<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

Sale prices and brand offerings have always been under the microscope during Black Friday Cyber Monday (BFCM), but in 2026 consumers are talking about much more than just discounts.

To understand how consumer expectations and conversations are changing, we used our Meltwater Social Listening capability to analyze 5.67 million posts from August 2025 to July 2026 about the shopping event across social platforms, forums, blogs, product review sites and comment sections.

That conversation (70.6% of which occurred in November 2025) revealed where brand reputation risks can emerge, as well as what inspires consumers to complain about or praise their shopping experiences. Here’s what the data tells marketers about preparing for BFCM 2026.

Download a one-page summary of our findings and explore the social listening capabilities that powered this analysis.

Key Black Friday and Cyber Monday consumer insights

Black Friday and Cyber Monday 2026 guidelines for brands

Black Friday and Cyber Monday 2026 consumer insights FAQ

<table class="table-primary">
<thead>
<tr>
<th>Methodology</th>
<th>Details</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>Capability</strong></td>
<td><a href="https://www.meltwater.com/en/capabilities/social-listening" target="blank">Meltwater Social Listening</a></td>
</tr>
<tr>
<td><strong>Analysis window</strong></td>
<td>1 August 2025 to 31 July 2026</td>
</tr>
<tr>
<td><strong>Data sources</strong></td>
<td>Social platforms, forums, blogs, product review sites, and comment sections</td>
</tr>
<tr>
<td><strong>Keywords tracked</strong></td>
<td>Black Friday, Cyber Monday, BFCM</td>
</tr>
<tr>
<td><strong>Total number of posts analyzed</strong></td>
<td>5.67 million</td>
</tr>
<tr>
<td><strong>Sample groups analyzed</strong></td>
<td>
20,000 random posts overall<br>
2,000 of the most engaging posts<br>
2,000 random negative posts<br>
2,000 of the most engaging negative posts<br>
2,000 random positive posts<br>
2,000 of the most engaging positive posts
</td>
</tr>
<tr>
<td><strong>Key term definitions</strong></td>
<td>
<strong>Engagement:</strong> The number of shares, comments, likes, retweets, replies, direct messages, etc.<br><br>
<strong>Post:</strong> A single piece of content that can include multiple mentions.<br><br>
<strong>Mentions:</strong> The number of instances in which keywords and phrases appeared within a post.
</td>
</tr>
<tr>
<td><strong>Note on theme percentages</strong></td>
<td>Theme categories are non-exclusive. A single post can carry more than one complaint or praise theme, so category percentages should not be read as adding up to 100%.</td>
</tr>
</tbody>
</table>

Our Social Listening analysis of 5.67 million Black Friday and Cyber Monday (BFCM) posts reveals how consumers engage with the shopping “holiday” along with their expectations of, and frustrations with, brands.

Only about 23% of BFCM posts came from consumers. 

In our sample of 20,000 random posts, less than a quarter of BFCM posts came from consumers themselves. By comparison, about 65% came from advertisers, underscoring just how easily the voice of the consumer can get lost in analyses of the broader BFCM conversation.

The consumer-brand distribution varied significantly across platforms. On Instagram, Facebook, and TikTok, BFCM conversation is dominated by promotional content. But for brands looking to zero in on the voice of the consumer, one platform stands out: On Reddit, BFCM conversation is 85% consumer-driven compared to only 5% of posts coming from brands.

The focus of consumer posts also changed depending on the source. In our negative sample, price integrity was the top complaint on Reddit and X, while boycotting BFCM altogether was the leading topic on Bluesky. Finally, complaints about product quality, delivery, fulfillment, and customer service were well represented in product reviews.

Reddit is where the consumer voice comes through loudest, but each data source has insight to offer brands analyzing customer frustration. Ultimately, where you look determines what you see.

Over 30% of negative consumer posts were about price integrity. 

Consumers can’t believe BFCM prices, and not in a good way. Price integrity was mentioned in about 34% of negative consumer posts in our random sample and 30% in our most engaging post sample. Simply put, today’s shoppers don’t trust that BFCM prices signal meaningful savings for them.

One Reddit post that drew engagement illustrates the frustration, claiming that a seemingly discounted laptop had not changed price in two months. The post does not provide two months of pricing data, but it doesn’t have to. Its point is clear, echoed in top comments calling BFCM sale prices misleading, if not outright “scams.”

Lack of consumer trust runs through other highly mentioned categories of negative conversation, such as BFCM boycotts and retail working conditions. However, it’s pricing honesty and transparency that consumers post about the most. 

BFCM brand risk now extends beyond price integrity.

