It is only a little over a year since the Facebook Cambridge Analytica data scandal erupted on the news but it has caused a major perception shift among internet users. Even those who were apathetic about social media usage are made more aware of its privacy infringing properties. Many have expressed being more cautious of their data footprint and declared on the increased value they have placed on privacy.
Yet, Higher Engagement on Social Media?
However, according to The Guardian, the number of daily active users has shot up across all social media platforms with average revenue per user being 19% in 2018, and overall revenue for the last quarter of 2018 is 30.4% more on the same quarter in 2017. This then brings into question the privacy paradox: why are people more engaged than before on social media despite being made more aware of its potentially unscrupulous terms and conditions as seen from countless data leak reports?
The Data-Anxiety Wave
While looking at the types of data leaks and social chatter that centers around data and privacy concerns could lead us to understand the causal relationship between the two, it would be more insightful to examine the way the narratives of the leaks have influenced internet users’ impression of data-driven behemoths like Facebook, Google and Apple. For instance, the only derivation from the types of data leaks such as the infamous Cambridge Analytica data scandal and the more recent WhatsApp security breach would be the increased in awareness of such cases that are occurring due to the prevalent use of social media. This would then put tech giants’ privacy terms under scrutiny as they are conduits to which data are channelled and distributed to businesses at a profit.
The increase in data leak reports has pushed data security watchdogs to be on their toes leading to more cover stories divulging data-exploiting activities. These essentially generate a hotbed for chatter on the technology that allows for data mining to occur. As seen in the buzz clusters, keywords for “Data” and “Privacy” generated similar topics of conversations where online users remain concerned about how their data have been misused through smart phones, along with the operating soft wares and services that run on it for two consecutive months.
The Law of Supply and Demand
This is of no surprise once we delve deeper into the narratives that instigated the data-anxiety wave where privacy is fetishized. News headlines as illustrated the figure below, consistently featured tech giants’ surreptitious tendencies to harvest data, focusing on their instrumental role in disseminating the data collected to companies and, even the government. The underlying message that may have been missed is the market forces responsible for such behaviour. Tech giants are merely the middle man, supplying a type of good – personal data – to interested buyers. The buyers are almost always spared from antagonistic journalism and eventually overlooked as allies to the process of data exploitation.
Unmasking the Illusory Nature of Data-Anxiety Wave
Pinning the privacy paradox onto cognitive dissonance experienced by online users as they are unable to connect data-harvesting intermediaries to their end consumers, could explain the rise in social media engagement despite the prevalence of data leak cases as observed by the study reported by The Guardian. This is good news for businesses who have been partnering with data-mining companies as it buys them time to reassess their marketing strategies, focusing on gaining the consumers’ trust. If internet users remain willing to share their personal details in exchange for customized content on their dashboards, it is up to businesses to retain such consent by focusing on integrity in their operations through contractual transparency. They ought to work towards unmasking the illusory nature of the data-anxiety wave brought about by negligence on the tech giants’ part by taking the lead in revealing the true benefits of data sharing – a more efficient market where research has been conducted through the provision of personal data.
Instead of endlessly promoting privacy,
maybe it is time we address the elephant in the room and embrace it: data and
its prime influence in the digital age.
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The advent of LLMs and AI search means that there has been a colossal shift in how audiences are consuming information today, and a reciprocal shift in how all types of organisations, from government agencies to brands are responding . But while discussion amongst PR and comms pros tends to fall disproportionately on how brands are impacted, the needs of the former are just as keenly felt, and often quite distinctive.
So how can government agencies respond, especially in a time of global flux, when major policy changes need to be communicated and important stakeholders need to be managed? After all, government organisations do care about reputation, much as brands do, but have quite distinctive goals when it comes to ensuring accurate information reaches the right audiences.
There is a new entry point for stakeholders
The era of "Let me Google that" is rapidly fading. Instead of clicking through to official websites, people are asking chatbots for direct answers. What’s striking is that government agencies often have no visibility on how they’re being talked about in the LLM space, even as it becomes a central channel for messaging and reputation.
