The Top 15 Social Media Monitoring Tools 2026

As generative AI reshapes how people research, learn, and make decisions, a new discipline is emerging: Generative Engine Optimization (GEO). Just as SEO helped brands get discovered on Google, GEO ensures they appear accurately and positively within AI-powered platforms like ChatGPT, Gemini, and Copilot. With millions of consumers now turning to generative engines instead of search engines, GEO is fast becoming essential for marketing and PR professionals looking to influence how their brand shows up in this new discovery layer, where conversations, not clicks, determine visibility.

What is generative engine optimization (GEO)?

Why should marketing & insights teams care about GEO?

How does GEO work? Key mechanisms & signals

How can you get started with GEO? Test your GEO readiness

Which media sources to prioritize for GEO

What's the difference between GEO and SEO?

8 essentials of generative engine optimization

Increase your GEO readiness with Meltwater

Generative Engine Optimization (GEO) is the practice of ensuring that AI platforms such as ChatGPT, Google Gemini, Llama, etc., provide accurate, positive, and up-to-date information about your brand when users ask questions. These platforms are referred to as Generative Engines, Answer Engines, or Large Language Models (LLMs). They are increasingly used by consumers to research purchases and make decisions.

GEO is a way of making sure that AI platforms give people the right information about a company’s brand and products. 

For example, if a person asks ChatGPT: “What is a good family car for camping trips?” then the auto manufacturers would want ChatGPT to suggest their SUV models, and list the benefits. To make sure that happens, the auto brands need to use GEO.

GEO ensures your brand shows up correctly within these AI-generated responses.

The practice of GEO involves several steps, but it is mostly focused on creating, structuring, and publishing content in the best way to influence the responses given by AI Generative Engines. 

In this article we’ll go into the detail of how GEO works, why it’s important, and how you can use it to make sure your brand is featured positively in AI platforms.

Sometimes, generative engine optimization (GEO) is referred to as answer engine optimization (AEO). Learn more about the differences and similarities of GEO and AEO, and take a look at the best AEO tools.

For a long time marketers have been able to rely on the fact that consumers almost exclusively use Google to research buying decisions. So they have been able to use Search Engine Optimization (SEO) to ensure their brand’s website appears in the organic search results, or use paid search advertising to buy ads on relevant search results. 

This is a highly effective way of targeting the right audience at the right time. But now things are changing.

LLMs aren't just another channel. They sit on top of this ecosystem. They synthesize everything into a single conversation.

Allie Konchar, Partner & Chief Client Officer, Omniscient

Changing user behavior: AI + conversational search

Increasingly, consumers are using tools like ChatGPT to research their buying decisions. Data from Meltwater’s 2026 Global Digital Report shows that ChatGPT.com is now the fifth most visited website on the web.

While search engines remain popular, many search queries now produce an AI generated summary that often provide enough information to divert users away from clicking through to websites, because their question has already been answered. McKinsey reports that around half of all Google search queries already produce AI-generated summaries, which can lead to zero-click searches, and that number keeps rising. 

With conventional web search, consumers are performing research manually, trying to find trustworthy sources and sifting through information to reach the answers they seek. But with Generative Engines, the AI is performing the research automatically, using its existing base knowledge alongside supplemental information pulled from more recent online sources. A search engine shows users where to find answers, while a Generative Engine just gives them the answer. 

For marketers, the key takeaway is that few top-of-funnel consumers are visiting your website, because during the awareness phase their information needs are being met by Generative Engines. This makes it more difficult to move them down into the consideration phase. 

TIP: For more data around growing AI usage and online consumer behavior, download our 2026 Global Digital Report, featuring nearly 700 pages statistics to help inform your GEO strategies next year.

You already know that everybody is talking about AI, and all of your competitors are having the same discussions as you about what it means for their marketing strategy. Those who move decisively to implement GEO, ensuring their brands are represented clearly and positively in Generative Engines, will capture the largest share of those consumers who start their buying journey with an AI query.

If you’re not optimizing for generative engines, you just might never reach your audience at all.

Kelly McKenna, Regional Director, Account Management at Meltwater

Those who ignore GEO will lose ground, as growing numbers of consumers turn from search engines to Generative Engines, the size of the audiences that can be reached through conventional search marketing will decrease. 

How do generative engines (LLMs) surface content?

