| name | strategic-communication-analysis |
| description | Narrative framing, media analysis, discourse analysis, influence assessment, information warfare detection for Swedish political intelligence |
| license | Apache-2.0 |
Strategic Communication Analysis Skill
Purpose
This skill provides comprehensive methodologies for analyzing strategic political communication in Swedish parliament and media. It enables intelligence operatives to identify narrative framing techniques, detect coordinated messaging campaigns, assess media bias, analyze parliamentary discourse patterns, and identify information warfare tactics through evidence-based analysis of speeches, documents, and media coverage.
When to Use This Skill
Apply this skill when:
- ✅ Analyzing parliamentary debate rhetoric and framing strategies
- ✅ Detecting coordinated messaging campaigns across party members
- ✅ Identifying media bias in political coverage
- ✅ Assessing influence operations and disinformation patterns
- ✅ Analyzing discourse shifts over time (framing evolution)
- ✅ Evaluating communication effectiveness of political figures
- ✅ Detecting astroturfing and inauthentic amplification
- ✅ Mapping information ecosystems and echo chambers
- ✅ Identifying counter-narrative opportunities
Do NOT use for:
- ❌ Content censorship or suppression of legitimate political speech
- ❌ Manipulating public discourse through disinformation
- ❌ Violating freedom of expression protections
- ❌ Personal attacks on communication style without policy substance
Strategic Communication Framework
Five Analytical Dimensions
The CIA platform analyzes political communication across five critical dimensions to understand messaging strategies and influence dynamics:
graph TB
subgraph "Communication Intelligence Collection"
A1["📝 Parliamentary Documents<br/>89K+ documents<br/>Motions, interpellations, bills"]
A2["🎤 Debate Transcripts<br/>Parliamentary speeches<br/>Committee testimony"]
A3["📰 Media Coverage<br/>News articles, press releases<br/>Social media posts"]
A4["🗳️ Voting Records<br/>Action-rhetoric alignment<br/>Behavior vs. statements"]
A5["🤝 Coalition Messaging<br/>Party coordination<br/>Message discipline"]
end
subgraph "Communication Analysis"
A1 --> B1[Narrative Framing Analysis]
A2 --> B2[Discourse Pattern Recognition]
A3 --> B3[Media Bias Detection]
A4 --> B4[Rhetoric-Action Gap Analysis]
A5 --> B5[Coordination Assessment]
end
subgraph "Intelligence Products"
B1 --> C1[Frame Identification]
B2 --> C2[Discourse Networks]
B3 --> C3[Bias Profiles]
B4 --> C4[Credibility Assessment]
B5 --> C5[Campaign Detection]
end
subgraph "Strategic Intelligence"
C1 & C2 & C3 & C4 & C5 --> D["📊 Communication Profile"]
D --> E[Influence Assessment]
D --> F[Disinformation Detection]
D --> G[Counter-Narrative Strategy]
end
style A1 fill:#e1f5ff
style A2 fill:#e1f5ff
style A3 fill:#e1f5ff
style A4 fill:#e1f5ff
style A5 fill:#e1f5ff
style D fill:#ffe6cc
style E fill:#ccffcc
style F fill:#ffcccc
style G fill:#fff9cc
1. Narrative Framing Analysis
Framing Theory Application
Political actors use framing to define issues in ways that advantage their positions. The CIA platform identifies dominant frames through document and speech analysis.
Database Views:
view_riksdagen_politician_document - All politician-authored documentsview_riksdagen_document_content - Full document text for NLP analysisview_riksdagen_document_element - Document structure and metadata
Frame Identification System
from typing import Dict, List, Tuple
import re
from collections import Counter
from dataclasses import dataclass
@dataclass
class Frame:
"""Political framing structure."""
frame_type: str
keywords: List[str]
metaphors: List[str]
values: List[str]
emotional_tone: str
class NarrativeFramingAnalyzer:
"""
Identifies narrative frames in political communication.
