Blog post
July 23, 2026

Who really shaped Australia’s latest social cohesion debates?

The Australian public’s reaction to government reforms and leaders was especially eventful. Debates about campus safety by the Royal Commission, a tax deal between Labor and the Greens unsettling the finance and property sectors, and a speech on “monoculture” by Pauline Hanson shifting opinion polls in an unexpected way; were three complex stories that saw audiences taking different sides, leading to many perspectives and angles.

We used Isentia’s Lumina to track the different viewpoints, key people, and stories with the largest volume and audience. Over four weeks (22 June to 17 July), we found 62 unique perspectives and nearly 900 media items across these three stories.

Key Stories, Key Drivers

Here’s a quick overview:

The Royal Commission on campus anti-semitism

The Royal Commission on Antisemitism and Social Cohesion’s hearings on university campuses was the biggest story by far. In less than a week, it drew 33 perspectives and 453 media items, reaching over 628k audiences. The story’s size came from the many institutions involved—student groups, representative bodies, and the federal government—each offering their own view on the same testimony.

Pro-Palestinian advocacy groups had the widest reach, making up about a third of all coverage. Spokespeople like Yasmine Johnson from Students for Palestine and Nasser Mashni from the Australia Palestine Advocacy Network told the commission their campus protests are a legitimate justice movement. They also raised concerns that criticism of government policy is being confused with antisemitism, which they say limits open debate.

Jewish student and staff groups also received significant coverage, making up about a fifth of the total. The Australian Union of Jewish Students described campuses where some students feel hesitant to attend and highlighted gaps in how universities handle complaints and support those affected. Most of this coverage came from wire services and was widely shared across news outlets like The Australian or the Midwest Times.

The federal government provided a third perspective, with similar coverage. Education Minister Jason Clare said universities had been slow to act and announced plans to tighten governance standards. This includes clearer anti-racism policies covering both antisemitism and Islamophobia. Reports also noted that TEQSA, the regulator, warned universities about outside groups joining campus protests, and the government’s antisemitism envoy suggested universities could face funding cuts if they do not do enough.

The Labor-Greens Tax Deal

The second-biggest story was more focused but still managed to stir strong reactions. Labor’s deal with the Greens to close a borrowing loophole for self-managed super funds, in return for Greens support on capital gains tax and negative gearing changes, led to 22 perspectives and 232 media items, reaching nearly 177k audiences.

The government, supported by the Greens, presented the deal simply — it closed a loophole that allowed wealthy investors to use their super funds to compete with first-home buyers at auctions. Treasurer Jim Chalmers cited a 2014 recommendation to support the change, and Greens treasury spokesman Nick McKim called it a win against “wealthy property investors.”

The Greens, however, took a tougher stance and received similar coverage for saying the deal was only a partial win. They argued that allowing existing arrangements to continue would let Labour protect wealthy investors rather than renters, and said the housing crisis would now be “squarely of Labor’s design.” This shows that support from a governing partner does not always mean they are satisfied, as Country News highlighted.

Finance and business groups pushed back with nearly as much coverage. The Self-Managed Super Fund Association and the Australian Finance Industry Association said the borrowing rules did not pose a systemic risk and argued that regulators should focus on “aggressive marketing” and property spruiking, not legitimate investors. The Australian Chamber of Commerce and Industry warned that the wider capital gains tax changes could hurt business investment. ABC News gave the most detailed account of this perspective, noting the sector was “surprised” by how the deal was made.

Pauline Hanson’s monoculture speech

This story had the fewest perspectives (just seven) but still reached nearly 236k people through 210 media items. That’s a bigger audience than the tax story, which had three times as many viewpoints.

The story began when Pauline Hanson used a National Press Club speech to argue that Australia should replace multiculturalism with a single “monoculture.” She cited Paul Hogan and the Socceroos as examples. The backlash was quick and unexpected and Hogan himself called her a “pelican” and said her views were racist. His response ended up shaping the story more than her monoculture speech.

What makes this story notable is what happened afterward. Two polls, Newspoll and Redbridge, showed One Nation’s primary vote dropping by about two points (Dairy News Australia) and Hanson’s personal approval falling ten points into negative territory. Labor regained a narrow lead and Labor minister Murray Watt quickly described the numbers as a “reality check,”. This framing spread almost as widely as the original speech, as the Bendigo Advertiser reported.

The speech and the poll results are really one story seen from three sides — Hanson’s message, her critics’ reactions, and Labor’s use of the polling. Each angle received similar coverage, showing that the speech missed its mark and gave the government a useful talking point.

How does this inform PR & Comms Strategy?

First, the number of perspectives in a story is important. A story with many viewpoints, like the antisemitism hearings, needs a different monitoring approach than one with just a few, because the loudest voices might not always be the most important.

