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The Federal Election has been announced and with 33 days of campaigning we thought it would be interesting to compare the number of mentions of the political parties over the past 2 weeks. Analysis from our media intelligence has given insight into Labor having a larger volume of media coverage across all media types in comparison to other political parties.

Examining coverage, we found the ‘Election’ had been the subject of over 33,000 media items and online being the preferred media type.

In terms of political parties, Labor has had a significant number of mentions of broadcast coverage whereas the Coalition had more mentions across more traditional media such as print, during the two-week period. Overall, Labor has had a decent lead over the Coalition in the number of mentions across broadcast, online and print combined during this time.

Interestingly, social mentions over Twitter, Facebook and YouTube were more prominent with individual parliamentarians rather than mentions of political parties. These items mentioned more controversial statements or social ‘worthy’ statements which generated these mentions.

It was found the Coalition had considerably more social mentions over Labor when searching for the parliamentarian’s name or their handle. One Nation were also in the mix, with more social mentions compared to the Greens and the Nationals combined.

With the data analysis we have uncovered, could this be insight into who will win the election on May 18?

If you would like to keep up-to-date for the remainder of the Federal Election campaign, our exclusive Federal Election briefing can ensure you're across all campaign announcements, funding commitments, policy updates and polling figures. If you would like to learn more about this service, get in touch with our team to discuss your needs.

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Stay on top of the federal election coverage

During an election, the volume of media coverage on political promises and topical debates increases. This can have a positive or negative impact on your organisation.

With our comprehensive federal election briefing, you can monitor and track relevant media data to gain insight into the federal election.

Understand your organisation, your competitors, your industry and the important topics. Understand the media data that shapes each campaign day.

From policy, campaign and program announcements to funding commitments and latest polling figures we can ensure you're kept up to date.

Download your sample below or get in touch with us to today!

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Ngaire Crawford, Director of Insights, ANZ

In leadership meetings across the industry, a single question has become unavoidable: "What is our AI strategy?" Behind this question is often the unspoken hope for an "AI Easy Button": a mythical, one-click solution to our most complex measurement challenges. As someone who spends a large portion of my time designing these new frameworks, I'm infinitely more excited about the blueprints and the foundations than what colour the house is painted.

For the first time in my career, we have the tools to stop using proxies and start building what we've always wanted: true, at-scale, sophisticated measurement. The real opportunity isn't in automation, which lets the AI decide;  it's in the architecture and design of systems for the AI to follow. For decades, I’ve been frustrated by proxies. I’ve watched organisations use metrics like Impressions and Share of Voice as proxies for impact and influence. Too many people have been measuring the loudness of their voice, not whether anyone was actually listening.

Much of the history of communications measurement has been a story of 'good enough' data. And in some cases, data that wasn't even good at all (*cough* AVEs). 

Why a blueprint still needs an architect

But before we can harness the potential of AI, we have to be honest about the technology and tools we're working with. As anyone who's ever used a "smart" tool knows, they can be... well, confidently wrong.

The new challenge isn't just "Garbage In, Garbage Out." The new challenge is that the AI has become a high-speed, frighteningly convincing echo chamber. When a machine delivers a flawed insight, it does so with the resolute certainty of a supercomputer, laundering that flaw into a "fact."As architects, our job is to audit the blueprints and stress-test the materials before we build the house. When my team and I test these models, we're not just looking for what they do right. We're methodically hunting for where they go wrong.

Where we continue to see a critical need for human intervention and expertise:

• Context Blindness: AI is a brilliant pattern-matcher, but it has limited real-world context and struggles to identify the intent of what’s being analysed. It can miss the nuance of language, the authority of a source, or whether something is fact or speculation.
• Language Bias: This is my personal favourite and takes a few forms. AI is trained on text, but it isn't (yet) trained on human subtext. This can look like missed nuance for slang used by younger audiences or emerging shifts in the meaning of language. Models are ultimately impacted and biased by their training data, so this can also mean larger systemic biases are amplified and not appropriately interrogated.
• Viewpoint Collapse: While AI can sometimes get locked into a perspective based on its training, it can also collapse multiple, distinct viewpoints (like a speaker's sarcastic intent vs. the literal text) into a single, flat monolith. This drastically changes the outcomes of your analysis and ultimately the understanding of your audience.