Consumer BFCM conversations have shifted since 2025 from largely focusing on price integrity to including other value signals. Shoppers have grown increasingly selective about what they spend money on. According to McKinsey’s State of the Consumer 2026 report, 82% of global consumers are delaying buying new stuff to replace their old stuff. 

We can see that selectiveness reflected in the widening of consumer complaints to include other aspects of the shopping experience beyond price integrity: customer service, delivery, fulfillment, and product quality. Negative posts about affordability and debt, some of the drivers behind consumers’ shopping behaviors, have also grown since BFCM 2025.

In a nutshell, here’s how the past year in BFCM conversation panned out:

Pre-BFCM 2025: During the August to October consideration phase consumers debated whether or not to take part in BFCM 2025 sales. Price integrity and product quality were the leading two topics of negative posts.

The lead up to the sale: As the consideration continued, complaints about price integrity peaked as social media users put advertised discounts on trial.

Peak week: From November 24, 2025, Black Friday, through the end of the month, price integrity continues to be the top talking point.

Post-BFCM 2025: In December, negative posts about customer service, delivery, and fulfillment balloon in volume.

Months later: Negative posts about product quality reached their highest levels from January to July 2026.

So while price integrity is still a key discussion point heading into BFCM 2026, consumers are also scrutinizing more dimensions of their shopping experiences with brands. Today’s disappointing experiences can become tomorrow’s, next week’s, or even next year’s brand reputation risks.

About 38% of positive BFCM consumer posts focused on advice shared by other shoppers. 

Our last point explored what consumers critique, but what do they praise? Hint: It’s mostly not brands. Only 5% of positive consumer posts in our random sample were about a company’s customer service. Compare that to the 38.2% that were about another social media user’s recommendations and advice.

For brands, this drives home that offering a great customer experience is more likely to prevent complaints than generate praise. Offering a product or service worth recommending and having a presence where shoppers advise each other (which, in this data, mostly means Reddit) are both key to getting customers to say good things about your brand unprompted. Authentic customer voices have the potential to influence other shoppers more powerfully than even a well-executed advertising campaign.

Based on the data, here’s what marketers and brands should explore in their BFCM 2026 planning:

Use different platforms to understand each facet of the consumer conversation.

Different social platforms cultivate different audiences and posts. Reddit stands out for having the highest volume of consumer posts, but it’s not the only one that brands can tap for insights. Reddit and product reviews reveal the shopping experience frustrations around BFCM, while X and Bluesky highlight how political and economic narratives shape the discourse. Tailoring your audience and market research strategies to how different sources operate will point you to more nuanced insights valuable to marketers and brands.

Prove that sale (or non-sale) prices offer meaningful value.

Brands need to get creative with how they make the value of sale prices immediately clear to shoppers. Side-by-side comparisons with regular prices aren’t as impactful as they used to be, but other proof points like product lifespan comparisons or UGC are among the alternatives. Not doing a BFCM sale? That’s also something to promote. Brands like REI, Vitsœ, Lucy and Yak, Patagonia, and others use the occasion to reinforce their brand values around sustainability, social responsibility, and honest pricing. Either way, brands need to communicate a compelling rationale around their pricing that can stand up to price sensitivity and scrutiny.

Plan how to engage with consumers in the weeks and months after BFCM 2026.

Instead of treating BFCM sales as the endpoint, understand them as the midpoint of your customer interactions. What happens after shoppers place their orders impacts how they talk about your brand online. If a purchase comes with late shipping, bad customer service, or a disappointing product, one purchase can lead to a social post that turns thousands of people off your brand. Get ahead of bad reviews with proactive communications like realistic delivery estimates, prompt delay notifications, easy-to-access order tracking, and clear return and exchange instructions. 

The Meltwater Social Listening data suggests that BFCM 2026 will be an opportunity for brands to prove the value of the entire customer experience for price-crunched shoppers. Sharpening communications based on cross-platform conversations can help marketers leverage this high-profile, high-risk shopping event to build lasting trust. 

Don’t stop here: Turn these insights into action with Meltwater. Explore how with a free demo.

How is the Black Friday and Cyber Monday consumer conversation changing in 2026?

Black Friday and Cyber Monday value now extends beyond discounts. Meltwater’s social listening analysis shows that consumers are increasingly discussing customer service, delivery, fulfillment, product quality, affordability, and debt alongside price integrity.

Why does price integrity still matter during Black Friday and Cyber Monday?

Shoppers still scrutinize whether sale prices offer meaningful value. Meltwater data shows that price remains a core Black Friday and Cyber Monday 2026 discussion point, even as consumers evaluate more aspects of the shopping experience.