When AI models become the primary gatekeeper, audiences bypass official portals entirely — driving down site traffic and leaving agencies vulnerable to misinformation, negative sentiment, or worse, being left out of the conversation altogether.
Therefore, the entry point is different. For commercial brands, this shift is profound but in some ways mediated – an FMCG brand, for instance, often discovered through third-party platforms in any case. But for government entities, the stakes are entirely different. As the sole, authoritative source for public information, they need citizens on their websites to get accurate details. Government agencies, especially if they are the authority or regulator in a particular industry or sector, need to make sure audiences know where to go to get the right information.
Different types of government agencies have different considerations
Not all government agencies are alike, and they all have different parameters that are quite non-negotiable for them, just by the way they function.
Service delivery agencies - these rely heavily on content freshness. They can’t risk outdated sources impacting eligibility or process changes not reaching audiences.
Regulators - these need to transmit authority and trust. Regulators have to make it a priority that they’re amongst the first place audiences go to for information and that the industry they’re regulating does not put them in the shade on the channels stakeholders are actually using
Policy departments - did the AI's account of a policy match what was actually announced? They want to be able to make sure the accuracy of a policy (and ideally, its effectiveness) is translated when audiences search through LLMs.
Local and state authorities want to make sure the services they carry out on behalf of locals are visible too. Much like service providers, there is a question around access and awareness of programmes and regulations, but also an added consideration: not appearing on LLMs discontent amongst those wishing to see a return on taxation and electoral mandates, and give credence to bad actors.
What do government agencies want to get out of this new LLM-mediated landscape?
Reputation is important, but that exists downstream from maintaining a flow of accurate information. It’s useful for communications teams in government organisations to self reflect and ask themselves the following questions:
Where are citizens going to find information about your services if not your website — and do you know what they're being told?
If there was a significant policy change or incident in the last twelve months, do you know how it's currently being characterised when someone asks an Al tool about your agency?
When you communicate a major service change or policy update, do you have any way of measuring whether it’s surfacing in searches about you?
How do you currently understand the gap between what your agency publishes and what citizens actually receive when they search for information?
Are there community groups, advocacy organisations, or media outlets shaping perception of your agency - and do you know if that's feeding into what Al models say?
These are gaps they already realise, but they don’t actually know what to do about it – how to manage or measure them. They need a tool that allows them to know this critical piece of information and make informed decisions.
Ngaire Crawford, Pulsar Group’s Executive Director for AI Strategy says, “It’s easy to disregard LLM reputation as part of a difficult AI landscape or something that is only really relevant to more product-based communication or marketing. Government communications is about social licence, and ensuring the public have access to up to date and accurate information about things that matter to them, the role that Generative AI plays in how a community understand an issue will only continue to grow, and knowing the impact that you can have in that through small shifts in channel strategies or more consistent messaging is a crucial part of the communications toolkit.”
Lumina AI View: AI visibility for PR & Comms
Lumina AI view is built for communicators who want to understand how their organisation and their competitors are being talked about by various AI models – including ChatGPT, Gemini, and Claude. AI View users get an insight into which sources are being cited, and how they would need to respond as a way of protecting their reputation or making sure correct information about them is being disseminated.
The tool provides an AI view score — a composite metric ranging from zero to 100, designed to help track brand performance over time and facilitate comparisons against competitors. It is calculated using five weighted factors — sentiment, visibility, authority, dominance and freshness. Beyond the overall score, the platform provides a summary of a brand's AI narrative based on four distinct reputation pillars — direction, performance, integrity and innovation.
These pillars help users identify exactly which dimension of a brand's reputation is under pressure, offering specific, actionable insights for board presentations or reviews. Ultimately, while the AI view platform provides the necessary intelligence, the strategic decisions regarding how to respond to these insights remain with the organisation.
Spotlight: An Australian Council
This progressive local government council is located in Australia’s leading center for culture and sports.
The council earned an AI view score of 74 reflected by strong reach and authority. Publications like CBD News and its own website are the most cited by LLMs — interestingly, most of them being cited by Claude.
