Blog post
October 29, 2025

Australia’s social media ban played out in the headlines

Australia’s upcoming social media ban for minors hasn’t been primarily driven organic debate. Instead, it’s unfolded through a deliberate, tightly paced sequence of government-led communications, each phase designed to build momentum, secure legitimacy, and keep control of the public narrative.

What we’re seeing in the media data isn’t a spontaneous rise in interest, but a pattern of spikes that line up neatly with major government moments. Each one serves a purpose in a broader narrative strategy, and each reveals something about where the public conversation is heading next.

The rollout of Australia’s social media ban has followed something of a three-act script. It really began on the world stage, with Prime Minister Albanese’s UN address framing the policy as a “world-first” and earning global praise that positioned Australia as a leader rather than a legislator under pressure, a narrative heavily amplified across bulletins nationwide. Momentum built when Denmark echoed the proposal, turning the story from an Australian policy into a global movement and giving journalists a reason to return to it without new domestic detail. Subsequently, the focus shifted home, with the launch of the government’s ad campaign. Coverage has moved from delivery to confirmation, from diplomacy to daily life, embedding the message of child safety through stories designed to connect emotionally with parents before the ban takes effect. 

Media coverage of the social media ban is being driven by a hierarchy of voices. At the top are the political architects, Anthony Albanese and Anika Wells, who account for 68% of all quoted commentary. Their dominance reflects a message tightly controlled from the centre, with each public appearance designed to reinforce authority and focus the debate. eSafety Commissioner Julie Inman Grant follows as the enforcer, providing regulatory credibility and keeping the story alive through ongoing updates and meetings with tech companies.Around them, Emma Mason’s personal story gives the policy its emotional weight, while expert voices like Dr Jason Nagata and Mitch Prinstein lend scientific legitimacy. Counter-voices such as Patrick McGorry are present but faint, just 1% of total commentary. Together, these strands create a coordinated ecosystem where political leadership, regulation, expertise, and emotion work in unison to sustain a single, dominant narrative.

The next layer of coverage reveals how the story’s momentum is being sustained, not just by government messaging, but by the constellation of organisations caught in its orbit. Meta, Google, TikTok, and Snapchat remain the gravitational centre of the conversation, collectively shaping more than a thousand mentions each. They are the policy’s focal point and the media’s shorthand for what’s at stake. 

Stories about ministerial meetings, enforcement challenges, and pleas for exemptions ensure these brands stay in the headlines, but on government terms, framed as subjects of regulation rather than equal participants in debate. This has also surfaced one of the key underlying questions: Will the ban actually work? There is a significant narrative thread focused on the practical challenges of enforcement, with YouTube widely quoted in the media as saying the ban is “‘extremely difficult’ to enforce”. 

With the media also reporting that the government will rely on “artificial intelligence (AI) and behavioural data to reliably infer age” rather than hard age verification, the public is left asking: If tech giants say it’s unenforceable and teens are already finding ways around it, what will this law actually achieve? 

The eSafety Commission anchors the enforcement narrative, while the European Commission’s support sustains the “world-first” framing abroad. As the scope of the ban widens, platforms like Roblox, Discord and Reddit have been pulled into focus, signalling how the policy, and its coverage, keeps expanding. This has forced the core question into the open: What is a “social media platform” in 2025?

Although the government’s narrative still dominates, a set of counter-stories is emerging, focusing on the policy’s real-world consequences. Central to these stories are concerns about young people losing access to vital online connections, particularly among regional or marginalised communities. Advocates for the LGBTIQA+ community and youth mental health experts like Professor Pat McGorry argue that the ban could isolate teenagers who rely on online spaces for support, and entrepreneurial opportunities. Other reporting has questioned the reliability of AI-based age verification, the volume of data collected, and the risk that well-intended rules might backfire, creating unintended consequences that contradict the policy’s goal of child safety. These counter-narratives remain smaller in scale than the dominant political messaging, but they cut through because they frame the debate around everyday impacts rather than top-down authority.

A particularly visible strand of coverage centres on the unclear definition of “social media” in the legislation. While the public typically thinks of platforms like Instagram and TikTok, the law’s wording has forced a broader debate that draws in platforms such as Roblox, Discord, and Steam. The eSafety Commissioner’s proactive enforcement measures have highlighted these regulatory ambiguities, prompting media to question whether platforms with different primary purposes should be included and whether the policy might trade one harm for another. Discord drew attention following a poorly timed data breach, which the public and media linked to potential ID theft risks. These reports show how regulators and secondary players can keep the conversation alive, highlighting risks, opening new angles, and forming alliances that complicate the policy debate. A notable example is YouTube’s effort to argue it should not be classified as a social media platform, citing the platform’s role in launching careers like Australian artist Troye Sivan as part of a broader cultural and creative ecosystem.

Together, these stories illustrate that while the government controls the main narrative, emerging counter-voices are beginning to shape the media conversation in ways that emphasise practical and social realities.