Second, pay attention when several perspectives are about the same size, as in the tax deal. If no single viewpoint stands out, the issue is likely still being debated. It’s a good idea to check back after some time instead of treating the first coverage as the final answer.

Third, compare any polarising message to the Hanson example before recommending it to a client. The numbers show that a divisive message can get attention but still turn public opinion against the speaker.

Conclusion

What links these stories is how much is lost when they are reduced to just two sides. The antisemitism hearings, the tax deal, and Hanson’s polling drop were all more complex than their main headlines suggested.

That’s why it’s valuable to track a story by its different perspectives and key drivers. See what Lumina can reveal for your industry or clients, and check out more analysis like this on the Isentia blog


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Audiences are no longer finding information through traditional search engines that favour established news outlets. AI models now highlight highly relevant and contextual information to audiences to often include niche and regional publications alongside major news media. This change challenges the old media hierarchy around tiered publications and pushes organisations to reconsider how and where they need to show up to stay visible in an AI-first world. 

Yes, organisations must focus on optimising their own content for LLMs, but will that always drastically increase the chances of AI models picking up your page? Probably not always. Smart strategy means targeting the specific publications your actual target audience reads — because those are the sources AI models retrieve when answering niche questions. 

It’s closer to digital PR than SEO

Generative Engine Optimization (GEO) is changing how brands approach online visibility. For years, traditional SEO meant focusing on your own site—optimising keywords, building backlinks, and improving on-page content. But AI models work differently. Instead of just using your website, these AI engines rely on trusted third-party sources to answer questions. This shift is taking place  gradually, of course. LLMs increasingly source from earned media (where it is accessible) and even offsite links from trusted sites. Owned media is still where the organisation has maximum control of how it’s own content travels, but a pivotal strategy shift is needed to match what AI models are picking up and citing.

To succeed with AI search, comms professionals need to think more like a digital PR strategist than a SEO expert. The best way to stand out is by earning mentions, quotes, and citations in the external publications your audience—and the AI systems they use—trust most. This does not make a distinction between Tier 1 or Tier 2 media. If AI models are crawling sites that mention an organisation, but the organisation does not acknowledge or even know those sites are being prioritised by LLMs, they risk falling behind in being the right kind of visible. 

To make this strategy work, looking beyond common metrics like traffic to the site or domain authority is not enough. Even a respected industry site will probably not influence AI answers as much if its content is behind a paywall or blocked from search engines. For AI visibility, accessibility to the site or page, structured data that can be crawled, and strong audience alignment are important. Since AI systems use both slow training cycles and fast real-time web searches (RAG), being featured on accessible, relevant niche sites helps an organisation show up accurately when models learn and when they search the web in real time. 

Why is Tier 2 media punching at Tier 1 weight?

According to Isentia's report How AI is destabilising trust and reputation amongst audiences, LLMs cite industry and trade publications about twice as often as traditional news sources. Company content and industry press make up over 60% of the share of voice LLMs use, while traditional news is twice as likely to generate negative sentiment. Thus, tier 1 outlets no longer automatically dominate AI-generated responses and may sometimes have the opposite effect.

Two main factors are driving this shift in which media is picked up by LLMs:

  • The paywalled problem was further expanded on by Dr Momoko Fujita during the Digital News Report: Australia webinar that news organisations must figure out how to make paywalled content easily readable by LLMs. By bridging this gap, these organisations can ensure that AI tools deliver accurate, high-quality reporting rather than missing out on premium content. If not, high-quality coverage may never reach the model. Isentia’s Prashant Saxena, VP of Revenue and Insights, SEA, during a recent partner event with IABC APAC on Why AI Visibility is the next reputation frontier illustrated a paywalled Bloomberg story, for example, that was accurately summarised details it could read at the top level, but fabricated details about raised guidance, even though guidance had been cut. This is because it could not read the rest of the article and tried its best to assume what it can with the information that’s accessible.
  • Specificity outweighs prestige. Tier 2, trade, and specialist publications are often more accessible, focused, and likely to provide the concrete, citable facts models need. Amy Chappell, Vuelio's Head of Insights Strategy, found a similar trend across sectors in her report on the visibility of supermarkets in the UK “ The role of AI, LLMs, and earned media in shaping reputation” and noted that supermarkets were most often cited by trade publications like The Grocer and Grocery Gazette, not national newspapers. Trade press stories, being more focused and well-sourced, provide models with clearer, more citable facts than broader national articles. This doesn’t mean that Tier 1 coverage does not matter — CEOs value front-page exposure because it remains highly influential. However, relying only on tier 1 hits now means missing significant AI visibility opportunities.