Content freshness scored lower at 48. Their website still carries error pages and annual reports from a few years ago. If a report — one that is seen as an organisation’s most comprehensive and authoritative content, is still being cited even if it’s older, might potentially be in the way of the organisation’s own perception. Which means more work is needed to prevent outdated content from still appearing.
What type of content is showing up?
Most citations for the council originate from government sources, followed by news outlets, reports, and blogs. The domain is evenly split between owned content and content earned from external sources media articles and independent authorities.
While most are recent, some older articles from major outlets such as The BBC remain visible and may significantly influence how LLMs perceive the council. Owned content typically addresses last year’s budget plans and the council’s latest vision for the city, which LLMs are referencing. Government sources are the major content type, however, external sources have a greater impact on the council’s overall LLM score.
The stakes are higher for government agencies
When brands track LLM visibility, they often ask, "Are we shown in a positive light?" or "Are we cited accurately?" For the government, additional questions arise: "Is this information accurate enough for someone to act on?" and "Are we still viewed as more authoritative than what we oversee?" Mistakes can have serious consequences, such as individuals applying for ineligible programs or missing critical deadlines for new initiatives or elections. This can quickly lead to public frustration. It is essential for government communicators to recognize these risks.
If you would like to know more about our Lumina suite, please reach out here and our team will get in touch with for you a quick demo.
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Blog
Why is tracking government visibility on LLMs different from tracking brands?
Government agencies often can’t see how AI chatbots describe them. Here’s why LLM visibility matters and how to track it.
There is a new frontier where public perception is shaped: Large Language Models. Right now, LLMs are answering critical questions about your organisation. What are they saying? And more importantly, which sources are shaping those answers?
To navigate this landscape, public relations professionals don't need generic tools, but rather technology that speaks their language, and addresses the realities of a changed media and informational landscape.
That is why we're unveiling Lumina AI View, the latest addition to our intelligent suite of AI tools from Isentia. Trained specifically on the workflows and challenges of modern PR & communications, Lumina AI View helps you understand exactly what AI knows about you, and how it learned it.
A new standard for AI visibility
AI View tracks your citation strength and source quality alongside those of your competitors, giving you a clear view of where you hold authority and where you have gaps.
Lumina AI View maps your AI reputation from the ground up, allowing you to:
See which sources matter: When tools such as ChatGPT or Gemini discuss your organisation, which outlets do they cite? Track your source footprint over time and view the impact of key target media on how you’re discussed. We measure your citation strength and source quality alongside those of competitors, giving you a clear view of where you have authority and where you have gaps.
Gain industry-specific insight: Your competitors get cited from Financial Times and Bloomberg. You get cited on Reddit. Each brings opportunity – and risk. Discover how you measure up against industry standards, and target the sources that actually influence how AI represents you.
Catch narrative shifts early: AI responses change when new sources appear, sentiment shifts, or old controversies resurface. Get alerts when citation patterns change suddenly, before they impact the way you’re perceived by stakeholders.
Measure your progress: From media monitoring to full media intelligence
Lumina AI View is built on the principle that insights get stronger with repeated measurement. To help you maintain a clear view of your reputation, our proprietary scoring system provides regular updates that show you:
Evolving trends in how sources cite your organisation
Competitive standing and benchmark metrics
Where models differ in information presented, and sources cited
Whether you run it weekly, on-demand, or whenever you need a check-in, patterns will emerge, trends will become clear, and you will build a baseline that makes any sudden narrative changes both comprehensible and the prerequisite to action.
Lumina AI View is part of Lumina AI, a comprehensive suite of AI tools built specifically for communicators. Our Lumina suite evolves traditional media monitoring into narrative intelligence, enabling you to truly understand how perceptions form, evolve, and impact your reputation.
Get in touch to register your interest and see what Lumina AI View can do for you.
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Blog
Introducing Lumina AI View: AI Visibility Built for PR & Comms
Lumina AI View, the latest in Isentia’s AI suite, is trained on PR & comms workflows to help you understand what AI knows about you — and how it learned it.