Crisis communications
Frameworks for accurate, rapid communication during high-pressure situations.
When to activate
- Breaking news requires immediate coverage
- Organization faces public crisis or controversy
- Misinformation is spreading rapidly and needs countering
- Emergency situation requires coordinated communication
- Rapid fact-checking is needed before publication
- Preparing crisis response plans before incidents occur
Breaking news protocol
First 30 minutes checklist
## Breaking news response
**Event**: [Brief description]
**Time detected**: [HH:MM]
**Initial source**: [Where we learned of this]
### Immediate actions (0-10 min)
- [ ] Verify event is real (minimum 2 independent sources)
- [ ] Alert editor/team lead
- [ ] Check wire services (AP, Reuters, AFP)
- [ ] Monitor official accounts (agencies, officials)
- [ ] DO NOT publish unverified claims
### Verification phase (10-30 min)
- [ ] Primary source contacted/confirmed
- [ ] Location verified (if applicable)
- [ ] Official statement obtained or requested
- [ ] Eyewitness accounts gathered (note: unverified)
- [ ] Social media claims flagged for verification
### First publication decision
- [ ] What we KNOW (confirmed facts only)
- [ ] What we DON'T know (be explicit)
- [ ] What we're working to confirm
- [ ] Attribution clear for every claim
Newsroom escalation matrix
from enum import Enum
from dataclasses import dataclass
from typing import List
class CrisisLevel(Enum):
LEVEL_1 = "routine" # Single reporter can handle
LEVEL_2 = "elevated" # Editor involvement needed
LEVEL_3 = "major" # Multiple reporters, editor oversight
LEVEL_4 = "critical" # All hands, executive involvement
@dataclass
class BreakingEvent:
description: str
level: CrisisLevel
confirmed_facts: List[str]
unconfirmed_claims: List[str]
sources_contacted: List[str]
assigned_reporters: List[str]
def escalation_needed(self) -> bool:
"""Determine if event needs escalation."""
triggers = [
len(self.unconfirmed_claims) > 5, # Too many unknowns
"fatalities" in self.description.lower(),
"official" in self.description.lower(),
"government" in self.description.lower(),
]
return any(triggers)
ESCALATION_TRIGGERS = {
CrisisLevel.LEVEL_2: [
"Multiple fatalities confirmed",
"Major public figure involved",
"Legal/liability concerns",
"Significant local impact",
],
CrisisLevel.LEVEL_3: [
"National news potential",
"Active danger to public",
"Major institution affected",
"Coordinated misinformation detected",
],
CrisisLevel.LEVEL_4: [
"Mass casualty event",
"Government/democracy implications",
"Our organization directly involved",
"Imminent physical threat",
],
}
Rapid verification framework
The 5-minute verification
When time is critical, prioritize these checks:
## Rapid verification checklist
### Source check (1 min)
- [ ] Who is claiming this?
- [ ] Are they in position to know?
- [ ] Have they been reliable before?
### Corroboration (2 min)
- [ ] Does anyone else confirm?
- [ ] Check wire services
- [ ] Check official sources
### Red flags (1 min)
- [ ] Too perfect/dramatic?
- [ ] Matches known false narratives?
- [ ] Single source only?
### Decision (1 min)
- [ ] PUBLISH: Multiple credible sources, no red flags
- [ ] HOLD: Needs more verification
- [ ] MONITOR: Developing, don't publish yet
Verification triage
For verification mechanics (source credibility, image/video checks, archiving evidence), use the source-verification skill. This skill focuses on the time-pressure layer: what to triage first, who decides to publish, and how to communicate uncertainty.
Triage rule: claims with the highest physical-harm or election-impact potential go first, then claims that name specific people, then everything else.
Crisis communication templates
Holding statement
For when you need to say something but facts are incomplete:
## Initial statement template
[Organization] is aware of [brief description of situation].
We are actively [gathering information / investigating / monitoring the situation].
[If applicable: The safety of [stakeholders] is our top priority.]
We will provide updates as verified information becomes available.
For media inquiries: [contact]
For [affected parties]: [resource/hotline]
Last updated: [timestamp]
Correction/clarification
## Correction notice
**Correction [or Clarification]**: An earlier version of this [article/statement/post] [stated/implied] [incorrect information].
[If factual error]: The correct information is: [accurate facts].
[If misleading]: To clarify: [accurate context].
This [article/statement] has been updated to reflect accurate information.
We regret the error.
[Timestamp of correction]
Retraction (when necessary)
## Retraction notice
**Retraction**: [Publication name] is retracting [article title], published on [date].
[Brief explanation of what was wrong]: Our reporting [stated/relied on] [problematic element]. Subsequent verification revealed [why it was wrong].
[What you're doing about it]: We have [removed/updated] the article and are reviewing our editorial processes.
[Accountability]: We apologize to [affected parties] and our readers.
The full text of the original article is available [here/on request] for transparency.
Questions: [contact]
Social media crisis response
Monitoring during crisis
from dataclasses import dataclass
from datetime import datetime
from typing import List, Dict
from collections import Counter
@dataclass
class CrisisMention:
platform: str
content: str
author: str
timestamp: datetime
sentiment: str # positive, negative, neutral, misinformation
reach: int # followers/potential impressions
requires_response: bool = False
class CrisisMonitor:
"""Track crisis-related social mentions."""
def __init__(self, crisis_keywords: List[str]):
self.keywords = crisis_keywords
self.mentions: List[CrisisMention] = []
def add_mention(self, mention: CrisisMention):
self.mentions.append(mention)
def get_dashboard(self) -> dict:
"""Real-time crisis overview."""
recent = [m for m in self.mentions
if (datetime.now() - m.timestamp).seconds < 3600]
return {
'total_mentions_1h': len(recent),
'sentiment_breakdown': self._sentiment_counts(recent),
'top_platforms': self._platform_counts(recent),
'misinformation_count': len([
m for m in recent
if m.sentiment == 'misinformation'
]),
'high_reach_negative': [
m for m in recent
if m.sentiment == 'negative' and m.reach > 10000
],
'pending_responses': len([
m for m in self.mentions
if m.requires_response
])
}
def _sentiment_counts(self, mentions: List[CrisisMention]) -> Dict:
return dict(Counter(m.sentiment for m in mentions))
def _platform_counts(self, mentions: List[CrisisMention]) -> Dict:
return dict(Counter(m.platform for m in mentions))
Response decision tree
## Should we respond to this?
### High priority (respond quickly)
- [ ] Factual misinformation spreading rapidly
- [ ] Direct question from journalist
- [ ] Affected party seeking help
- [ ] Influential account spreading false info
### Medium priority (respond thoughtfully)
- [ ] General negative sentiment
- [ ] Questions about our response
- [ ] Comparisons to competitors' handling
### Low priority / Do not engage
- [ ] Obvious trolling
- [ ] Ba