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Panama Papers: Companies, Tax haven & Newsroom logistics
The Panama Papers (Spanish: Papeles de Panamá) are 11.5 million leaked documents (2.6 terabytes of data) published from April 3, 2016. The papers detail financial and attorney–client information for more than 214,488 offshore entities. These documents, some dating back to the 1970s, were created by, and taken from, the former Panamanian offshore law firm…
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Panama Papers topic overview
The analysis highlights Companies, Tax haven and Newsroom logistics as prominent areas in the source structure around Panama Papers.
Related topics
276
Source areas
13
Connected nodes
290
Extracted relationships
78
Concept neighborhoods
35
Bridge connections
290
What this topic covers Research coverage
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
People named · 66 topics
Overview · 50 topics
Tax haven · 32 topics
Newsroom logistics · 26 topics
International Federation of Association Football (FIFA) investigation · 21 topics
Data security · 15 topics
Disclosures · 14 topics
Responses by Mossack Fonseca · 13 topics
Illegal activities · 12 topics
Leak and leak journalism · 9 topics
Clients of Mossack Fonseca · 8 topics
Responses in Panama · 8 topics
Allegations, reactions, and investigations · 2 topics
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
Key facts & relationships
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Explore all related topics Closing gaps
A structured outline of related entities, concepts and subtopics. Open any item to build a new map centered on it.
Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
How Panama Papers connects Entity context
Entity context groups extracted connections by relationship type. Counts show recurrence in the analyzed source; the examples are research signals, not claims about importance.
The extracted context around Panama Papers shows recurring relationship patterns in the source. For example, Panama Papers → Africa, African Network, ANCIR, Armando, Centers, Corruption Reporting ProjectPanama Papers, Costa RicaPanama Papers Portal, Financial Times, Foreign, France, Germany, Iceland, Inkyfada, International Consortium, Investigative Journalists, Investigative Reporting, June, Le Desk, Le Monde, Morocco Another extracted example is Panama Papers → Attorney General's, Bella Vista, Financial Analysis Unit, Financial Subjects, Following, Mossack Fonseca, On April, Organized Crime, Panama, Panama City, Panamanian, Regulation, Second Specialized Prosecutor, Some, Supervision, The, The Municipality, UAF, Violation. Use these groups to spot repeated connection types before inspecting the individual relationships.
Panama Papers
Top relations
related to External links· 39
Panama Papers → Africa, African Network, ANCIR, Armando, Centers, Corruption Reporting ProjectPanama Papers, Costa RicaPanama Papers Portal, Financial Times, Foreign, France, Germany, Iceland, Inkyfada, International Consortium, Investigative Journalists, Investigative Reporting, June, Le Desk, Le Monde, Morocco
related to Law enforcement· 19
Panama Papers → Attorney General's, Bella Vista, Financial Analysis Unit, Financial Subjects, Following, Mossack Fonseca, On April, Organized Crime, Panama, Panama City, Panamanian, Regulation, Second Specialized Prosecutor, Some, Supervision, The, The Municipality, UAF, Violation
related to Further reading· 10
Panama Papers → Bastian Obermayer, Breaking, Frederik Obermaier, How, ISBN, Oneworld Publications, Powerful Hide Their Money, Rich, Story, The Panama Papers
related to Leak and leak journalism· 10
Panama Papers → Edward Snowden, Gerard Ryle, ICIJ, Micah White, Occupy, The, The ICIJ, This, Twitter, WikiLeaks
Important terminology
Frequent words and multi-word phrases across the lead, headings, infobox and body. Useful for terminology coverage.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
Panama Papers relationships Subject–Predicate–Object triples
Relationships are represented as Subject → Predicate → Object triples. Confidence describes extraction strength, not the real-world importance of a relationship; lower-confidence rows may be contextual rather than explicit.