It’s important to understand that while Generative Engines are based on similar technology, they all work a little differently. Broadly speaking they all use two different sources of knowledge to provide an answer to a query: 

<table class="table-primary alternate-color">
  <caption>How Generative Engines (LLMs) Surface Content</caption>
  <thead>
    <tr>
      <th>Generative Engine</th>
      <th>Primary Retrieval Method</th>
      <th>Sources Most Often Cited</th>
      <th>Sources Less Likely to Be Cited</th>
      <th>Notes on Behavior</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>ChatGPT</td>
      <td>Bing search + internal training data</td>
      <td>Reuters, AP News, Wikipedia, Encyclopedic sources</td>
      <td>Social media platforms (X/Twitter, Instagram)</td>
      <td>Prefers authoritative, neutral, structured sources with high trust signals.</td>
    </tr>
    <tr>
      <td>Google Gemini</td>
      <td>Google Search + YouTube + Knowledge Graph</td>
      <td>Reddit, YouTube, Google-indexed blogs</td>
      <td>X/Twitter</td>
      <td>Strongly favors user-generated content, community threads, and Google-owned properties.</td>
    </tr>
    <tr>
      <td>Perplexity</td>
      <td>Real-time web crawling + citation-first retrieval</td>
      <td>News publishers, reputable blogs, research papers</td>
      <td>Low-trust UGC</td>
      <td>Most transparent engine — always cites sources and updates in real time.</td>
    </tr>
    <tr>
      <td>Claude</td>
      <td>Internal training data + selected retrieval</td>
      <td>Academic sources, reputable news, long-form articles</td>
      <td>Short-form social media</td>
      <td>Prefers long-form text and well-structured explanatory content.</td>
    </tr>
  </tbody>
</table>

Training data: All LLMs begin by being trained on a huge volume of existing content and data — we’re talking about dozens of terabytes (a terabyte is 1,000Gb). This training data comes from a wide variety of sources (including your website), and gives the LLM its base knowledge about the world. The LLM does not have any knowledge of anything that happened after its initial training. 

Live Web Search: To overcome the knowledge cut-off problem, the training data is supplemented with fresh information gathered from the internet. If the Generative Engine thinks that a user’s query would benefit from more up to date information than it already has, it fetches new content from trusted online sources and works that into its response. Google uses its own search engine to find fresh information, while ChatGPT uses various sources, including Microsoft Bing.

It’s also important to note that AI does not quote any of this content verbatim in its responses. Much like a human, it uses the information it has learned to inform its own answer, and that can include information from various sources, including the press, competitors, consumer reviews. So don’t expect it to always be on-message!

Another key point is that unlike a search engine, an LLM does not have an index of content which it searches to find answers. It would be impossible to search ChatGPT for all mentions of your brand — that’s simply not how it works. 

An LLM is more similar to a human brain, in that it has a lot of knowledge stored in patterns and relationships between words and concepts. It is extremely difficult, verging on impossible, to examine and understand the detail of an LLM’s learned knowledge because of the sheer scale and complexity; this is a challenge referred to as Black Box AI. 

But we do understand how Generative Engines work at a high level, and we can analyze the kind of responses they produce for relevant queries, which gives us insight into how we should optimize our content for increased visibility.

Tip: Learn more about the citation layer.

What content features improve GEO-visibility?

Bear in mind that when you’re optimizing content for Generative Engines, the goal is to write copy that is easier for AI to digest and incorporate into its responses.

Clarity: Avoid overly elaborate language. Do not use corporate-jargon. Keep it simple, professional, and to the point. Leave no room for ambiguity. 

Authority: Wherever possible back up your statements with data from reputable sources, quotes from experts or industry leaders, and reference-able proof points. 

Structure: Organize your pages well with clear titles, headings, and subheadings. Use heading tags (H1, H2, etc) correctly to specify the hierarchy of your content. 

Recency: AI platforms look for more recent information from trusted sources to supplement their base knowledge, so in both owned and earned media, aim to publish fresh content recently.

In many respects GEO shares principles with good SEO practice. If you write consistently high quality content which addresses the needs of your target audience, and structure it correctly on well designed, standards compliant web pages, you will succeed.

Reading tip: Learn how a website rebuild drove a 73% citation increase.

Test your GEO readiness: a 2-minute self-assessment.

First you need to understand your starting point. Meltwater’s GenAI Lens is a tool that gives brands insight into how they are discussed by all major Generative Engines, including ChatGPT, Claude, Gemini, Perplexity, Grok, and Deepseek. 

With GenAI Lens you’ll be able to understand how your brand is already featured in LLMs, what source websites are used in those responses, and whether outdated or inaccurate information is being used. 

We know that LLMs pull content from the web, which they use to inform their responses. So whatever is said about your brand online will significantly affect how Generative Engines talk about your company. 

Start with your own website, this is the first place LLMs will look for information about your organization. But also focus on trusted, high authority third party sites like the mainstream news and specialist media within your industry, because these are the kinds of sources that LLMs will use. GenAI Lens can show you which websites the leading LLMS use to source information about your brand. 

Once you have a comprehensive audit of how your brand is currently portrayed online, you can perform a gap analysis against your desired messaging, and build a plan for how to reach that objective.