Based on Entman (1993) framing theory:
1. Problem definition
2. Causal interpretation
3. Moral evaluation
4. Treatment recommendation
"""
FRAME_TEMPLATES = {
'ECONOMIC_COMPETITIVENESS': Frame(
frame_type='ECONOMIC_COMPETITIVENESS',
keywords=['konkurrenskraft', 'tillväxt', 'företagande', 'innovation', 'export'],
metaphors=['motor', 'drivkraft', 'framtid'],
values=['prosperity', 'efficiency', 'growth'],
emotional_tone='aspirational'
),
'SOCIAL_JUSTICE': Frame(
frame_type='SOCIAL_JUSTICE',
keywords=['rättvisa', 'jämlikhet', , , ],
metaphors=[, , ],
values=[, , ],
emotional_tone=
),
: Frame(
frame_type=,
keywords=[, , , , ],
metaphors=[, , ],
values=[, , ],
emotional_tone=
),
: Frame(
frame_type=,
keywords=[, , , , ],
metaphors=[, , ],
values=[, , ],
emotional_tone=
),
: Frame(
frame_type=,
keywords=[, , , , ],
metaphors=[, , ],
values=[, , ],
emotional_tone=
),
: Frame(
frame_type=,
keywords=[, , , , ],
metaphors=[, , ],
values=[, , ],
emotional_tone=
)
}
() -> :
query =
doc = pd.read_sql(query, .connection, params=[document_id]).iloc[]
text_content = doc[].lower()
title_content = .lower()
frame_scores = {}
frame_evidence = {}
frame_name, frame .FRAME_TEMPLATES.items():
keyword_score = (
text_content.count(keyword) keyword frame.keywords
) / (frame.keywords)
metaphor_score = (
text_content.count(metaphor) * metaphor frame.metaphors
) / (frame.metaphors)
title_score = (
title_content.count(keyword) * keyword frame.keywords
) / (frame.keywords)
total_score = keyword_score + metaphor_score + title_score
frame_scores[frame_name] = (total_score, )
frame_evidence[frame_name] = {
: [kw kw frame.keywords kw text_content],
: [m m frame.metaphors m text_content],
: (kw title_content kw frame.keywords)
}
sorted_frames = (frame_scores.items(), key= x: x[], reverse=)
dominant_frame = sorted_frames[][] sorted_frames[][] >
{
: document_id,
: doc[],
: doc[],
: doc[],
: dominant_frame,
: frame_scores,
: frame_evidence[dominant_frame],
: sorted_frames[][],
: ([s s sorted_frames s[] > ]) > ,
: doc[]
}
() -> :
query =
documents = pd.read_sql(query, .connection,
params=[party_id, date_from, date_to])
party_frames = []
_, doc documents.iterrows():
frame_analysis = .analyze_document_framing(doc[])
frame_analysis[] = doc[]
frame_analysis[] = doc[]
party_frames.append(frame_analysis)
frame_distribution = Counter([f[] f party_frames])
total_docs = (party_frames)
authors = (([f[] f party_frames]))
author_frames = {author: Counter() author authors}
frame_analysis party_frames:
author_frames[frame_analysis[]][frame_analysis[]] +=
coordination_score =
(authors) > :
top_frame = frame_distribution.most_common()[][]
authors_using_top_frame = (
author, frames author_frames.items()
frames[top_frame] >
)
coordination_score = authors_using_top_frame / (authors)
{
: party_id,
: ,
: total_docs,
: (frame_distribution),
: frame_distribution.most_common()[][],
: frame_distribution.most_common()[][] / total_docs,
: (coordination_score, ),
: (frame_distribution),
: ._classify_framing_strategy(
frame_distribution.most_common()[][] / total_docs,
coordination_score
)
}
() -> :
consistency > coordination > :
consistency > coordination > :
consistency < coordination < :
:
2. Media Bias Detection
Systematic Bias Analysis
Media bias affects public perception of political actors. The CIA platform systematically measures bias through coverage patterns and linguistic analysis.