TTTA extracted 78 structured relationships around Panama Papers. Examples in this analysis include Panama Papers → related to External links → Searchable and Panama Papers → related to External links → Papers Portal. The table shows each extracted connection, where it came from and its confidence.
Subject
Predicate
Object
Confidence
Src
Panama Papers
related to External links
Searchable
0.60
section
Panama Papers
related to External links
Papers Portal
0.60
section
Panama Papers
related to External links
International Consortium
0.60
section
Panama Papers
related to External links
Investigative Journalists
0.60
section
Panama Papers
related to External links
US
0.60
section
Panama Papers
related to External links
Panama Papers Portal
0.60
section
Panama Papers
related to External links
Süddeutsche Zeitung
0.60
section
Panama Papers
related to External links
Germany
0.60
section
Panama Papers
related to External links
The Guardian
0.60
section
Panama Papers
related to External links
United Kingdom
0.60
section
Panama Papers
related to External links
Financial Times
0.60
section
Panama Papers
related to External links
Le Monde
0.60
section
No triples extracted yet.
Related concept clusters Concept neighborhoods
Concept neighborhoods group terms that sit near one another in the extracted graph. They are useful for discovering adjacent language and context, but proximity does not by itself imply a factual relationship.
The concept neighborhoods around Panama Papers bring nearby vocabulary together. In this analysis, examples include Papers, Said and Mossack. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
Panama Papers
Papers
Said
Mossack
April
Would
Fonseca
Information
Icij
Tax
Documents
Journalists
President
panama papers
Papers
Said
Mossack
April
Would
President
Fonseca
Information
Icij
Tax
Documents
Journalists
leaked
Documents
Data
Icij
Also
May
Shell
Papers
Financial
Information
Million
Fonseca
Business
offshore
Legal
Companies
Law
Shell
International
Reporters
Tax
Business
May
Icij
Fonseca
Mossack
mossack fonseca
Mossack
Clients
Companies
Business
Shell
Panama
Company
Tax
Offshore
Legal
Law
Said
international sanctions
Shell
Tax
Offshore
Journalists
Government
Law
Also
Reporters
Company
Panamanian
Us
Papers
international consortium of investigative journalists
Shell
Tax
Offshore
Journalists
Government
Papers
Law
Icij
Also
Reporters
Company
Legal
data journalism
Leaked
Documents
Business
May
Icij
April
Also
Companies
Reporters
Legal
Money
Financial
Connections between topic areas Semantic bridges
Semantic bridges connect parts of the graph that would otherwise be more separate. A bridge is a structural signal in this analysis, not a claim that the two topic areas are equivalent.
For Panama Papers, one of the stronger structural bridges in this analysis connects Panama Papers with People named. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
Min side: 3
Panama Papers — People named· splits 224 ⟂ 67Panama Papers — Overview· splits 240 ⟂ 51Panama Papers — Tax haven· splits 258 ⟂ 33Panama Papers — Newsroom logistics· splits 263 ⟂ 28Panama Papers — International Federation of Association Football (FIFA) investigation· splits 269 ⟂ 22Panama Papers — Data security· splits 275 ⟂ 16Panama Papers — Disclosures· splits 276 ⟂ 15Panama Papers — Responses by Mossack Fonseca· splits 277 ⟂ 14Panama Papers — Illegal activities· splits 278 ⟂ 13Panama Papers — Leak and leak journalism· splits 281 ⟂ 10Panama Papers — Clients of Mossack Fonseca· splits 282 ⟂ 9Panama Papers — Responses in Panama· splits 282 ⟂ 9Panama Papers — Allegations, reactions, and investigations· splits 288 ⟂ 3
Map overview Semantic statistics
Number of nodes, edges, triples, density and central hubs. Use it to gauge the size and connectivity of the map.
Panama Papers
Nodes291
Edges290
Triples78
Avg. degree1.99
Density0.006873
Components1
Source & methodology
TTTA analyzes the structure around Panama Papers to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Companies, Tax haven & Newsroom logistics, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
