A 10-Day Snapshot: How LLMs Describe Australian Political Parties to Voters

News and social media have always influenced how the average Australian votes. The difference now with AI is that millions of people are asking LLMs for information on political parties and leaders without knowing for certain where their answers are coming from and how they’re evolving.

These AI platforms are telling Australians what parties stand for, their strengths and downfalls, and are ultimately driving swing voters one way or the other.

To investigate how this is happening, we created multiple Streem AI Visibility Dashboards to look into how often each AI platform is mentioning each party, the sentiment each platform responds with when talking about each party, and where they’re sourcing information from.

Why LLM Monitoring Matters for Brands

1. Who Does this Affect
2. Our Methodology 
3. LLM Answers for General Party Information 
4. Party Policy Strengths and Weaknesses, According to AI 
5. What This Means for All PR & Comms Teams
6. Where to start with AI Visibility

Who Does this Affect

In June 2026, Roy Morgan released research that said 58% of Australians aged 14+ were using AI tools. The top five by usage were ChatGPT, Gemini, Copilot, Canva Magic Studio, and Claude. Australians aged 25-34 were most engaged with this technology (74%), followed by the 35-49 age group (72%), 18-24s (68%) and 14-17s (66%).

According to an ANU report on AI adoption in Australia, the younger and more educated an Australian is, the more likely they are to hold positive views towards Generative AI. 48.7% of Australians were using GenAI to seek information or facts at least one day per week.

In July, the University of Sydney Business School’s AI Exposure Index also used census data on occupation types to measure which of our 150 federal electorates were most exposed to AI-driven job disruption. The index found that top-ranked electorates by AI exposure were politically volatile areas. Leadership in these seats across New South Wales, Victoria and Queensland were changing at 3.2 times the typical rate across the 2019, 2022 and 2025 Federal Elections, meaning that LLMs are speaking to Australians who are more likely than most to change their political affiliation.

For government relations teams, this has become an obvious reputational risk with real-world consequences when pens are put to the ballot.

Our Methodology 

Manual and Streem AI-suggested prompts were used to emulate real questions asked by Australians in our analysis of LLM responses. Streem AI then identified Australian political parties within the data. These parties were our entities, which we then looked at to surface AI Mentions, Sources, and Sentiment.

LLM Answers for General Party Information 

To discover how LLMs answered general information queries on Australian political parties, we first created an AI Visibility Dashboard built on prompts such as “Can you explain what each Australian political party stands for?” and “Compare Australian political parties.”

In the table of results below, Labor came out on top as the most-mentioned party for these query types on all platforms but Australia’s most frequented LLM, ChatGPT, where the Greens came out on top by 1.8%. Across ChatGPT, Google AI Overviews, Grok, Perplexity, and Claude, Labor (20.8%), the Greens (18.7%), and the National Party (16%) ranked in the top three.

Further down, party rankings diverged sharply by platform. Each produced a materially different picture of the Australian political landscape even when they were asked the same questions.

Grok, for example, gave One Nation a 16.9% Share of Voice, where their average result on the other four platforms was half that, at 9.55%. Google AI Overview summaries also named just nine total political parties, while ChatGPT mentioned 21, allowing minor parties far more attention.

Top 10 Most-Mentioned Political Parties by AI Platform 

But what sources are these AI platforms referencing? And do those sources differ between platforms? Is a ChatGPT user getting different political information than a Claude user would?

By looking at top-cited sources by platform, we were able to find out, in a 1-week snapshot, which LLMs were letting parties describe themselves, and which prioritised more independent sources.

By industry, nonprofit domains were cited most, making up 28.3% of all sources. News and publishing domains were the second-most referenced sources at 24.4%, further proof of the clear link we’re seeing between earned media, AEO and GEO.

Drilling down into the specifics of these sources, the dominant insight from this analysis was the standout significance of Wikipedia. Across all five platforms, Wikipedia was the top-referenced source, holding a 13.8% share of the top-cited domains. Even as an information resource that makes “no guarantee of validity”, it is prioritised because it is structured, verified by humans, and constantly updated. ABC News and the Women for Election websites came in second and third place, with citations driven by explainer-type articles using many of the prompts’ key search terms. The Liberal Party, three parliament-owned websites, australianpolitics.com and buildaballot.org.au made up the rest of the top ten domains. Although the latter two domains suffer from low domain authority (56 and 22 respectively), their domain names most likely ranked high for query relevance.

ChatGPT was the only platform where its top sources consisted solely of party or parliament-owned websites, with answers drawn exclusively from government communications material. As the country’s most frequented LLM, this means that during this one-week snapshot at least, owned content was prioritised above earned media with no built-in check against independent sources. Its most-used URL was the Nationals’ ‘What We Stand For’ landing page.

Compared to ChatGPT, which cited just nine URLs in the ten-day timeframe, Google AI Overview summaries referenced 44 separate links, with a much larger presence of social media and video content. Together, YouTube, Facebook and Reddit content owned 22% of cited domains. Linked YouTube videos were either produced by official news sources like this video from Guardian Australia titled “Voting 101: Who are the Australian political parties clamouring for your vote?” or by content creators like AusPol Explained, who summarised the policies behind each party ahead of the 2025 Federal Election in this cited, 54-minute explainer. From Facebook, Google AI Overviews used four videos belonging to ABC News, SBS News, and 10 News. Cited Reddit threads included a 2-year old post on r/AskAnAustralian titled “Can someone explain the current political parties of AUS?”

Answers from X ’s AI assistant, Grok, also slipped into party self-citation, but with decent source variety otherwise, citing authoritative independent sources like SBS, The Guardian, Reuters and Greenpeace.

