Local Signal is an MJRC initiative and a central pillar of its 2026–2028 strategy. Its purpose is to chart how people actually get informed where they live, and to situate journalism within that broader information landscape. Rather than beginning and ending with a directory of media outlets, Local Signal adopts an information ecosystem perspective. It maps formal newsrooms alongside public institutions, civic and cultural organizations, political actors, sports clubs, creators and influencers, community events, and commercial actors that shape civic attention.
The project’s ambition is practical: to create a living, portable evidence base that communities, newsrooms, researchers, and funders can use to identify flows of trustworthy local information.
Purpose
In every locality, information flows through a network of nodes that are only partly journalistic. City halls, police departments, health systems, schools and universities, transport authorities, sports organizations, churches, festivals, and local creators command significant audience attention. Some fill gaps that journalism leaves; others depend on journalism to make sense of public life.
Local Signal documents this field from an inclusive and socially broader perspective, moving beyond the narrow media-centric lens. It collects verifiable, time-stamped data and pairs it with on-the-ground knowledge to show who reaches whom, through which channels, and with what degree of authority or influence. The goal is not to grade or police actors, but to make the system legible: where journalism is central, where it is peripheral, and where interventions could matter most.
What Local Signal Produces
At the core of the project lies a master database built on a portable schema. Each locality receives a registry of entities—media, institutions, civic and cultural bodies, political actors, sports organizations, creators/influencers, and key companies. For each entity, two types of data are collected:
Online. Web presence and social accounts, dated audience metrics (followers, subscribers, engagement where available), and financial and ownership data (company or charity numbers, regulators, and links to filings). Relationships among entities (ownership, sponsorship, collaboration, regulatory ties) are documented to expose structure and dependence.
Offline. Unlike most mappings that stop at newsroom lists, Local Signal extends outward to capture the hybrid online–offline nature of local information. Fieldwork with local experts—journalists, librarians, community organizers, venue managers, and others—surfaces channels that online data misses: parish magazines, WhatsApp and neighbourhood groups, noticeboards, flyering, and recurring civic events. These insights also contextualise online reach, showing which accounts truly set the agenda, which outlets punch above their weight, and which institutions or creators act as de facto news sources.
Because local information changes quickly, the database includes a resampling layer, with sensible cadences for refreshing metrics (depending on resources, local expertise available, etc.). Each metric is date-stamped and sourced to ensure auditability. From this dataset, Local Signal produces local dossiers—narrative analyses that interpret the ecosystem’s shape, highlight dominant nodes and missing voices, and identify risks and opportunities. These dossiers update as new snapshots are added, enabling longitudinal views.
Equally important, Local Signal is auditable and repeatable: every claim traces to a public source, every metric is dated, and the schema is consistent across geographies, making comparisons meaningful and updates straightforward.
Definitions and Scope
Local Signal applies a two-layered definition of “local” to ensure findings travel well across contexts:
Metropolitan area (primary lens): A functional urban region, combining the city core and its commuting zone. This reflects real information spillovers and audience overlap, capturing how people encounter and share content beyond municipal boundaries.
County/province level (secondary lens): The administrative tier used for public spending, regulation, elections, health, and policing. Many influential entities operate at this scale, and key datasets are published here.
Analyses specify which lens is applied in each case. Where both are relevant, Local Signal builds nested views (metro within county) to preserve comparability.
Methodology: A Layered Ecosystem Model
Local Signal organizes each locality into layers, treating the ecosystem as a set of nodes (entities) and edges (relationships):
Journalism & Media – newspapers, digital publishers, radio, local TV, hyperlocals, student and community outlets, newsletters, podcasts.
Public Institutions & Services – city and county government, health systems, police and fire, schools and universities, transport, utilities, ports, airports, regulators.
Civic & Culture – museums, libraries, festivals, venues, faith communities, voluntary associations that convene people and circulate information.
Politics & Democracy – elected officials, candidates, party associations, election administrators, watchdogs.