@Service
public class MediaBiasDetector {
public MediaBiasProfile analyzeBias(
String mediaOutletId,
String politicalEntity,
LocalDate startDate,
LocalDate endDate
) {
String sql = """
WITH coverage_baseline AS (
-- Calculate expected coverage based on political relevance
SELECT
political_entity_id,
entity_type,
political_relevance_score,
CASE entity_type
WHEN 'PARTY_LEADER' THEN political_relevance_score * 3
WHEN 'MINISTER' THEN political_relevance_score * 2.5
WHEN 'COMMITTEE_CHAIR' THEN political_relevance_score * 1.5
ELSE political_relevance_score
END as expected_coverage_weight
FROM political_entity_relevance
),
actual_coverage AS (
-- Measure actual coverage from media outlet
SELECT
mc.political_entity_id,
COUNT(DISTINCT mc.article_id) as article_count,
AVG(mc.sentiment_score) as avg_sentiment,
SUM(mc.prominence_score) as total_prominence,
-- Linguistic indicators
AVG(LENGTH(mc.quote_text)) as avg_quote_length,
COUNT(CASE WHEN mc.is_primary_source THEN 1 END) as primary_source_count,
-- Framing indicators
mc.dominant_frame,
mc.frame_valence
FROM media_coverage mc
WHERE mc.media_outlet_id = :mediaOutletId
AND mc.coverage_date BETWEEN :startDate AND :endDate
GROUP BY mc.political_entity_id, mc.dominant_frame, mc.frame_valence
),
bias_analysis AS (
SELECT
cb.political_entity_id,
cb.entity_type,
cb.expected_coverage_weight,
COALESCE(ac.article_count, 0) as actual_articles,
COALESCE(ac.total_prominence, 0) as actual_prominence,
COALESCE(ac.avg_sentiment, 0) as sentiment_score,
-- Coverage bias: Actual vs. Expected
CASE
WHEN cb.expected_coverage_weight = 0 THEN 0
ELSE (COALESCE(ac.article_count, 0)::float /
NULLIF(cb.expected_coverage_weight, 0)) - 1
END as coverage_bias_ratio,
-- Sentiment bias: Deviation from neutral (0.5)
ABS(COALESCE(ac.avg_sentiment, 0.5) - 0.5) * 2 as sentiment_bias_magnitude,
CASE
WHEN COALESCE(ac.avg_sentiment, 0.5) > 0.5 THEN 'POSITIVE_BIAS'
WHEN COALESCE(ac.avg_sentiment, 0.5) < 0.5 THEN 'NEGATIVE_BIAS'
ELSE 'NEUTRAL'
END as sentiment_bias_direction
FROM coverage_baseline cb
LEFT JOIN actual_coverage ac ON cb.political_entity_id = ac.political_entity_id
WHERE cb.political_entity_id = :politicalEntity
)
SELECT
*,
-- Overall bias score (0-100, higher = more biased)
(
ABS(coverage_bias_ratio) * 40 +
sentiment_bias_magnitude * 60
) as overall_bias_score,
-- Bias classification
CASE
WHEN ABS(coverage_bias_ratio) > 0.5 AND sentiment_bias_magnitude > 0.3
THEN 'HIGH_BIAS'
WHEN ABS(coverage_bias_ratio) > 0.3 OR sentiment_bias_magnitude > 0.2
THEN 'MODERATE_BIAS'
ELSE 'LOW_BIAS'
END as bias_classification
FROM bias_analysis
""";
jdbcTemplate.queryForObject(sql, MediaBiasProfile.class,
Map.of(
, mediaOutletId,
, politicalEntity,
, startDate,
, endDate
));
}
}
Media Bias Taxonomy
| Bias Type | Detection Method | Risk Level | Strategic Implications |
|---|
| Coverage Bias | Frequency disparity > 50% | HIGH | Systematic visibility manipulation |
| Sentiment Bias | Sentiment deviation > 0.30 | HIGH | Reputation attack or promotion |
| Source Selection Bias | Quote frequency imbalance | MODERATE | Platform control |
| Framing Bias | Consistent negative/positive frames | HIGH | Narrative control |
| Omission Bias | Missing coverage of significant events | CRITICAL | Information suppression |
| Contextualization Bias | Selective context inclusion | MODERATE | Meaning manipulation |
3. Discourse Network Analysis
Parliamentary Discourse Patterns
Political discourse reveals coalition structures, ideological alignment, and influence networks through linguistic similarity and interaction patterns.
