Media Influence Matrix: Methodology 2025+

A revised and expanded framework grounded in the original MIM methodology and redesigned to map the full contemporary information sphere.

Download the Methodology SummaryDownload the Data Collection Templates

Introduction: Why the Methodology Has Changed

The Media Influence Matrix (MIM) was launched in 2017 to examine the forces shaping journalism and the wider information environment. The project originally relied on narrative country reports based on three core areas: regulation, funding, and technology.

The information system of the 2020s, however, is radically different. News and journalism now operate within a much broader and more complex information sphere, influenced not only by media institutions but also by:

  • Artificial intelligence companies developing foundation models and generative systems
  • Global cloud and platform corporations managing data flows and algorithmic distribution
  • Quantum internet research and secure communication infrastructures
  • Telecommunications operators and cross-sector conglomerates
  • Data brokers, advertising exchanges, and infrastructure providers
  • Transnational regulatory bodies and emerging AI/algorithm oversight agencies

To reflect these changes, the project has transitioned from static, text-based reporting to a database-driven methodology. This new framework:

  • Maps the full spectrum of actors shaping information flows
  • Tracks regulatory, economic, technological, and infrastructural power
  • Enables standardized, comparable country datasets
  • Supports time-series analysis of trends and influence
  • Integrates seamlessly with the Global Media Finances Map (GMFM) project
  • Allows future automation and interactive visualizations

The New Media Influence Matrix

Rooted in the original MIM methodology (adopted in 2017), this revised framework has been adapted to reflect today’s broader and more complex information environment. It organizes the work into three pillars and applies standardized entities, variables, and data models so that country profiles function as dynamic databases rather than static publications.

Research Overview

A map of the areas and topics covered by the Media Influence Matrix project

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Regulation & Policy Influencers

Maps regulators, decision-makers, and influence actors who shape laws, oversight, and public communication rules.

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Provenance & Funding Mapping

Covers ownership, financial flows, funders, and the economic structures that shape the information sphere.

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Technology, AI & Infrastructure

Examines platforms, telecoms, AI firms, data flows, and the technological systems that enable or constrain information access.

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1. Regulation and Policy Influencers

This pillar captures state institutions, para-state bodies, and other actors involved in governing the information environment, along with their mandates, powers, resources, and influence dynamics.

1.1 Regulatory Authorities

This category includes all institutions with authority over any segment of the information sphere. Types of regulators include, but are not limited to:

  • Broadcast and spectrum authorities
  • Print and press regulators or self-regulatory bodies
  • Online content and digital services regulators
  • Data protection and privacy authorities
  • Competition, antitrust, and market oversight bodies
  • Telecommunications and network regulators
  • Cybersecurity and digital-security agencies
  • Electoral commissions with responsibilities over information environments
  • Advertising oversight and consumer-protection bodies
  • AI and algorithm governance bodies
  • Any other public authority with relevant jurisdiction

Data collected for each regulator:

  • Legal mandate and regulatory domain
  • Key tasks and powers (e.g., sanctions, licensing, content removal, algorithmic auditing)
  • Jurisdictional boundaries
  • Governance structure and board composition
  • Appointment and dismissal rules
  • Five-year budget and sources of funding
  • Transparency requirements
  • Safeguards for independence
  • Access-to-data obligations
  • Documented controversies or examples of political influence
  • Significant enforcement actions or decisions

1.2 Policy Decision-Makers

This section profiles individuals who directly or indirectly shape media, technology, and information policies. These may include:

  • Ministers and deputy ministers
  • Chairs and senior officials of regulators
  • Parliamentary committee leaders
  • Senior civil servants
  • Presidential or executive advisers

Data collected:

  • Current role and institution
  • Career background and past affiliations
  • Political ties
  • Influence areas (e.g., AI regulation, spectrum management, media funding)
  • Documented examples of policy impact
  • Links to industry, political, or foreign actors

1.3 Internal and External Influencers

Influencers include both domestic and foreign actors that shape regulatory or policy outcomes.

Internal influencers:

  • Industry players (media companies, telecoms, tech companies, advertising agencies, large corporations unrelated to media but active in lobbying)
  • Civil society organizations (NGOs, academic institutions, professional associations, advocacy groups)
  • Influential individuals (journalists, bloggers, celebrities, activists, public intellectuals)

External influencers:

  • Intergovernmental organizations
  • Foreign governments
  • International donors and development agencies
  • Transnational regulatory bodies
  • Foreign corporations with local lobbying activity

Data collected:

  • Actor profile
  • Funding sources
  • Areas of influence
  • Methods of influence (advocacy, lobbying, litigation, political alliances, campaigns)
  • Documented influence events or interventions

1.4 Decision-Making Transparency

The methodology assesses the transparency and openness of regulatory and policy processes.

Tracked indicators include:

  • Publication of decisions, agendas, and meeting minutes
  • Existence and quality of public consultation mechanisms
  • Availability of regulatory filings and datasets
  • Laws governing transparency
  • Patterns of opaque or discretionary decision-making
  • Accessibility of information related to sanctions, budgets, and procurements

2. Provenance and Funding

This pillar maps the ownership, financial structures, market power, and funding flows that shape the information sphere. It integrates all economic and ownership dimensions previously covered in the project and aligns with the Global Media Finances Map.

2.1 Media and Information Companies

This category includes all organizations producing or distributing news or public information, including broadcasters, publishers, online outlets, news agencies, fact-checking organizations, and hybrid digital actors.

Data collected:

  • Ownership structure (direct and beneficial owners)
  • Shareholding chains and cross-sector holdings
  • Revenue structure (advertising, subscriptions, philanthropy, government funding, platform subsidies)
  • Profit and loss (five-year series)
  • Audience reach and market share (five-year series)
  • Editorial orientation or positioning
  • History of political or ideological alignment
  • AI integration in content production
  • Corporate links to other strategic sectors (energy, real estate, finance, defense, etc.)