Learn how Isentia helps comms teams manage media coverage and public opinion around major policy changes.

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Would you trust a brand more if an AI model recommended it? For many, the answer is yes – and it’s changing the very nature of PR & Comms.

Our latest report digs into the changing nature of trust, as audiences turn to AI models for quick answers instead of going to organisations or media outlets directly, with AI fast becoming the final stop in the comms cycle. 

This report unpacks:

  • Why trust has shifted, and where audiences are having these conversations
  • Why AI has become the last stop in the comms cycle
  • Methods for staying on top of your brand trust and reputation

To access the full report, fill in the form below:

Discover our Lumina AI suite here.


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Blog
How AI is destabilising trust and reputation amongst audiences?

Learn how LLMs reshape brand perception and actionable steps organisations can take to maintain trust and reputation in the new information era.

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If you ask ChatGPT or Gemini about your organisation today, the answer won't come straight from your website. Instead, it uses sources the model already trusts, which are often months or years old. So if your last big mention was a crisis or a controversy from 2023, that's probably still how AI describes you.

This is the tough reality for anyone working in PR and communications today. More people are getting their first—and sometimes only—impression of your organisation from an AI-generated summary, not from search results or the homepage. And these summaries often rely on outdated information.

What does freshness actually mean?

Content freshness refers to how recent the sources are that an AI model uses when it talks about you. It might seem like a minor technical point, but it's actually very important.

Search engines have always valued fresh content, and they let you update information quickly. If you change a page, Google recrawls it, and rankings can shift in days. Large language models don't work like this. As Lisa Main, Director at Main Bureau, said on Isentia's "AI as a Stakeholder" panel,  "large language models are not databases of verified facts." These models are trained on a snapshot of the internet, updated only from time to time, and they rely on sources that were already prominent when they were trained. This means a past crisis or a controversy that is already resolved can keep showing up in AI answers long after it's no longer relevant.

She shared the example of how a day and a half after a notorious terror attack, she asked ChatGPT if the area had ever experienced a tragedy of that type. It replied that it had not." The model wasn't being careless, but it just hadn't updated to include the latest news. This gap between what reality is and what AI still believes is true sums up the content freshness problem.

Dr Nici Sweaney, founder of AI Her Way, explained on the same panel why this gap matters. She calls AI "an accidental narrator" — it shapes what people believe about your organisation just by repeating the latest information it received. The system simply uses what's available and is not trying to be harmful, so it's important to make sure that information is up to date.

How does this change the way organisations show up?

For PR and communications teams, this changes what "reputation management" means. Put simply, messaging that an LLM cites will remain relevant, no matter when it dates from. Messaging that has not been factored into the LLM’s answers, meanwhile, will have no discernible impact on an increasingly vital - even central - channel, regardless of how many other metrics it might win out on. 

This leads to two important things to consider:

  • First, the conditions that surround recent earned media, statements, and announcements determine whether an AI model updates its picture of the brand, or keeps running on an outdated one. Catherine Arrow of the PR Knowledge Hub made a related point on the "Inside the AI Shift" webinar: LLMs and the agents built on them are "often forbidden from going behind paywalls, from scraping particular sites," which she said creates a kind of "news vacuum." The same logic applies to the brand’s own newsroom or press page. If it isn't feeding the model something current, the model has nothing current to draw from.
  • Second, owned content—like blog posts, media releases, and website pages — are strategically important because they’re something the organisation in question can control , but only if they are updated. If a page hasn't changed in eighteen months, it's much more likely to disappear from AI results, making any reputation built on it unstable. If something is published once and not updated, the brand risks letting older, less positive stories take its place.

For public sector and government communicators, the stakes are more immediate again. When a government agency's guidance changes, whether that's eligibility criteria, compliance requirements, or a service update, and the fresh version doesn't make it into what AI models are citing, people will still get fed old information, with potentially devastating real-world implications. 

The evidence is already there

This is not just in theory. It's playing out in global research and in the day-to-day data right now.

  • AI is quietly replacing the front door to your content

The Reuters Institute's Digital News Report Australia 2026 confirms that many PR teams have noticed that Google organic search traffic to news sites dropped by a third worldwide between November 2024 and November 2025, and by 38% in the US, as AI Overviews and AI Mode launched. Publishers expect this traffic to nearly halve again in the next three years. Some now call this trend a move towards "Google Zero." For communications teams, this means people are increasingly less likely to  click through to your website to check if information is current. More often, they're trusting what the AI says: hence why it’s so important to monitor content freshness.

  • AI models are now web-enabled and they might not actually guarantee source accuracy

One challenge is that most major chatbots are now web-enabled. For example, ChatGPT can browse the internet, Gemini uses Google Search, and Perplexity has its own live index. This makes it easy to assume that AI always knows the latest information. However, this does not mean that they are always accurate when it comes to citations. A study from Columbia's Tow Center for Digital Journalism tested eight AI search tools with 1,600 queries. They found that these tools failed to correctly identify or cite the source article more than 60% of the time. Some tools were wrong on most tests and rarely showed any uncertainty. New information has not had time to be checked or confirmed like older stories have. This is the real risk of relying on the newest updates — a story that is fast moving and poorly sourced about your organisation might end up in an AI answer before it’s even verified or fact-checked. 