This is the methodical, behind-the-scenes work that often goes unseen, and it is the crucial due diligence needed. It’s not as flashy as writing a press release faster, but it’s the only way to build a tool you can actually trust to make a strategic decision.

New tools, same bedrock principles

This testing isn't just about finding technical bugs or funny hallucinations. We’re testing these new AI models against the foundational, hard-won principles of communications measurement that our industry has spent years formalising.

AI is an incredibly powerful new tool, but it doesn't get a free pass. It still has to follow the rules of good measurement.

• Measure outcomes, not just outputs: This has always been our goal. An AI-driven approach that only counts outputs (like mentions or sentiment) 1,000 times faster is still just a faster measure of noise. It doesn't tell you if a single mind was changed or a single action was taken.
• Demand transparency: A metric is useless if you can't explain how it's calculated. This is my biggest critique of the current "plug-and-play" approach to AI. If a vendor provides a proprietary 'Reputation Score' of 7.2, and they can't (or won't) tell you the formula, it's not a metric. It's marketing.
• Link activity to business objectives: This is the most important rule of all. The only reason to measure is to inform a strategic decision that ladders up to a business goal. A tool that just produces data, but no clear insight linked to your specific objectives, has failed.

When we stop seeing AI as a magic box and start seeing it as a powerful, scalable engine, one that we must build and steer based on these principles, then it becomes truly transformative.

The payoff: the tools are finally catching up to our ambition

A new frontier of opportunity is here. Such as the capability to move from being reactive to being predictive, and it takes careful design to get this right. Our traditional analysis has been brilliant at explaining what has just happened. Now, as architects of these new systems, we are building and testing AI models that can scan the horizon for the faint signals that precede a major narrative shift.

We can empower movement from broadcasting and the old spray and pray approach; to precision, deliberate engagement of stakeholders and audiences. This is another area where the craft of measurement design is essential. AI gives us the power to see the micro-communities and specific, high-authority voices that actually shape opinion. The work is in designing the models that can identify them accurately.

Finally, we can (at last!) move from quantifying to qualifying at scale. For me, this is the most exciting and complex challenge. For 20 years, I’ve had to choose: a large-scale quantitative study (which missed nuance) or a small-scale qualitative review (which couldn't be scaled). As architects, we can now design frameworks that don't just give a "positive" score but confirm that a specific strategic message landed, with the right audiences, and in the intended context.

That is the opportunity. It's not magic. It's the methodical, patient engineering we've been waiting for. It’s the difference between a "plug-and-play" gimmick and a truly strategic asset. The real payoff isn't just faster reporting, it’s about fundamentally upgrading behaviours and expectations of measurement. This isn't an overnight shift. As any research leader will tell you, a new methodology takes time, testing and refinement to get right.

The future we've been waiting for

For my entire career, we’ve been strategic thinkers working with tools that could only show us the past. We were forced to be historians, meticulously analysing what had already happened to predict future behaviour. The key to using this new, complex technology effectively is; strong communication, articulation and critical human thinking. The power of any AI is unlocked by the quality of the question you ask it. It's a system that rewards clear, precise, and strategic language.

This is a massive homefield advantage for communicators, who have spent their entire careers honing the exact skills required to be the architects of this new era. The AI we are using today is the worst it will ever be. It will only get better, faster, and more capable from here. This is what's so thrilling, and it's just the beginning. This new generation of AI driven approaches doesn't replace our intuition, it amplifies it. As communicators (and researchers!) this is the moment to level up. We get to be the explorers and the strategists who connect communications directly to business, policy and societal outcomes. 

We're not just building better measurement and deeper insights; we're leading a more intelligent, more responsive and more impactful profession. What an incredibly exciting time to be in this industry.

Ready to be the architect of your own measurement strategy?

To learn how to build the right KPIs and tell a compelling story with your data, register for our live webinar:

• Topic: Making Communications Count: Build your KPI confidence and storytelling"
• Date & time: 12 November, 11am AEDT/ 2pm NZT
• Hosted by: Ngaire Crawford, Director of Insights for ANZ, Isentia.

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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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