Tip: AI brand monitoring, AI media monitoring, and LLM monitoring will help you understand your online footprint. Learn how to track your LLM visibility and how to track LLM brand mentions.

An important difference between GEO and SEO is understanding how people search differently when using Generative Engines. Users interact conversationally with AI, and are likely to ask more complex questions in natural language, compared to simple keywords or short queries in a search engine. So it’s useful to get an idea of what kind of questions people will ask about your brand or industry, to help plan your GEO content.

Tip: Learn more about AI visibility for retail brands.

Unlike search engines, at present the leading Generative Engines do not share data on popular user queries, so it’s not easy to find out exactly what people are asking about your business. But you can look at popular keyword searches associated with your brand, and brainstorm the most likely questions people will ask Generative Engines. 

You can also use a social listening platform, like Meltwater Explore+ to analyze social media conversations around your brand and industry, to gain insight into common questions. Once you have mapped out all of the most likely questions people might ask, you can start creating content that answers those questions.

Tip: Learn how to connect social listening with generative engine optimization. 

Conventional SEO was built around search queries but GEO requires thinking in concepts, context, and authority — you need to focus on the intent of the query, rather than simply trying to match keywords. 

LLMs prioritize sources that provide comprehensive, well-structured, and contextually rich content. Focus your content strategy on:

Educational depth: Long-form resources that answer questions end-to-end. Publish explainers, FAQs, and data-backed insights that answer “how” and “why.”

Credibility signals: Citations, quotes, and data from reputable sources (analyst studies, thought leadership, or trusted media).

Rich media: Generative Engines pull information from videos and images as well as text content, so make use of varied content formats. 

Consistency across channels: LLMs learn from media, social, and owned content together; ensure your brand narratives are aligned across them.

The key thing is to ensure that your content is genuinely helpful in answering those questions you mapped out. If your content is too focused on sales messages, it’s less likely to be used by the LLMs.

TIP: Discover innovative AI-generated content examples to enhance your digital strategy.

You can optimize the content published on your website and owned channels, but LLMs also source information from trusted third party websites. That means digital PR has a key role to play in ensuring your brand is featured in high authority earned media, including news features, expert comment from your spokespeople, product reviews, and any other kind of brand story traditionally generated through a media relations campaign.

Tip: Learn more about the importance of earned media for GEO and about PR Pitch GEO optimization.

The tricky part is that unlike advertising, which can tell exciting and attention grabbing stories, PR professionals are limited to working only with the facts. But while you cannot always control the outcome of media outreach activity, ensuring that PR and GEO are aligned will help increase the volume of third party sources that feature the right messaging.

Tip: Learn how GEO is reshaping PR strategies.

Meltwater’s GenAI Lens gives you the ability to track your GEO performance over time, so you can understand whether your strategy is working well and spot areas where you need to improve. The tool provides visualizations that show brand sentiment, emotion, key phrases, people, products and things mentioned, alongside citations.

Armed with this information you can identify gaps in your GEO strategy where you might need to create more or better content. You can also discover which third party sources have the greatest impact on your GEO performance, and use that information to optimize PR outreach to focus on the most important media. 

Like SEO, this is a process of constant iteration and improvement. GEO should not be seen as a one-off project that will generate positive results in perpetuity, but rather an ongoing activity that needs to be integrated with the broader marketing mix.

Tip: Learn How to Measure PR Performance Across LLMs and Generative Search, understand how to report on LLM visibility, and take a look at executive report for AI search.

When an LLM processes a user query, it can often detect that the response might require more recent information than it has access to in its original training data. In that situation it will look online for more up to date content that will help it to form a more accurate answer, but where exactly do Generative Engines look for this information?

At Meltwater we used GenAI Lens to research which content sources the leading AI platforms were most likely to reference in their responses, and there were distinct differences between them all:

Open AI ChatGPT: Heavily favors text-based news and newswires (Reuters, AP, PR Newswire) and reference sites (Wikipedia). Much less likely to cite social media and UGC. 

Google Gemini: Leans towards high quality UGC from sites like Reddit and YouTube, as well as other online sources within the Google ecosystem. Does not index X/Twitter content. 

Google Search AI Overviews: Highly focused on UGC from Reddit and YouTube, as well as Facebook, Yahoo and LinkedIn. 

xAI Grok: Strongly skewed towards conversational UGC from X and Reddit. Less likely to cite traditional reference sources like Wikipedia, YouTube or academic sites. 

Unlike SEO, where most practitioners were happy to optimize only for Google, in GEO we have to consider the nuances of several different platforms; at the very least ChatGPT and Google AI Overviews. In short, it’s good practice to ensure you have up to date content propagated across a wide range of platforms where LLMs might look for supplemental information.  