Sport & Identity – professional and grassroots clubs, supporter groups, fan media—often among the largest local attention drivers.
Creators & Influencers – food, lifestyle, parenting, history, memes, neighbourhood pages, administrators of messaging groups.
Commercial Actors – major employers and sponsors, advertisers, media buyers—because money flows shape information flows.
For each node, Local Signal logs web and social accounts (Facebook, Instagram, X, YouTube, TikTok, LinkedIn, newsletters), offline presence (broadcast footprints, print distribution, noticeboards, recurring events), and relationships (ownership, sponsorships, content partnerships, regulatory oversight).
Data Collection
Local Signal blends structured desk research with local expertise:
Desk research. Public websites and platform pages; company and charity registers; broadcast and press regulators; electoral and procurement data; open statistics. Every metric includes an as-of date and a source URL.
Local expert verification. Journalists, community leaders, cultural managers validate the map, add overlooked nodes, and flag channels that matter in daily life but leave few digital traces.
Resampling. Default cadences keep snapshots fresh while respecting platform volatility and local rhythms. Frequencies tighten during elections, crises, or major events.
Quality control. Discrepancies (e.g. follower vs. like counts) are tracked, change logs maintained, and edge cases annotated (brand aggregators, franchise accounts, cross-boundary entities).
Metrics and Schema
The schema is compact enough for field use yet rich enough to support comparative analysis, including:
Entities. Stable ID, name, type/subtype, legal form, company/charity numbers, regulators, address, website, notes; Accounts. Platform, handle, URL, verification status, category tags; Metrics Snapshots. Followers, subscribers, likes (or other visible metrics), numeric value, as-of date, source URL; Finance. Year-end, revenue/profit (if disclosed), employees, scope (entity vs. parent), filings URL; Ownership. Parent–child relationships, percentages (if known), source URL; Links. Sponsorships, collaborations, regulatory and funding ties, other relationships; Resampling. Last checked, frequency (days), next due date.
Collaboration and Participation
Local Signal is designed as a living research base where universities, local newsrooms, civic-tech groups, community media, research labs, and funders are invited to contribute and use the framework. Partners can adopt the schema, add entities, validate observations, or co-author analyses.
We particularly seek collaboration with groups already collecting local statistics, tracking local misinformation, or developing civic tools—so that definitions align and duplication is avoided. Participation can range from contributing data, to validating offline channels, to co-publishing analyses.
Ethics, Transparency and Limits
Local Signal only documents public activity and public entities. It does not scrape behind logins or collect personal data beyond what organizations and public figures disclose. Every figure links to a source and an as-of date. Ambiguities are flagged, and qualitative influences (e.g. noticeboards) are attributed and contextualized. The goal is not surveillance but public accountability and clarity about how local information travels.
How Local Signal Can Be Used
The database created through Local Signal is housed in the Media and Journalism Exchange, a curated environment where all additions and contributions are verified to ensure quality and reliability. Within this space, members can freely access, use, and adapt the data for their own projects while benefiting from the assurance that every entry has been checked for accuracy and traceability.
The resource serves multiple purposes ranging from ecosystem health checks (identify missing beats, over-reliance on single actors, or gaps in plurality within a local information space) to investment targeting (guide funders and newsrooms toward areas where support could have the greatest civic impact, e.g., health, housing, council scrutiny, youth audiences) to policy and governance (ground debates about news deserts, misinformation, and civic capacity in verifiable, regularly updated evidence) and newsroom strategy (show where audiences already are, when to partner or compete, and how to calibrate coverage and distribution).
Beyond its immediate use by local partners, the datasets are used regularly to generate comparative research papers on local information flows, enabling cross-place analysis while preserving the nuance of each locality.
To explore the datasets, contribute your own observations, or get involved in shaping the project, join the Media and Journalism Exchange—a private, trusted space where researchers, journalists, and curious minds come together. It is a safe community designed for collaboration and exchange, where people interested in media and its social impact can both contribute and learn.
Photo by Wonderlane on Unsplash
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