For more than a decade, social media has been a central channel for government communication. Local agencies have relied on platforms such as Facebook, X and Instagram to share timely updates, reach residents quickly and provide visibility during emergencies or fast-moving situations. For many communications teams, social media became the most immediate and flexible way to inform the public.
Over the past year, however, a noticeable shift has occurred. Across cities, counties and special districts, communications professionals have increasingly raised concerns — not about whether social media should be used, but about whether it is still functioning as expected. Agencies are reporting rising levels of misinformation in comment threads, growing workloads tied to moderation and correction, and confusion when older posts resurface without context. These challenges are not isolated incidents. They point to a deeper structural problem emerging in the public information environment.
That problem is this: Social media platforms are increasingly being treated as authoritative inputs by artificial intelligence systems, even though they were never designed for that role.
Why This Issue Is Surfacing Now
The renewed attention on social media breakdowns is not driven by a single policy change or platform decision. Instead, it reflects a broader transformation in how people access and interpret information.
Members of the public are turning more frequently to AI-powered tools — search summaries, digital assistants and generative chat systems — to understand government actions, policies and local events. These tools synthesize information across many sources and present it as a single, coherent answer. In practice, that means government communications are no longer consumed only by human readers navigating websites or social feeds. They are increasingly processed, summarized and re-expressed by automated systems.
For many agencies, this shift becomes visible only after the fact. A communications team may encounter an AI-generated summary circulating among residents, media or internal stakeholders — sometimes accurate, sometimes incomplete, and sometimes outdated. In those moments, the limitations of social media as a publishing environment become harder to ignore.
What Social Media Platforms Are Designed to Do
Social media platforms are built for engagement. Their design prioritizes visibility, interaction and participation. Content is ranked algorithmically based on reactions, comments and sharing behavior. Posts are intended to spark conversation, not to serve as final or canonical statements.
These characteristics are not flaws. They are intentional features that make social platforms effective spaces for dialogue and community interaction.
At the same time, social media platforms generally lack features that support authoritative publishing. There is no native concept of a “final” or “superseded” statement. Versioning and update signaling are limited or informal. Official posts, replies, and public comments often appear visually equivalent. Content can resurface long after publication without clear contextual markers.
For human readers, these limitations are often manageable. People infer context, recognize tone, and follow timelines. They understand that a comment is not the same as an official update, and that older posts may no longer reflect current conditions.
For AI systems, that distinction is far less clear.
How AI Systems Read Government Information
AI systems do not read information the way humans do. They do not understand institutional hierarchy, intent or conversational nuance. Instead, they rely on patterns, structure, repetition and inferred signals of authority drawn from available data.
When AI systems process social media content, several challenges arise. Official statements and public comments may be treated as part of the same informational layer. Replies or follow-up posts can be interpreted as clarifications or continuations, even when they are not. Older posts may be summarized as current if no explicit update signal exists. Engagement metrics can unintentionally suggest importance or authority.
Even well-moderated comment sections can introduce ambiguity when AI systems attempt to determine “what the agency said” versus “how the public responded.”
This is not a failure of artificial intelligence. It is a consequence of applying automated interpretation to environments that were designed for conversation rather than recordkeeping. Social media was never intended to function as a canonical source of record, particularly for systems that must interpret information at scale.
How Disinformation Enters the System
In this context, disinformation does not always originate from deliberate manipulation. More often, it emerges from structural conditions that distort meaning over time.
Common examples include screenshots of social media posts shared without subsequent updates or corrections, partial excerpts circulating outside their original thread or timeline, comments or replies interpreted as official clarifications, and archived posts resurfacing during new events with similar characteristics.
When AI systems ingest these fragmented signals, they may generate summaries that are technically faithful to the available content but misaligned with an agency’s current position. The result is not intentional misinformation, but misinterpretation amplified by automation.
For communications teams, this creates a reactive cycle. Staff are no longer responding solely to public questions or feedback; they are increasingly tasked with correcting downstream interpretations produced by systems outside their direct control.
Engagement Versus Authoritative Publication
In response to these pressures, some agencies have begun to draw a clearer distinction between two different communication functions.
The first is engagement. Engagement platforms are designed for conversation, feedback, visibility and community interaction. Social media excels in this role and remains essential for reaching residents quickly and interactively.
The second is authoritative publication. Authoritative systems are designed to provide finalized, attributable and up-to-date records of official information. They emphasize clarity, provenance and recency rather than interaction.
Historically, these functions were often blended. A social media post could serve as both an engagement tool and a de facto record of an agency’s position. That approach worked when human readers were the primary audience and context was interpreted informally.
AI has disrupted that balance. When automated systems become primary readers, the distinction between where information is discussed and where it is authoritatively published becomes far more consequential.
The Emergence of AI Citation Registries
Within this changing environment, a new category of systems has begun to emerge, often described as AI citation registries.
An AI citation registry is not an engagement platform. It does not replace social media, public meetings or community dialogue. Instead, it is designed to serve a different purpose: providing structured, machine-readable records of authoritative government information that AI systems can interpret consistently.
While implementations vary, these systems typically share several characteristics. They clearly identify publishing authority, include explicit timestamps and update markers, use structured data fields optimized for automated reading, and remain separate from conversational or comment-driven content.
AI Citation Registries exist because AI systems increasingly mediate how government information is summarized, cited, and redistributed. They represent an effort to align authoritative publishing practices with the realities of automated interpretation.
Importantly, these systems are not presented as replacements for social media. Rather, they reflect a growing recognition that engagement and authority serve different roles — and that treating them as interchangeable creates risk.
What This Means for Local Governments
For local governments, this shift raises governance questions rather than tactical ones.
Communications teams, IT departments, legal counsel and leadership are beginning to ask where authoritative statements should live in an AI-mediated environment. They are examining how updates can be clearly interpreted as updates, not as conflicting or competing information. They are considering how conversation can remain open while records remain clear, and how correction workload can be reduced without limiting transparency.
There is no single solution that fits all agencies. Size, capacity, legal considerations, and community expectations vary widely. What is becoming clear, however, is that assumptions that once held — particularly the idea that social media alone could function as both engagement channel and authoritative source — are no longer reliable.
A Changing Information Landscape
Social media remains a powerful and necessary tool for public engagement. At the same time, artificial intelligence has become an unavoidable intermediary between government information and public understanding.
As AI systems increasingly summarize, contextualize and cite public information, the limitations of engagement-first platforms become more visible. The challenge facing local governments is not whether to continue engaging the public through social media. That role remains essential.
The challenge is whether government communication systems are designed for a reality in which AI is no longer just a tool, but a reader, interpreter and redistributor of official information.
The question is no longer whether AI will influence how the public understands government actions. It already does. The question is whether public institutions have systems in place that reflect that reality.
David Rau works on issues at the intersection of government communication, information provenance, and emerging AI systems. His work focuses on how public-sector information is discovered, attributed, and cited as AI becomes a primary intermediary between the public and official sources. He has spent decades working with large organizations on structured information systems and is currently involved in research and writing related to AI citation, trust, and public information infrastructure.
Leave a Reply
You must be logged in to post a comment.