In terms of which online sources are most likely to be referenced overall, we looked at several categories and charted the most frequently cited websites:

GEO helps your brand to appear in AI platforms — but how does it work? This guide has everything you need to know to get started with GEO.

What is Generative Engine Optimization (GEO) & How to Do It

<table class="table-primary alternate-color">
  <thead>
    <tr>
      <th>Film</th>
      <th>Defining conversation theme</th>
      <th>Standout finding</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td><em>Spider-Man: Brand New Day</em></td>
      <td>Plot and spoilers</td>
      <td>Plot-related discussion represented 26.4% of conversation</td>
    </tr>
    <tr>
      <td><em>The Odyssey</em></td>
      <td>Controversy</td>
      <td>Controversy accounted for 25.7% of conversation</td>
    </tr>
    <tr>
      <td><em>Toy Story 5</em></td>
      <td>Merchandise and franchise activity</td>
      <td>Fan content over-indexed most strongly on engagement</td>
    </tr>
    <tr>
      <td><em>The Devil Wears Prada 2</em></td>
      <td>Fashion and premieres</td>
      <td>Fashion and beauty represented 23.7% of conversation</td>
    </tr>
    <tr>
      <td><em>Obsession</em></td>
      <td>Plot interpretation and verdicts</td>
      <td>Conversation peaked on day 21</td>
    </tr>
  </tbody>
</table>

It’s a truth universally acknowledged: film discourse online is rarely just about the movie. Conversation doesn’t start and stop within the two-hour runtime. It extends from early promotion and teasers to post-premiere discussions as the trends mature, and memes and spoilers begin to proliferate.

There are trailer reactions, red-carpet looks to review, fan edits, merchandise drops, box-office numbers, character discourse, and that one extremely determined Reddit thread explaining why everyone else misunderstood the ending. All of it counts as conversation. But all of it can be interpreted differently depending on where it occurs and how it is measured.

Meltwater analyzed online discussion around five major 2026 theatrical releases: Spider-Man: Brand New Day, The Odyssey, Toy Story 5, The Devil Wears Prada 2, and Obsession. The analysis captured 2.47 million posts from social platforms, forums, blogs, and comment sections.

The posts that traveled furthest were often clips, fan edits, and other highly visual content. The discussions where people picked apart endings, performances, characters, and whether they would recommend the film were much less likely to appear among the most engaging posts.

And each film developed its own unmistakable online personality along the way.

The movie content that travels furthest isn’t where audiences form their verdict

Opening weekends usually make noise, but achieving staying power is different story

Every movie had its own conversation personality

Plot debates stay in the group chat while fan edits travel

A negative post doesn’t always mean a bad review

How Meltwater Explore+ helps marketers understand the whole audience conversation

FAQ: How audiences talk about major movie releases

<table style="width:100%; border-collapse:collapse; font-family:Arial, sans-serif; font-size:14px;">
  <tbody>
    <tr>
      <td style="padding:12px; border:1px solid #ccc; font-weight:bold; width:30%; font-size:14px;">Capability</td>
      <td style="padding:12px; border:1px solid #ccc; font-size:14px;">
        <a href="https://www.meltwater.com/en/capabilities/social-listening" style="font-size:14px;">Meltwater Social Listening</a>
      </td>
    </tr>
    <tr>
      <td style="padding:12px; border:1px solid #ccc; font-weight:bold; font-size:14px;">Analysis window</td>
      <td style="padding:12px; border:1px solid #ccc; font-size:14px;">
        Each film is analyzed within a 21-day window: the seven days before release, release day itself and the thirteen days after.
      </td>
    </tr>
    <tr>
      <td style="padding:12px; border:1px solid #ccc; font-weight:bold; font-size:14px;">Data sources</td>
      <td style="padding:12px; border:1px solid #ccc; font-size:14px;">
        Social platforms, forums, blogs and comment sections
      </td>
    </tr>
    <tr>
      <td style="padding:12px; border:1px solid #ccc; font-weight:bold; font-size:14px;">Brands / entities tracked</td>
      <td style="padding:12px; border:1px solid #ccc; font-size:14px;">
        Spider-Man: Brand New Day, The Odyssey, Toy Story 5, The Devil Wears Prada 2, Obsession
      </td>
    </tr>
    <tr>
      <td style="padding:12px; border:1px solid #ccc; font-weight:bold; vertical-align:top; font-size:14px;">How quantitative data was analyzed</td>
      <td style="padding:12px; border:1px solid #ccc; font-size:14px;">
        Conversation volume, platform mix, overall sentiment, and conversation momentum were measured across all 2,473,997 posts in the five 21-day analysis windows. Using the same timeframe for each film allowed us to compare the size and shape of conversation consistently, including when discussion peaked, how quickly it declined, and how much activity remained one and two weeks after release.
      </td>
    </tr>
    <tr>
      <td style="padding:12px; border:1px solid #ccc; font-weight:bold; vertical-align:top; font-size:14px;">How qualitative data was analyzed</td>
      <td style="padding:12px; border:1px solid #ccc; font-size:14px;">
        <p style="font-size:14px;">To understand what audiences were actually discussing, we analyzed a classified sample for each film consisting of:</p>
        <ul style="font-size:14px;">
          <li style="font-size:14px;">The 2,000 highest-engagement social posts</li>
          <li style="font-size:14px;">2,000 additional social posts selected at random from below that group</li>
          <li style="font-size:14px;">The 2,000 highest-engagement Reddit posts</li>
        </ul>
        <p style="font-size:14px;">Posts in these samples were coded by topics such as plot and endings, performances, controversy, merchandise, fashion, and fan-created content, allowing us to examine the themes and reactions behind the broader quantitative trends.</p>
      </td>
    </tr>
    <tr>
      <td style="padding:12px; border:1px solid #ccc; font-weight:bold; vertical-align:top; font-size:14px;">Key term definitions</td>
      <td style="padding:12px; border:1px solid #ccc; font-size:14px;">
        <p style="font-size:14px;"><strong>Engagement</strong>: the number of shares, comments, likes, retweets, replies, direct messages, etc.</p>
        <p style="font-size:14px;"><strong>Post</strong>: a single piece of content, can include multiple mentions</p>
        <p style="font-size:14px;"><strong>Mentions</strong>: the number of instances when key words and phrases appeared within a post</p>
        <p style="font-size:14px;"><strong>Share of Engagement (SOE)</strong>: the percentage, or share, of total engagement actions attributed to a particular brand, market, or entity</p>
        <p style="font-size:14px;"><strong>Subreddit</strong>: a user-created community on Reddit dedicated to a particular interest or topic</p>
      </td>
    </tr>
  </tbody>
</table>