Cited vs consulted: LLMs read a hundred sources, but cite only a few

Which type of media gets cited relies upon how AI models scan different pages. If these models are citing much more niche media outlets, we can assume that a lot of these pages that are consulted could be a part of very relevant Tier 2 media that ends up actually getting cited, and that we’re seeing more and more examples of in AI answers.  At the IABC APAC and Isentia webinar on measuring brand visibility in AI answers, Prashant Saxena, Isentia's VP of Revenue and Insights for SEA, stated that in the search era "we would get sources on our page one, page two, mostly page one", and people would click through to form their own opinions. The combined click-through rate in that era was 35 to 40 per cent. Nowadays, he says, "it's just four to five per cent" — since LLMs provide a smooth, ready-made answer and "most of us aren't really checking the citations".

Communications teams now face a new consideration: the distinction between sources that are consulted and those that are cited. At the IABC APAC and Isentia webinar, Takeo Apitzsch, Hoffman Agency’s Chief Digital and AI Officer, explained that AI models scan hundreds of pages to generate an answer but cite only a select few to users. This means that the audience sees only a small, curated portion of the sources that actually influenced the AI's response and a lot of what actually shapes the AI answer doesn’t get visible credit. Therefore, organisations need to make sure they reach out to those publications that AI models can actually crawl and audiences trust the most. 

What does this mean for communications professionals?

We are seeing four practical shifts:

  • Rebuild your tier list based on what LLMs actually cite, not on internal assumptions. A so-called “low-priority” trade publication or niche forum may contribute more to your AI visibility than a national outlet you have long targeted.
  • Keep your reshuffled tier list fresh, not just correctly ranked. In Why is content freshness the new currency for AI visibility? we discuss that a page that hasn't been updated in eighteen months is far more likely to drop out of AI answers altogether, no matter how well it once performed. Getting the right tier 2 outlets on side is only half the job done. Feeding them (and your own owned channels) on an ongoing basis is the other half.
  • Treat consistency as an essential. The largest gap between an organisation’s claims and what an LLM will confidently state is often due to inconsistencies between owned content and third-party coverage. When this occurs, the model may stop providing factual answers altogether.
  • Shift your focus from share of voice to share of mind. It is now less about how much you are discussed and more about whether the systems mediating the most have got the correct information about your organisation.If the system holds the wrong version, your audience may never access the right one.

Structurally, as Ashley Knapp, Head of Brand and Corporate Affairs, East Asia at Schneider Electric noted during the webinar, these efforts can no longer remain siloed. Owned, earned, shared, and paid media have traditionally been managed by separate teams. Now, because of AI visibility, this required a unified approach, as models do not distinguish between departments but are first to detect inconsistencies. 

This also means reconsidering the PESO (paid, earned, shared and owned) strategy deployed by organisations since the way that LLMs access and prioritise them has changed. They prioritise brevity in content due to the high costs of GPUs and data centres. As a result, the shortest, clearest, and most trusted answers are favoured which benefits brands with strong reputations. Earned media remains important, but its influence now depends more on the credibility of the analyst than the platform. Shared content amplifies messages more than ever but is also where misinformation spreads fastest. Paid media is becoming more prominent in some models, though brands are still learning how this impacts visibility.

Media monitoring companies are becoming strategic AI visibility consultants

This shift requires media monitoring companies to evolve. Tracking mentions and sentiment across media channels has been central to media intelligence, but AI visibility has added a new dimension to this.  This means monitoring not only what is said about an organisation, but also which sources AI models use when answering questions about that organisation, and assessing how current, authoritative, and consistent those sources are. This gives media monitoring organisations an opportunity to own what they’ve developed and also be thought leaders in this space. Stakeholders value the “so what” advice much more than just knowing “this is what is being said about you in the media”.

Lumina AI View addresses this by tracking which sources ChatGPT, Gemini, Claude, and other models cite when representing an organisation, benchmarks citations against competitors, identifies narrative shifts before they reach stakeholders, and regularly scores AI visibility against four reputation pillars: Direction, Performance, Integrity, and Innovation, These pillars have always supported reputation management, now applied to a largely unseen audience.

If you're weighing up where a tool like this sits alongside the rest of your stack, our own comparison, Best AI Tools for PR & Comms Teams (2026), breaks down how AI-assisted coverage, measurement, crisis response and reporting tools stack up, Lumina included.

Because that’s really the mindset shift comms teams, and the firms advising them both need to make. As Takeo put it on the IABC APAC and Isentia webinar: “I fear that this is the mindset shift communications teams and their advisors must adopt. I fear that AIs will be your secondary, and if not, at least equal… audience in the future.” Beyond human visibility, reputation is about being accurately represented by the systems that mediate access to your audience, which is an additional layer that cannot be trivialised anymore. 


Want to see which sources are shaping how AI describes your organisation? Get in touch about Lumina AI View.

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Blog
How relevant is Tier 1 and Tier 2 media hierarchy in impacting how organisations show up in LLMs?

The hierarchy that exists between Tier 1 & 2 publications today is being challenged. AI models are the new way audiences discover information requiring organisations to rethink how they show up to remain visible in an AI-mediated environment.

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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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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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