WITH politician_linguistic_profile AS (
SELECT
p.person_id,
p.first_name || ' ' || p.last_name as name,
p.party,
STRING_AGG(DISTINCT dc.keyword, ', ') as frequent_keywords,
STRING_AGG(DISTINCT dc.topic, ', ') as discussed_topics,
AVG(dc.complexity_score) as avg_linguistic_complexity,
AVG(dc.emotional_intensity) as avg_emotional_intensity,
COUNT(DISTINCT CASE WHEN d.document_type = 'motion' THEN d.document_id END) as motion_count,
COUNT(DISTINCT CASE WHEN d.document_type = 'interpellation' THEN d.document_id END) as question_count,
COUNT(DISTINCT CASE WHEN d.document_type = 'debate_speech' THEN d.document_id END) as speech_count
person p
document_person dp p.person_id dp.person_id
document d dp.document_id d.document_id
document_content dc d.document_id dc.document_id
d.created_date NOW()
p.person_id, p.first_name, p.last_name, p.party
),
discourse_similarity (
p1.person_id person_a,
p2.person_id person_b,
p1.name name_a,
p2.name name_b,
p1.party party_a,
p2.party party_b,
(
( k.keyword)::
(
( k1.keyword) ( k2.keyword) ( k.keyword),
)
(
(string_to_array(p1.frequent_keywords, )) keyword
(string_to_array(p2.frequent_keywords, )) keyword
) k,
( (string_to_array(p1.frequent_keywords, )) keyword) k1,
( (string_to_array(p2.frequent_keywords, )) keyword) k2
) keyword_similarity,
(
( t.topic)::
(
( t1.topic) ( t2.topic) ( t.topic),
)
(
(string_to_array(p1.discussed_topics, )) topic
(string_to_array(p2.discussed_topics, )) topic
) t,
( (string_to_array(p1.discussed_topics, )) topic) t1,
( (string_to_array(p2.discussed_topics, )) topic) t2
) topic_similarity,
(p1.avg_linguistic_complexity p2.avg_linguistic_complexity) style_similarity,
(p1.avg_emotional_intensity p2.avg_emotional_intensity) tone_similarity,
(
((
( k.keyword)::
(
( k1.keyword) ( k2.keyword) ( k.keyword),
)
(
(string_to_array(p1.frequent_keywords, )) keyword
(string_to_array(p2.frequent_keywords, )) keyword
) k,
( (string_to_array(p1.frequent_keywords, )) keyword) k1,
( (string_to_array(p2.frequent_keywords, )) keyword) k2
), )
((
( t.topic)::
(
( t1.topic) ( t2.topic) ( t.topic),
)
(
(string_to_array(p1.discussed_topics, )) topic
(string_to_array(p2.discussed_topics, )) topic
) t,
( (string_to_array(p1.discussed_topics, )) topic) t1,
( (string_to_array(p2.discussed_topics, )) topic) t2
), )
( (p1.avg_linguistic_complexity p2.avg_linguistic_complexity))
( (p1.avg_emotional_intensity p2.avg_emotional_intensity))
) discourse_similarity_score
politician_linguistic_profile p1
politician_linguistic_profile p2
p1.person_id p2.person_id
)
person_a,
person_b,
name_a,
name_b,
party_a,
party_b,
discourse_similarity_score,
discourse_similarity_score
discourse_similarity_score
discourse_similarity_score
relationship_type,
party_a party_b discourse_similarity_score
bridge_indicator
discourse_similarity
discourse_similarity_score
discourse_similarity_score ;
4. Coordinated Campaign Detection
Messaging Synchronization Analysis
Detecting coordinated messaging campaigns reveals strategic communication planning and potential influence operations.
from datetime import timedelta
import numpy as np
from scipy import stats
class CoordinatedCampaignDetector:
"""
Detects coordinated messaging campaigns through temporal and content analysis.
Indicators of coordination:
1. Temporal clustering (simultaneous messaging)
2. Message consistency (identical or near-identical content)
3. Hashtag/keyword coordination
4. Amplification patterns (coordinated sharing)
5. Strategic timing (event-based synchronization)
"""
def detect_campaign(
self,
party_id: str,
date_from: str,
date_to: str,
time_window_hours: int = 24
) -> Dict:
"""
Detects coordinated messaging campaigns within a party.
Returns campaign indicators and coordination metrics.