A note on qualitative analysis: Samples were used differently depending on the question being asked. High-engagement social and Reddit posts were kept separate when comparing platforms or engagement. Estimates of each topic’s share of the broader conversation combined the random-social and Reddit samples, weighted according to each platform’s share of the film’s full conversation; the engagement-ranked social sample was excluded because it disproportionately represents content that generates interaction. Topic categories could overlap, so a single post could contribute to more than one theme.

Download a one-page PDF summarizing the findings of this analysis.

The most engaging movie content and the most revealing audience discussion occupy surprisingly different corners of the internet.

Reddit makes that divide hard to miss. Depending on the film, Reddit accounted for anywhere between 15.9% and 49.4% of total conversation, yet it represented no more than 0.1% of any film’s 2,000 most engaging posts. For example, almost half of the entire Odyssey conversation happened on Reddit, but none of those posts appeared in its highest-engagement social sample.

Content built to travel: short-form video, clips, fan edits, fan art, promotional moments, and tie-ins.

That makes engagement rankings useful for answering questions like, “What is getting shared?” or “Which creative is breaking through?”, but they are considerably less useful for answering, “What did audiences actually think?”

The latter tends to surface in discussion-heavy spaces, where viewers have enough room to argue about an ending, question a character decision, dissect a performance, or simply tell several hundred strangers exactly why everyone else is wrong.

Why it matters for marketers: Engagement is a measure of amplification, not a universal proxy for audience opinion. If campaign reporting looks only at the content with the biggest numbers, marketers can end up measuring what audiences helped circulate while missing the conversations that explain how the campaign or release actually landed among the audiences discussing it in depth.

Four of the five films followed the release pattern you might expect: conversation peaked on release day, then began to fall.

How quickly it tapered off was another matter.

By day 21, The Devil Wears Prada 2 had dropped to 13% of its peak conversation volume. Spider-Man: Brand New Day was at 21%, while The Odyssey and Toy Story 5 retained 29% and 30%, respectively. The films may all have experienced a release-day spike, but their conversations did not have the same shelf life.

Its release day was only its fourth-busiest day. Conversation dipped midway through the analysis window and then started climbing again, eventually reaching its highest level on day 21 — the final day measured. Its second week retained 97% of first-week conversation volume, a feat not achieved since Sound of Freedom as one Redditor astutely observed.

The subject matter helps explain what made that film’s conversation look so different. Plot, endings (yes, plural), and spoilers accounted for 34.4% of Obsession discussion, the highest share among all five films. Verdicts reached 26.9%, while themes and meaning accounted for 17.8% — more than three times the share for any other title.

In other words, audiences did not simply encounter Obsession and move on. They found things to argue (ahem, have spirited discussion) about.

Why it matters for marketers: A launch spike shows that a campaign captured attention. Staying power shows whether audiences continued giving the subject reasons to matter. Tracking both makes it easier to distinguish a large opening moment from a conversation with a life of its own.

