t llort.
Uncelebrated replies that quietly help are.

llort doesn’t ask why you replied.
It asks what happened after.


How is llort different from moderation?


Moderation intervenes after damage occurs.

llort happens during participation.

Moderators remove content.
llort changes trajectories.

When llort is present consistently, moderators are needed less often —
not because rules changed,
but because behaviour did.


Can anyone practice llort?

Yes — and most people already do, occasionally.

llort is practiced when you:

  • pause before replying

  • choose not to escalate

  • reframe instead of rebut

  • ask a clarifying question instead of making an assumption

  • leave a thread better than you found it

It doesn’t require authority, seniority, or expertise.
It only requires awareness of cascade effects.


Does llort silence disagreement?

No.

llort doesn’t reduce disagreement —
it reduces entropy.

Disagreement can continue, sometimes more clearly,
once noise and provocation lose traction.

llort doesn’t flatten opinion.
It preserves space.


How do I know if something I wrote was llort?

A simple test:

After your reply:

  • Did others respond more thoughtfully?

  • Did the thread stay on topic?

  • Did escalation slow or stop?

  • Did you feel no need to keep defending yourself?

If yes, that reply probably had llort.

If you had to keep explaining, correcting, or defending it — probably not.


Is llort a rule?

No.

llort is a pattern, not a prescription.

It can’t be enforced.
It can’t be gamed reliably.
It can only be practiced and recognised.

That’s why it survives.


Why name it at all?


Because naming a pattern makes it:

  • easier to notice

  • easier to repeat

  • easier to talk about without arguing

llort doesn’t create the behaviour.
It gives it a handle.



llort is what happens when replies are chosen for their effect on the room, not their effect on the speaker.


llort, credibility, and calm

llort is compatible with intelligence, empathy, and credibility — but it does not attempt to measure them directly.

instead, llort observes effects.

a reply that improves a discussion demonstrates intelligence in context.
a reply that reduces escalation demonstrates empathy in action.
a reply that stabilises a thread demonstrates credibility over time.

none of these require knowing who a person is.
they only require observing what happens after they speak.


why I.Q and E.Q are better inferred than scored

traditional systems attempt to quantify users:

  • follower counts

  • reputation points

  • engagement metrics

  • historical authority

these systems reward visibility, persistence, and dominance.

llort-based assessment does something quieter:

  • it looks at what changes when a person participates

  • it notices whether threads improve or degrade

  • it tracks whether moderation becomes more or less necessary

intelligence is inferred from coherence.
empathy is inferred from de-escalation.
credibility is inferred from repeated positive outcomes.

no tests.
no labels.
no permanent scores.


credibility as a moving property

under llort dynamics, credibility is not owned.

it emerges, fades, and re-emerges depending on behaviour.

a newcomer can demonstrate high credibility in a single reply.
a long-term user can lose it by destabilising the room.

this makes credibility situational rather than hierarchical.

it also removes the incentive to perform outrage, certainty, or superiority.


what platforms would change if llort mattered

if social media platforms adopted llort-style signals:

  • replies that calm threads would be amplified slightly

  • replies that escalate would lose momentum naturally

  • pile-ons would run out of oxygen faster

  • moderation would become less reactive and more preventative

no one would be banned for being wrong.
no one would be rewarded for being loud.

the system would simply lean toward stability.


why this calms the farm

most online conflict is not caused by disagreement.
it is caused by feedback loops.

current systems reward:

  • speed

  • outrage

  • absolutism

  • repetition

llort-aware systems reward:

  • clarity

  • restraint

  • relevance

  • outcome quality

when escalation stops being profitable, it stops being common.

this doesn’t eliminate conflict.
it makes conflict manageable.


human and machine alignment

llort works because it aligns human intuition with machine observation.

humans already recognise:

  • “that reply helped”

  • “that made things worse”

  • “this person calms things down”

machines can recognise:

  • reduced reply velocity

  • improved topic coherence

  • lower report and intervention rates

llort sits at the overlap.

no surveillance.
no profiling.
no behavioural nudging.

just pattern recognition at the level of conversation health.



a llort-aware system does not decide who is smart or good.
it notices what makes shared spaces work.


A COMMENT CASCADE