The same can be said for Perplexity and Claude. Perplexity skewed heavily to government websites and some voting tools, along with the same broadcaster staples, while Claude pulled information from mostly mainstream, Australian-focused references and public-broadcaster sources, with a smaller tail of less reliable material such as a crowd-edited fandom wiki and one fringe minor-party site.

To summarise the trends for each platform:

• ChatGPT was a clear outlier, describing each party through their owned content rather than pulling from independent secondary sources.

• Google AI Overview leaned on video/social platforms, even for a factual political query, using a mix of news media and influencer content.

• Grok and Perplexity drew heavily from institutional and civic sources, plus mainstream press. This suggests that the platforms were using more of a "how government works" framing rather than a "who are the parties" framing.  
  

• Claude weighted reference and encyclopedic sources most heavily. Wikipedia and Britannica alone made up 30% of citations, and it's the only platform with no party-run site in its top ten. However, it also had the shakiest outlier domains of any platform.

Top 10 Most-Cited Sources by AI Platform

Party Policy Strengths and Weaknesses, According to AI

When these same LLMs are asked about each political party’s policy strengths and weaknesses, mention volumes and sentiment trends change. Prompts like “What Australian political party is best for [POLICY]?” were used in multiple Streem AI Visibility Dashboards to observe these changes, with eight policy areas including Economy, Housing, Health & Welfare, Education, Environment & Energy, Crime, Infrastructure & Transport, and Security & Foreign Affairs.

Overall, sentiment roughly mirrored historical party-brand associations in Australian politics. The Liberal Party and Coalition led on the Economy and Crime in responses, while Labor and the Greens dominated mentions and positive sentiment on Health, Education, Environment, and Infrastructure.

Because AI platforms often prioritise relevance over recency, some of the sources used to make these calls on whether the Coalition handles the economy well, for example, were published up to eight years ago. Most of that older content is news media coverage.

For Economy-related queries, top-cited URLs included a 2023 Yahoo!Finance article written after the release of that year’s Federal Budget, a 2022 Sydney Morning Herald article, and even a 2018 article from Guardian Australia.

The data backing up the arguments in these articles may be outdated, but the keywords they are written with and the domain authority the articles are hosted will earn them AI citations if there are no better pieces of content, earned or owned, that relate to the queries being searched.

Most-Mentioned Parties by LLM Sentiment 

What This Means for All PR & Comms Teams

The trends we can see across these five leading LLMs aren’t limited to how Australian political parties are being discussed on this channel. The same dynamics apply to every organisation that has an audience using AI Search as a source of truth when making purchase decisions.

If almost half of Australians are using GenAI to seek information every week, this makes it a default channel for any brand in the country. Here are some takeaways from our analysis that apply to any PR or communications teams, not just the government relations professionals.

1. Owned content might perform well, but it's a fragile safety net. 

The number of party-owned URLs cited by LLMs in this analysis is good news for the communications teams curating marketing copy. Websites that have developed decent authority, a clear structure with scannable features like FAQ sections and newsrooms with recent press releases will win attention.

However, if AI is only referencing your owned content, your brand is missing out on external credibility signals. Not only this, platforms’ scraping behaviours have and will continue to change. In August 2026, Reddit lost a significant chunk of its visible citations in ChatGPT after OpenAI changed how the AI ranks sources. In this environment, going all in on one channel exposes you to risk if you’re trying to grow your brand’s AI Visibility. The best safety net is well-dispersed investment across social, owned, and earned content in particular to collect independent validation by authoritative voices.

2. LLMs can feel more positively about one area of your brand than another. 

Tracking your brand’s LLM sentiment is essential, but our analysis demonstrates that sentiment can split by topic area. Two users searching for a bank recommendation, for example, could receive completely different impressions of one organisation depending on the angle they’ve asked a question with.

With LLM sentiment analysis, start with where negative sentiment is being sourced from. Can you do outreach to those media outlets? Or, looking at your positive sentiment, can you do outreach to the outlets you’ve been successful with, and combat the key messages in the content you’re having issues with?

3. Don't ignore encyclopedic and reference sources.

For larger corporations who own a dedicated Wikipedia or Britannica page, these are some of your most important online representations. With informational queries, LLMs are defaulting to these sources because of their structure, the number of backlinks they receive, and their high domain authority. Treat the accuracy and maintenance of these pages as a genuine AEO and GEO priority if they are available to you.

4. You need to find the outdated media content that still counts.

If LLMs are citing 8-year-old news articles, communications teams need to know how to combat negative or inaccurate information if it’s shining through.

Old press coverage, outdated leadership biographies, stale ‘About Us’ pages, or abandoned blog posts can resurface as an authoritative answer if nothing fresher or better-optimised exists. Owned content decay is now a live reputational risk, especially with earned media coverage being more permanent than ever. Using a tool like Streem AI Visibility can help you measure your brand’s visibility and find the sources shaping what your customers are hearing about you.

Where to start with AI Visibility 

Your warmest leads are probably asking LLMs about you and your competitors. Often, it’s where they receive their first impression. But rather than pulling attention away from earned, social and owned media, the rise of AI Search has actually made integrated, cross-channel communications strategies more important.

To find direction for these strategies, organisations today need to understand their LLM source ecosystem. Where are ChatGPT, Claude, and other platforms sourcing information about you? What is the sentiment they’re distributing to your audience, and what outreach can you do to optimise how you’re appearing? If your organisation is looking into AI Visibility as a priority area, start by booking in a meeting with our team to explore how Streem AI Visibility is positioned alongside Media Monitoring, Social Listening, and Outreach in one simple workflow.