When you put five major releases into the same measurement framework and their differences become much more interesting and apparent than a simple ranking of who had the most mentions.

To get an estimate of which themes stood out for each release, we used a qualitative analysis of random social and Reddit posts by topic, then weighted the results to reflect each platform's share of the overall conversation.

Spider-Man: Brand New Day was a plot-driven movie discussion. Plot, the ending, and spoilers represented 26.4% of its conversation, second only to Obsession. It also generated the highest share of box-office and trade discussion of the five titles, at 16%.

The Odyssey was the controversy movie — by a very wide margin. Controversy represented 25.7% of its conversation, compared with 2.6% or less for every other film. Much of that centered on historical accuracy, while discussion of craft and production also reached a dataset-high 13.1%.

Toy Story 5 had the most commercial conversation mix. Merchandise and tie-ins were its largest topic at 17.2%, followed closely by cinemas and tickets at 13.9% and nostalgia and legacy at 13.5%. That gave the conversation a distinctly franchise-driven feel, shaped as much by the world around the movie as by reactions to the story itself.

For The Devil Wears Prada 2, the clothes did a lot of the talking. Fashion and beauty accounted for 23.7% of conversation and premieres and events another 20.4%. Plot, endings, and spoilers reached only 5.3% — the lowest of any film in the analysis. Audiences spent considerably more time discussing how the sequel looked than what happened in it.

And Obsession was the title audiences wanted to pick apart. Plot and endings led at 34.4%, verdicts reached 26.9%, and themes and meaning hit 17.8%. Its conversation was built far more around interpretation than promotion.

These identities were certainly not always fixed. 

Toy Story 5 moved from merchandise before release to verdicts immediately after release, back to merchandise, and eventually to fan-created content in week two. 

The Devil Wears Prada 2 moved from premieres to fashion and finally toward box-office discussion. 

By contrast, The Odyssey remained controversy-led through most of its release window, while Obsession quickly became dominated by plot discussion.

The stage at which you examine a campaign can therefore change the story you tell about it.

Why it matters for marketers: Total volume tells you how much attention something received. Topic mix tells you what that attention was actually about. Following those topics over time can also reveal whether the campaign narrative held, disappeared, or gave way to something audiences found more interesting.

If engagement is your main goal, give the fans something to remix.

Fan edits and fan art generated a larger share of engagement than their share of posts for every film analyzed. For The Odyssey, that gap was +1.0 percentage point — meaning fan content accounted for 1 percentage point more of total engagement than other posts. For Toy Story 5, the gap widened to +13.0 points. Clips posted with little or no commentary showed the same pattern, over-performing across all five films.

The topics on which people expressed deeper opinions did almost the opposite.

Plot, endings, and spoiler discussion generated less engagement than their share of posts for every film. So did discussion of performances. Even when plot debate was a major part of the conversation, as it was for Spider-Man: Brand New Day, the subject still underperformed on engagement, relative to how often people talked about it.

That difference grows increasingly important later in a release cycle. Fan edits and art increased from 4.4% of sampled conversation before release to 13.8% by week two. Fan activity can therefore remain highly visible even when the overall volume of conversation around the film is declining.

That makes fan content a useful momentum signal. But by itself, it doesn’t tell us why that momentum exists. So while useful, it needs to be examined carefully in conjunction with other metrics.  

Why it matters for marketers: Highly engaging fan content can show which creative ideas audiences want to carry forward themselves. Use it to identify formats and cultural moments worth amplifying, but pair it with broader conversation analysis before using engagement as evidence of how audiences feel.

Sentiment analysis is a useful barometer, but those in the entertainment industry should be wary: it gets messy when people are discussing fictional characters that they love to hate, or genres designed to inspire negative emotions such as fear or unease. 

To understand what was behind the negative scores, we examined posts from the three qualitative samples for each film: 

A random sample of additional social posts

We then assessed whether the negativity was directed at the film itself, its characters, the cinema experience, or something else entirely.

Across all five releases, Reddit conversation was substantially more negative than the highest-engagement social content. Toy Story 5, for example, moved from 5% negative sentiment among its most engaging social posts to 28% among its top Reddit posts. Spider-Man: Brand New Day moved from 6% to 22%. Obsession jumped from 19% to 46%.

But much of that negativity was not necessarily a thumbs-down on the movie.

For Obsession, half of the negative posts centered on characters, while fewer than one in five offered an actual verdict on the film. Spider-Man followed a similar, though smaller, pattern. Being furious with a character can be evidence that a story has done its job rather than evidence that the audience hated watching it.

The horror genre presents an even tougher problem. Well-meaning adjectives, that are actually a mark of praise, can sound terrible. Words such as “bleak,” “brutal,” and “disturbing” may describe exactly the experience a viewer wanted, but conventional sentiment analysis has little reason to interpret them as compliments. Four in five of the negative posts using this kind of language belonged to Obsession.