"""
query = """
SELECT
d.document_id,
d.created_date,
d.title,
d.subtitle,
dc.text_content,
dc.keywords,
p.person_id,
p.first_name || ' ' || p.last_name as author,
p.party
FROM document d
JOIN document_content dc ON d.document_id = dc.document_id
JOIN document_person dp ON d.document_id = dp.document_id
JOIN person p ON dp.person_id = p.person_id
WHERE p.party = %s
AND d.created_date BETWEEN %s AND %s
AND d.document_type IN ('motion', 'interpellation', 'press_release')
ORDER BY d.created_date
"""
documents = pd.read_sql(query, self.connection,
params=[party_id, date_from, date_to])
if len(documents) < 5:
return {'campaign_detected': False, 'reason': 'Insufficient data'}
documents[] = pd.to_datetime(documents[])
time_window = timedelta(hours=time_window_hours)
temporal_clusters = []
i, doc documents.iterrows():
cluster_docs = documents[
(documents[] >= doc[]) &
(documents[] <= doc[] + time_window)
]
(cluster_docs) >= :
temporal_clusters.append({
: doc[],
: (cluster_docs),
: cluster_docs[].tolist(),
: cluster_docs[].tolist()
})
coordinated_clusters = []
cluster temporal_clusters:
cluster_docs = documents[documents[].isin(cluster[])]
all_keywords = []
_, doc cluster_docs.iterrows():
doc[]:
all_keywords.extend([k.strip() k doc[].split()])
keyword_freq = Counter(all_keywords)
repeated_keywords = [k k, count keyword_freq.items() count >= ]
coordination_score = (repeated_keywords) / (((all_keywords)), )
coordination_score > :
coordinated_clusters.append({
**cluster,
: coordination_score,
: repeated_keywords[:]
})
expected_cluster_rate = (documents) / ((date_to - date_from).days * / time_window_hours)
observed_clusters = (temporal_clusters)
p_value = stats.poisson.sf(observed_clusters - , expected_cluster_rate)
campaign_detected = (
(coordinated_clusters) >=
p_value <
(c[] > c coordinated_clusters)
)
{
: campaign_detected,
: party_id,
: ,
: (documents),
: (temporal_clusters),
: (coordinated_clusters),
: p_value < ,
: (p_value, ),
: coordinated_clusters[:],
: ._assess_campaign_sophistication(
(coordinated_clusters),
([c[] c coordinated_clusters], default=)
)
}
() -> :
cluster_count >= max_coordination > :
cluster_count >= max_coordination > :
cluster_count >= :
:
5. Rhetoric-Action Gap Analysis
Credibility Assessment Through Alignment Analysis
Politicians often promise one thing and vote another. The CIA platform quantifies this gap to assess credibility.
@Component
public class RhetoricActionGapAnalyzer {
public RhetoricActionProfile analyzeGap(String politicianId) {
String sql = """
WITH politician_rhetoric AS (
-- Extract policy positions from documents
SELECT
dp.person_id,
d.issue_category,
d.issue_subcategory,
dc.stance, -- Extracted stance: 'support', 'oppose', 'neutral'
dc.stance_confidence_score,
d.created_date as rhetoric_date
FROM document d
JOIN document_content dc ON d.document_id = dc.document_id
JOIN document_person dp ON d.document_id = dp.document_id
WHERE dp.person_id = :politicianId
AND d.document_type IN ('motion', 'interpellation', 'debate_speech')
AND dc.stance IS NOT NULL
AND d.created_date >= NOW() - INTERVAL '4 years'
),
politician_actions AS (
-- Extract voting behavior on same issues
SELECT
v.person_id,
b.issue_category,
b.issue_subcategory,
v.vote, -- 'Ja' (yes), 'Nej' (no), 'Avstår' (abstain), 'Frånvarande' (absent)
b.vote_date as action_date,
b.vote_outcome
FROM vote v
JOIN ballot b ON v.ballot_id = b.ballot_id
WHERE v.person_id = :politicianId
AND b.vote_date >= NOW() - INTERVAL '4 years'
),
rhetoric_action_alignment AS (
SELECT
pr.person_id,
pr.issue_category,
pr.issue_subcategory,
pr.stance as stated_position,
pa.vote as actual_vote,
pr.rhetoric_date,
pa.action_date,
pa.vote_outcome,
-- Calculate alignment
CASE
WHEN pr.stance = 'support' AND pa.vote = 'Ja' THEN TRUE
WHEN pr.stance = 'oppose' AND pa.vote = 'Nej' THEN TRUE
WHEN pr.stance = 'neutral' AND pa.vote = 'Avstår' THEN TRUE
ELSE FALSE
END as is_aligned,
-- Gap severity
CASE
WHEN pr.stance = 'support' AND pa.vote = 'Nej' THEN 'DIRECT_CONTRADICTION'
WHEN pr.stance = 'oppose' AND pa.vote = 'Ja' THEN 'DIRECT_CONTRADICTION'
WHEN pr.stance IN ('support', 'oppose') AND pa.vote = 'Frånvarande' THEN 'STRATEGIC_ABSENCE'