  • People are turning to AI chatbots specifically for what's new

The same report found that 35% of people who use AI chatbots for news do so to get the latest media updates. Dr Sora Park from the University of Canberra's News and Media Research Centre explained on the "Digital News Report Australia 2026" webinar that the main reason people use AI chatbots for news is that "AI collates stories from different news sources into a single response." People expect these tools to provide current information. If your organisation's newest content isn't included (and you have something current or novel to communicate) you miss the chance to reach audiences when they're most interested.

  • Fresh content doesn’t always equate to ‘new’ content

A notable example  of creating freshness that LLMs reward and prioritise comes from updating existing pages, rather from creating brand-new content. Republishing and refreshing current material is more effective than many communications teams realise, as long as one actually updates the content, not just the date.

  • Evergreen pages are the first casualties when AI overviews roll in

The DNR Australia 2026 report also notes that once someone is inside an AI chatbot conversation, they rarely leave it to check the source — only 4% of AI chatbot users say they always or often click through to the original article, compared with 19% for search and 17% for social media. The pages that used to earn traffic just by sitting there, permanent and useful, are now the ones most likely to lose visibility, because AI models favour what's recent over what's merely correct.

  • One fresh statement doesn't automatically undo a stale narrative

If an executive online, especially one who has a lot of weight to what they post online, says something controversial and it quickly spreads across media articles, social media and search — it will definitely be picked up by AI as well. There is a golden window of opportunity that they need to capitalise on to clarify what they said. If they don’t, the negative story that was already built into the data AI models use, will not be affected much by the executive’s clarification statement, which wasn’t that timely anyway. As Catherine Arrow of the PR Knowledge Hub said on the "Inside the AI Shift" webinar: "public relations and media relations are not the same thing," and relying on a single release misses the point. The real lesson is not to publish faster after a crisis, but to build a strong, up-to-date presence before you need it. In our latest report, “How can leaders communicate in an age of scrutiny”, we’ve outlined exactly how comms leaders can communicate by adapting their content to audiences exposed to the “AI way” of news dissemination. 

What PR & Comms teams should actually do?

The challenge is that organisations can't make an AI model update its answers whenever they want. What they can do is track whether recent work is actually being noticed, which is what  Lumina AI View can help with.

Lumina AI View monitors which sources AI models use when talking about your organisation, how strong and recent those sources are, and how you compare to competitors. Freshness is one of five key factors in the overall score. If your freshness score drops, it's an early warning that your latest campaign or announcement hasn't reached the AI ecosystem yet, and older stories are still dominating.

What’s important to note is that the tool provides a list of source citations, paired with reputation pillars like direction, integrity, performance and innovation — giving a comms professional a fully-rounded understanding of what they need to do. It’s not just the case of knowing source citations, but also of understanding your own AI perception and performance to make informed decisions — whether that’s for a brand,a government agency, a NFP or elsewhere.

This kind of tracking is even more important because it shifts by industry and by market, so "AI visibility" doesn't mean the same monitoring job for every organisation. AI answers for healthcare might draw from the smallest, highest-trust pool of sources (mostly clinical and government), but SaaS and fintech answers lean heavily on editorial reviews and comparison sites.  Ngaire Crawford made a similar point regionally on the "AI as a Stakeholder" panel. For the APAC region specifically, she pushed back on the assumption that editorial media dominates AI citations — "there are a lot of really massive claims about the impact of editorial media... some as high as 85, 88%. That's not what we're seeing." Instead, she found "a fairly even split between (editorial media) and company content," alongside a real presence for review sites, forums, and academic sources. For a comms team, that means the freshness strategy that works for a media-heavy consumer brand might not work for a government agency whose AI visibility is really riding on review sites, .gov pages, or industry forums instead.

By tracking regularly — weekly or as a routine check— you turn the vague concern of "what is AI saying about us" into something that is super clear. You can see if recent coverage changed your list of citations, or if your owned content is still being found, or where there are gaps that need to be filled because old stories still exist and are causing problems.

The opportunity in staying current

There's a real advantage here too. If old content keeps you tied to an outdated story, fresh content is a direct way for PR and communications teams to influence how AI presents them. Publishing regularly, keeping your own pages updated, and getting recent, credible coverage is not just for human audiences. It's how PR professionals can make sure the systems shaping first impressions have the right information.

Teams that make it an ongoing habit of checking in regularly, watching for changes, and keeping fresh, credible content flowing, will have more control over how AI describes their organisation.


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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Why is content freshness the new currency for AI visibility?

AI summaries are replacing websites as your organisation’s first impression. Here’s why content freshness—and the sources feeding these models—matters more than ever.

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