And sometimes the movie itself is an innocent bystander swept up in a negative discussion it, or its content, has no control over. Complaints about ticket prices, projection problems, sound levels, or another person behaving badly in the cinema can all appear inside the surrounding conversation and contribute to a negative score.

Why it matters for marketers: A sentiment score can tell you where to investigate, but context tells you what to do about it. Before immediately treating negativity as a reputation problem, determine whether audiences dislike the subject itself, are passionately debating it, or are reacting to something else entirely.

How Meltwater social listening helps marketers understand the whole audience conversation

The lesson to be learned from the online conversation around these five summer films extends well beyond entertainment. Marketers in all industries should take a shrewd approach to assumptions based on the biggest numbers — those figures do not necessarily provide the most useful insights.

Meltwater’s social listening capabilities help marketers analyze conversation across multiple sources and turn those signals into actionable intelligence, making it easier to understand not only what is getting attention but what that attention means. Understanding that allows teams to move deftly from gathering data to making strategic decisions.

See beyond the posts with the biggest numbers

Engagement rankings can favor highly shareable content while leaving discussion-heavy environments almost invisible.

Analyze conversations across social platforms, forums, news, reviews, and other sources rather than relying on a single engagement-ranked feed.

Compare conversation volume with engagement to identify the difference between what audiences discuss in depth and what they amplify.

Examine individual sources and communities to understand where different types of audience response are happening.

Business outcome: Build a more representative view of audience response and reduce the risk of mistaking high visibility for broad approval.

What dominates before a launch may have little resemblance to what audiences care about days or weeks later.

Track conversation volume over time to see when attention rises, peaks, and fades.

Analyze themes and sentiment as they develop to identify what is sustaining the conversation.

Compare periods to see when campaign messaging gives way to audience-led topics or emerging narratives.

Business outcome: Make decisions based on the conversation audiences are having now rather than the one they were having at launch.

Turn sentiment and engagement into context

A negative score or high-engagement post can tell teams that something happened without explaining what drove the response.

Analyze sentiment alongside the themes and conversations surrounding it.

Identify the subjects, audiences, and sources contributing to changes in perception.

Meltwater analyzed nearly 2.5 million posts about Spider-Man: Brand New Day, The Odyssey, Toy Story 5, The Devil Wears Prada 2, and Obsession to uncover what gets audiences talking — and why the posts with the most engagement don’t always tell you what viewers actually think.

What 2.47 Million Posts Reveal About 2026 Summer Movie Audiences

Talkwalker, now Lumen by Talkwalker, added LLM Insights for tracking brand mentions across ChatGPT, Gemini, Claude, and Perplexity. So, the decision is less about whether a platform tracks AI and more about coverage, depth, workflow, and cost.

You may need stronger earned-media monitoring, deeper consumer research, a simpler tool for a smaller team, or a different approach to tracking your brand across AI-generated answers.

Here are five Talkwalker alternatives to compare based on those needs.

What are the best Talkwalker alternatives?

The five strongest options to consider are:

These five platforms cover different needs, from enterprise consumer intelligence to more accessible brand monitoring. We also compare their AI visibility capabilities, since tracking how your brand appears in AI-generated answers is becoming part of the evaluation.

Talkwalker itself added LLM Insights in January 2026, so the question is now less about who offers AI monitoring and more about how each platform handles it alongside social listening.

If earned media monitoring matters as much as social conversation, also compare the broader range of media monitoring tools available.

Comparison of the best Talkwalker alternatives

<table class="table-primary alternate-color">
  <thead>
    <tr>
      <th>Tool</th>
      <th>Best for</th>
      <th>G2 score</th>
      <th>Key differentiator</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Meltwater</td>
      <td>Enterprise media intelligence</td>
      <td><a href="https://www.g2.com/products/meltwater/reviews?source=search">4.1</a></td>
      <td>Native AI visibility tracking via GenAI Lens</td>
    </tr>
    <tr>
      <td>Brandwatch</td>
      <td>Consumer intelligence at scale</td>
      <td><a href="https://www.g2.com/products/brandwatch-social-media-management/reviews?source=search">4.0</a></td>
      <td>Historical data archive dating back to 2008</td>
    </tr>
    <tr>
      <td>Sprinklr</td>
      <td>Enterprise CXM + social listening</td>
      <td><a href="https://www.g2.com/products/sprinklr-social/reviews">4.1</a></td>
      <td>Social listening bundled into a full CXM suite</td>
    </tr>
    <tr>
      <td>Synthesio</td>
      <td>Enterprise consumer research</td>
      <td><a href="https://www.g2.com/products/synthesio/reviews?source=search">4.3</a></td>
      <td>AI-powered topic modeling for post-purchase behavior</td>
    </tr>
    <tr>
      <td>Brand24</td>
      <td>Small and midsize teams</td>
      <td><a href="https://www.g2.com/products/brand24/reviews?source=search">4.6</a></td>
      <td>Self-serve setup, no sales cycle required</td>
    </tr>
  </tbody>
</table>

Meltwater combines media monitoring, social listening, and consumer intelligence in one platform, plus native AI visibility tracking through GenAI Lens. 