2.2 Owners and Beneficial Controllers

This component maps individuals and institutions that exercise ownership or control over information actors.

Data collected:

  • Beneficial owners and controlling shareholders
  • Wealth origin and business background
  • Political affiliations or activities
  • Cross-sector and cross-border holdings
  • Involvement in lobbying, political financing, or public contracting
  • Documented controversies, sanctions, or legal issues

2.3 Funders of the Information Sphere

Funding actors fall into two broad groups:

Non-governmental funders:

  • Advertisers
  • Corporations
  • Philanthropic foundations
  • Political parties
  • Individual donors

Governmental funders:

  • Public service media financing
  • State advertising
  • Subsidies and grants
  • Strategic communication budgets
  • Emergency or crisis funding programs

Data collected:

  • Total annual spending (five-year series)
  • Allocation criteria
  • Top recipients
  • Funding mechanisms
  • Patterns of favoritism or political use
  • Specific links to editorial outcomes or agenda setting

3. Technology, AI, and Infrastructure

This pillar maps the technological ecosystem that structures information flows, from telecommunications infrastructure to artificial intelligence and platform governance.

3.1 Technology and Infrastructure Actors

This category includes:

  • AI companies (LLM developers, generative AI platforms, detection tools)
  • Platform companies (social networks, search engines, messaging apps, content platforms)
  • Quantum internet and secure communications providers
  • Telecommunications operators
  • Cloud service providers and hyperscalers
  • Data brokers and advertising-technology companies
  • Cybersecurity companies
  • Device ecosystem operators and operating system developers
  • Content delivery networks

Data collected:

  • Services, products, and ecosystem role
  • AI capabilities and data dependencies
  • Market penetration and user numbers
  • Local operations (data centers, offices, staff)
  • Ownership and funding
  • Regulatory compliance history
  • Involvement in public procurement or national security
  • Impact on the visibility or monetization of news content

3.2 Algorithms, Data Flows, and Distribution

This section examines how information is ranked, recommended, filtered, transmitted, and monetized.

Data collected:

  • Main recommendation and ranking systems relevant to the country
  • Cross-border data flows
  • Interoperability conditions
  • Access to platform APIs and distribution tools
  • Visibility changes affecting news outlets
  • Data localization or transfer requirements
  • Notable instances of algorithmic suppression or amplification

3.3 Technology–Government Relations

This section tracks the relationship between technology companies and state institutions.

Data collected:

  • Regulatory compliance and enforcement
  • Content removal, data access, and surveillance requests
  • Political lobbying and influence activities
  • Public procurement and government partnerships
  • Ownership or financial ties
  • AI regulation, digital services compliance, and transparency practices

3.4 Technology–Journalism Relations

This section covers how platforms and technology systems affect journalism.

Data collected:

  • Share of traffic coming from platform referrals
  • Dependencies on platforms for monetization and distribution
  • Effects of algorithmic changes on visibility and revenue
  • Subsidies, grants, or programs supporting news outlets
  • Ownership links between tech and media entities
  • AI adoption in newsrooms
  • Platform rules affecting media content (e.g., zero-rating, content labeling, API limitations)

Data Model and Database Structure

The methodology uses a standardized relational data model that transforms country profiles into comprehensive datasets. The core entities include:

  • Regulator
  • Person
  • Company
  • Media Outlet
  • Tech Entity
  • Funder
  • Ownership Link
  • Funding Flow
  • Regulatory Action
  • Influence Link
  • Distribution Dependency

Each entity includes structured attributes, time-series variables where relevant, and standardized taxonomies that enable cross-country comparison and integration with the Global Media Finances Map.

Data Collection Templates

Below are the templates used by research teams. These can be exported to spreadsheets without modification.

Template: Regulators

  • Regulator name
  • Domain (broadcast, print, online, data protection, competition, telecom, cybersecurity, AI, etc.)
  • Legal mandate
  • Key tasks
  • Powers
  • Jurisdiction
  • Board members
  • Appointment rules
  • Annual budget (five-year series)
  • Funding source
  • Transparency mechanisms
  • Independence indicators
  • Recent decisions
  • Notable controversies

Template: Policy Actors and Influencers

  • Actor name
  • Actor type (individual, NGO, company, IGO)
  • Sector
  • Funding sources
  • Influence methods
  • Areas of policy engagement
  • Documented impact cases

Template: Media and Information Companies

  • Company name
  • Sector
  • Direct ownership
  • Beneficial ownership
  • Revenue (five-year series)
  • Profit (five-year series)
  • Market share
  • Audience metrics (five-year series)
  • Editorial orientation
  • Cross-sector holdings
  • AI integration

Template: Funding Flows

  • Funder name
  • Type (advertiser, philanthropy, government, political, etc.)
  • Total annual spending (five-year series)
  • Recipients
  • Amounts
  • Allocation criteria
  • Notes

Template: Technology Entities

  • Name
  • Type (AI, platform, telecom, cloud, quantum, data broker, CDN, OS developer)
  • Services provided
  • AI capabilities
  • Market penetration
  • Local operations
  • Ownership
  • Regulatory compliance
  • Government relations
  • Journalism relations

Template: Distribution Dependencies

  • Media outlet
  • Platform or technology entity
  • Type of dependency (traffic, monetization, distribution, visibility)
  • Percentage of traffic or reach
  • Notes

Versioning and Citation

Version: MIM Methodology 2025+, v1.0
Publisher: Media and Journalism Research Center (MJRC)

Suggested citation:
Media and Journalism Research Center (2025). Media Influence Matrix – Methodology 2025+: AI-Era, Database-Driven Framework. MJRC.