How social media conversation influences the market
An analysis of the finance industry post-GameStop
There’s no doubt the GameStop saga caused quite a stir within the finance industry and beyond. From a communications perspective, I’d like to reflect on the reputation of the finance industry and what’s changed as a result of the incident. I’d also like to explore the sentiment towards different financial institutions.
It’s important to delineate between investment banks, hedge funds (including HFTs and other obscure finance organisations) and commercial banks. For all the flak that commercial banks get, on the whole, individual banks are pretty good at communicating with consumers, maintaining their reputation and avoiding healthy skepticism turning into outright criticism.
The sentiment around GameStop
Both professionally and personally I am compelled to regularly read the comments under breaking news articles shared on social media, and no matter what the cause or issues, I cannot recall a single story’s response being so absolutely one-sided.
When Isentia did an analysis of social media conversation relating to the GameStop saga, it found 88% of comments expressed an opinion about the topic supported the insurgent Wall Street Bets group. Approximately 50% of comments expressed a clear statement that hedge funds were losing at their own game and deserved no sympathy. The positively gleeful schadenfreude at the losses sustained by the hedge funds unified groups that in any other contexts would be building strawmen to attack each other in a never-ending battle of replies. Commenters in support of Wall St Bets floated conspiracy theories of the Democratic Party’s and Joe Biden’s personal involvement, while others called for the beginning of a socialist revolution. Robinhood closing positions and restricting trading in GME united such disparate voices as Alexandria Ocasio-Cortez, Ted Cruz and Donald Trump Jr., a potential dinner party for the ages that was cut short by AOC reminding Cruz of his role in the January 6th Capitol storming.
This reaction shouldn’t be surprising to anyone. Recent real-life examples, like the GFC have built up doubt in financial institutions like hedge funds and investment banks, and cultural representations of these organisations tend to be negative.
Opinions of Wall Street
Looking at Hollywood films about investment bankers and hedge funds, we see a list of negative depictions; The Big Short, Wall Street: Greed is Good, Wolf of Wall Street, Margin Call and American Psycho. Indeed, it’s hard to find a positive depiction of Wall Street and high finance with the possible exception of the Pursuit of Happyness.
Opinions of ‘Wall Street’ in the 2017 US YouGov survey, found that 77% of people believed that “most people on Wall Street would be willing to harm consumers if they believed they could make a lot of money and get away with it”, 72% believed Wall St financiers were more greedy and selfish than regular people. By contrast, in 2018, only 66% of 18-24 year olds and 76% of 25-34 year olds said they have always believed the earth was round. Put another way, you would possibly have more success on social media, arguing for a theory of a flat earth, than you would for the idea that there are ethics on Wall Street.
But none of this is new, those surveys are years old, people still talk about the open wounds of the GFC in 2008 and American Psycho was released in 2000. It may have ebbed and flowed at times, but movements like Occupy Wall Street didn’t feel like a response a single moment, but looked to address long-standing grievances. People have long been cynical about hedge funds and investment banks, and it doesn’t appear much has been done by the industry to improve the situation. Thought pieces on public relations and branding express exasperation with the financial services sector, generally acknowledging early in the text; ‘we know you don’t like it but here’s why you need it’, much like a dentist explaining flossing.
And who could blame hedge funds and investment banks for being ignorant in the modern world of communications? They are non-consumer facing businesses. They don’t have issues like other non-consumer-facing businesses (mining companies) that regularly seek community and environmental project approvals and face a highly organised and powerful climate change movement. They also don’t have an easy story to tell. The mining sector’s blue-collar jobs and regional economies have a more convincing message than providing liquidity to financial markets and diversifying risk.
The power of social media conversation
So if the reputation hasn’t changed why would we suddenly talk about it from a communications perspective? Because the consequences have changed. The power of social media conversation and accessible trading platforms has meant that suddenly the poor reputation of these firms not only makes them a target for activist financial consequences, but also an entirely unsympathetic one (again, 88% of commenters on social media supported the actions of Wall Street Bets, and celebrated hedge fund losses). The question of regulating future market distortions such as GameStop is made politically complicated by this sentiment.
The other thing that’s changed; the messaging is clearer. People often distrusted Wall Street, but the usual attacks of income inequality and regulation tend to be murky and complicated. The frequent usage of the word democratisation is probably the most interesting development in the entire GameStop saga from a communication perspective.
What about regulation?
Anyone who has studied or read about behavioural economics and knows about experiments in the Dictator Game will readily understand there is an innate drive towards fairness in our dealings with others, and perceptions of unfairness encourage us to punish the perpetrators, even if it’s to our own detriment. Thus, generating a clear narrative of unfairness is a powerful tool to generate support for a cause. The traditional attacks of income inequality are often the flagship of anti-Wall Street or anti-finance messaging and are often blurred enough to create a reasonable level of debate. Even if everyone agrees that regulation isn’t working, surveys in the US show deep divides between whether people believe the problem is about not enough regulation, not adequate enforcement of existing regulation, or if it’s the wrong type of regulation. This disagreement makes it difficult to build a clear policy of reform.
Democratisation, however, feels like it is a more powerful and clearer message. Propelled into the limelight most clearly by the actions of Robinhood, it asks a question of fairness that is far harder to obscure or dispute. We can argue whether or not the game is rigged, but no one can deny it’s unfair if you’re not allowed to play.
What’s next for investment banks and hedge funds?
As a communications professional I am interested in the development of the narrative of democratisation and accessibility, and how those who support regulation in the post-GameStop era find ways to attack this sentiment. I am also interested to see how investment banks and hedge funds respond. They’re clearly starting with a difficult story to tell. While none of this has been a problem before, they now need to find a way to communicate with the wider public, particularly young people. They also need to shift a narrative that has existed for decades if not longer. In this partisan era one might avoid consequences from the antipathy of either progressives or conservatives, but the concentrated and focused antipathy of both, united by a simple and powerful message, is something to be concerned about.
Loren is an experienced marketing professional who translates data and insights using Isentia solutions into trends and research, bringing clients closer to the benefits of audience intelligence. Loren thrives on introducing the groundbreaking ways in which data and insights can help a brand or organisation, enabling them to exceed their strategic objectives and goals.
This report aims provides insight into the Banking Industry in Vietnam. We look into people's preferences into their customer experience; using either traditional or digital banks, we deep dive into the topics driving social conversations about the banking industry, and the top mentioned brands related to the banking industry in Vietnam.
We have explored the latest trends and unpacked the current situation faced by the digital banking industry in Vietnam.
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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Blog
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.
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.