In practice, that means you can track what journalists and customers are saying about your brand, spot shifts in sentiment or competitor conversations, and see how your brand appears in tools like ChatGPT, Gemini, and Perplexity. GenAI Lens also shows how often you appear and which sources are influencing those AI answers.

Best for: PR, marketing, and communications teams that need media, social, and AI monitoring in one platform.

Real-time media monitoring across 270,000+ news sources, 15+ social channels, TV, radio, and podcasts.

Mira, Meltwater's AI assistant, summarizes large volumes of conversation and helps teams build searches faster.

GenAI Lens monitors brand visibility across ChatGPT, Gemini, Claude, Perplexity, Grok, DeepSeek, and other AI platforms, with prevalence, competitive benchmarking, and source tracking.

Media relations tools help teams identify journalists and manage outreach alongside monitoring.

Cross-team capabilities give PR teams real-time alerts, marketers social performance data, and insights teams audience analysis.

Pricing: Meltwater uses custom pricing based on products, data requirements, and team needs. Explore Meltwater’s pricing and plans, or read our detailed pricing guide to learn more before requesting a tailored quote.

Strong journalist and media relations tools

Social proof: Haleon used Meltwater's social listening to cut market research turnaround from five weeks to five days (an 80% efficiency gain) and drove 272% year-over-year engagement growth for its Preparation-H brand by acting on the conversation data it uncovered.

Brandwatch is suitable if your team runs large-scale consumer research. Its Consumer Research platform processes data from more than 100 million sources and provides access to a historical archive containing over a trillion consumer conversations.

For AI and search intelligence, Brandwatch connects with Trajaan, which became part of parent company Cision. Brandwatch surfaces Trajaan data inside Consumer Research alongside social signals.

Best for: Enterprise consumer intelligence teams that need deep, high-volume conversation analysis.

Coverage across 100+ million online sources, including a historical archive dating back to 2008.

AI-powered sentiment and trend analysis built for research-heavy teams.

LLM/AI visibility tracking through Trajaan, a search intelligence platform Brandwatch's parent company, Cision, added through a partnership and later acquired.

Pricing: Custom quote; no public self-serve tier.

AI visibility comes through Trajaan, which is a separate module with its own setup.

Sprinklr fits your team if you want consumer intelligence connected to a much broader customer experience platform. Its listening tools can pull signals from social, digital, media, reviews, and first-party customer data.

Sprinklr has also introduced LLM Insights, extending that analysis to AI-generated answers.

Best for: Large enterprises that want social listening bundled inside a broader customer experience management platform.

Social and digital listening across 30+ channels, alongside extensive media and web coverage.

AI-powered analysis for customer sentiment, emerging topics, anomalies, and customer trends.

Competitive benchmarking across brands, markets, and campaigns.

Social Listening

What 2.47 Million Posts Reveal About 2026 Summer Movie Audiences

The Best Talkwalker Alternatives for Social Listening

Best Brandwatch Alternatives for AI Visibility (2026)

10 Best Brand Tracking Software & Tools

Black Friday and Cyber Monday Consumer Trends 2026

Competitor Product Launch Tracking: Monitoring Competitor Feature Releases

Trendspotting: meaning, examples, and tips

The Top 15 Social Media Monitoring Tools 2026

Executive Marketing Reporting: What Executives Care About

How AI Recommends Saving Money on Groceries: Analysis of 8 Leading AI Models

How to Use Meltwater to Identify Messaging Driving Engagement

Best Sentiment Analysis Tools for PR & Marketing 2026

Social Listening

What 2.47 Million Posts Reveal About 2026 Summer Movie Audiences

The Best Talkwalker Alternatives for Social Listening

Best Brandwatch Alternatives for AI Visibility (2026)

10 Best Brand Tracking Software & Tools

Black Friday and Cyber Monday Consumer Trends 2026

Competitor Product Launch Tracking: Monitoring Competitor Feature Releases

Trendspotting: meaning, examples, and tips

The Top 15 Social Media Monitoring Tools 2026

Executive Marketing Reporting: What Executives Care About

How AI Recommends Saving Money on Groceries: Analysis of 8 Leading AI Models

How to Use Meltwater to Identify Messaging Driving Engagement

Best Sentiment Analysis Tools for PR & Marketing 2026

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