WHEN pr.stance IN ('support', 'oppose') AND pa.vote = 'Avstår' THEN 'STRATEGIC_ABSTENTION'
ELSE 'ALIGNED'
END as gap_type
FROM politician_rhetoric pr
JOIN politician_actions pa
ON pr.person_id = pa.person_id
AND pr.issue_category = pa.issue_category
AND pr.issue_subcategory = pa.issue_subcategory
AND pa.action_date >= pr.rhetoric_date -- Vote after stated position
AND pa.action_date <= pr.rhetoric_date + INTERVAL '1 year' -- Within 1 year
)
SELECT
p.person_id,
p.first_name || ' ' || p.last_name as name,
p.party,
COUNT(*) as comparable_positions,
COUNT(CASE WHEN is_aligned THEN 1 END) as aligned_count,
COUNT(CASE WHEN NOT is_aligned THEN 1 END) as misaligned_count,
-- Alignment rate
COUNT(CASE WHEN is_aligned THEN 1 END)::float /
NULLIF(COUNT(*), 0) as alignment_rate,
-- Gap breakdown
COUNT(CASE WHEN gap_type = 'DIRECT_CONTRADICTION' THEN 1 END) as contradiction_count,
COUNT(CASE WHEN gap_type = 'STRATEGIC_ABSENCE' THEN 1 END) as strategic_absence_count,
COUNT(CASE WHEN gap_type = 'STRATEGIC_ABSTENTION' THEN 1 END) as strategic_abstention_count,
-- Credibility score (0-100, higher = more credible)
ROUND(
(COUNT(CASE WHEN is_aligned THEN 1 END)::float / NULLIF(COUNT(*), 0)) * 100,
2
) as credibility_score,
-- Classification
CASE
WHEN COUNT(CASE WHEN is_aligned THEN 1 END)::float / NULLIF(COUNT(*), 0) >= 0.85
THEN 'HIGHLY_CREDIBLE'
WHEN COUNT(CASE WHEN is_aligned THEN 1 END)::float / NULLIF(COUNT(*), 0) >= 0.70
THEN 'MODERATELY_CREDIBLE'
WHEN COUNT(CASE WHEN is_aligned THEN 1 END)::float / NULLIF(COUNT(*), 0) >= 0.50
THEN 'LOW_CREDIBILITY'
ELSE 'UNRELIABLE'
END as credibility_classification
FROM view_riksdagen_politician p
JOIN rhetoric_action_alignment raa ON p.person_id = raa.person_id
WHERE p.person_id = :politicianId
GROUP BY p.person_id, p.first_name, p.last_name, p.party
""";
jdbcTemplate.queryForObject(sql, RhetoricActionProfile.class,
Map.of(, politicianId));
}
}
Credibility Assessment Matrix
| Alignment Rate | Contradiction Count | Credibility Level | Strategic Assessment |
|---|
| ≥ 85% | < 5 | HIGHLY_CREDIBLE | Reliable commitments, low strategic ambiguity |
| 70-84% | 5-10 | MODERATELY_CREDIBLE | Generally trustworthy, occasional flexibility |
| 50-69% | 11-20 | LOW_CREDIBILITY | Significant rhetoric-action gaps, strategic positioning |
| < 50% | > 20 | UNRELIABLE | Systematic contradictions, populist rhetoric |
ISMS Compliance Mapping
ISO 27001:2022 Controls
| Control | Communication Analysis Application |
|---|
| A.5.7 - Threat intelligence | Monitor information warfare campaigns and disinformation patterns |
| A.5.9 - Inventory of information and other associated assets | Catalog media sources and communication channels |
| A.8.16 - Monitoring activities | Continuous monitoring of narrative framing and messaging campaigns |
NIST Cybersecurity Framework 2.0
| Function | Strategic Communication Integration |
|---|
| IDENTIFY (ID.AM) | Identify communication assets and influence networks |
| PROTECT (PR.AT) | Awareness training for disinformation detection |
| DETECT (DE.CM) | Detect coordinated campaigns and influence operations |
| RESPOND (RS.CO) | Counter-narrative response strategies |
CIS Controls v8
| Control | Application |
|---|
| CIS Control 6 - Access Control Management | Restrict access to communication intelligence data |
| CIS Control 13 - Network Monitoring and Defense | Monitor information ecosystem for threats |
Hack23 ISMS Policy References
This skill implements requirements from:
References
Communication Theory Literature
- Entman, R. M. (1993). "Framing: Toward Clarification of a Fractured Paradigm." Journal of Communication, 43(4), 51-58.
- Scheufele, D. A., & Tewksbury, D. (2007). "Framing, Agenda Setting, and Priming: The Evolution of Three Media Effects Models." Journal of Communication, 57(1), 9-20.
- Bennett, W. L., & Livingston, S. (2018). "The Disinformation Order: Disruptive Communication and the Decline of Democratic Institutions." European Journal of Communication, 33(2), 122-139.
- Lakoff, G. (2004). Don't Think of an Elephant!: Know Your Values and Frame the Debate. Chelsea Green Publishing.
Database Intelligence Sources