Which sources reveal the most useful Black Friday and Cyber Monday consumer insights?

Different sources reveal different parts of the conversation. Meltwater analysis found that Reddit and product reviews surfaced shopping experience frustrations, while X and Bluesky highlighted political and economic narratives shaping Black Friday and Cyber Monday discourse.

What do consumers complain about most about Black Friday and Cyber Monday?

Over 30% of negative consumer posts were about price integrity according to Meltwater’s Black Friday and Cyber Monday analysis. Complaints also increasingly focus on customer service, delivery, fulfillment, product quality, affordability, and debt, making the full shopping experience a growing source of brand risk.

What drives positive Black Friday and Cyber Monday conversations among consumers?

Shopper advice appears far more often than praise for brands. In Meltwater’s analysis, 38.2% of positive consumer posts focused on another user’s recommendations and advice, compared with 5% focused on a company’s customer service.

Why should brands plan for the post-purchase Black Friday and Cyber Monday experience?

Post-purchase experiences can create lasting brand risk. Meltwater data shows that delivery, fulfillment, customer service, and product quality now shape more Black Friday and Cyber Monday consumer conversations, extending scrutiny well beyond checkout.

How can social listening strengthen a Black Friday and Cyber Monday strategy?

Social listening turns consumer conversations into actionable planning signals. Meltwater helps brands compare sources, identify emerging concerns, understand changing definitions of value, and prepare for reputation risks throughout the Black Friday and Cyber Monday customer journey.

This analysis of 5.67 million posts reveals how Black Friday and Cyber Monday consumer expectations are shifting in 2026 and what brands can do to protect their reputations and prove value.

Black Friday and Cyber Monday Consumer Trends 2026

July brings a new AI search visibility pattern

Top individual sources: Citation volume & trends

YouTube remains the strongest citation source

Social and professional sources now sit closer to the center of AI visibility

Wikipedia, NIH and Statista lost ground together

Perplexity now needs a social and professional strategy

Conclusion: GEO now depends on source mix

Generative AI platforms are creating new paths for customers to discover brands, and our July data gives PR and marketing teams a clear message: AI visibility now depends on matching content to the source ecosystems each model uses.

Meltwater’s July 2026 analysis is based on thousands of prompts across eight major LLMs: Claude, ChatGPT, Copilot, Google AI Mode, Google AI Overviews, Gemini, Perplexity and xAI Grok. The research compares July against June and places the results in the context of our ongoing tracking of AI search behavior - you can find our previous analysis here. 

This research is carried out using GenAI Lens, Meltwater's AI search visibility tracking tool. Find out how GenAI Lens can help track your brand's performance in AI search. 

In July, social, professional and video sources gained ground together, while several institutional and reference sources declined. YouTube reached a new high, LinkedIn moved from a supporting channel into the top five, Facebook held the #3 position, and Instagram stayed in the top 10 after its June surge.

At the same time, Wikipedia, NIH and Statista all fell. This creates a sharper divide between the social sources models use for broad visibility and the reference sources they use for institutional authority.

This changes the GEO playbook for marketing professionals. Visibility in AI answers now depends on a wider source mix that includes video, LinkedIn, social platforms, owned content, earned coverage and reference assets.

<table>
  <thead>
    <tr>
      <th>Rank</th>
      <th>Source</th>
      <th>Total Citations</th>
      <th>Vis. Score</th>
      <th>Cit. MoM</th>
      <th>Cit. Rate</th>
      <th>Visibility Score MoM</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>youtube.com</td>
      <td>229.3k</td>
      <td>26%</td>
      <td>+24.3% ↑</td>
      <td>+12.5% ↑</td>
      <td>+13% ↑ (18% rate score)</td>
    </tr>
    <tr>
      <td>2</td>
      <td>reddit.com</td>
      <td>128.2k</td>
      <td>18%</td>
      <td>+8.24% ↑</td>
      <td>+9.09% ↑</td>
      <td>+5.88% ↑ (12% rate score)</td>
    </tr>
    <tr>
      <td>3</td>
      <td>facebook.com</td>
      <td>88.7k</td>
      <td>14%</td>
      <td>+19.6% ↑</td>
      <td>0% →</td>
      <td>0% → (9% rate score)</td>
    </tr>
    <tr>
      <td>4</td>
      <td>nih.gov</td>
      <td>81.1k</td>
      <td>12%</td>
      <td>-9.65% ↓</td>
      <td>-16.7% ↓</td>
      <td>-7.69% ↓ (5% rate score)</td>
    </tr>
    <tr>
      <td>5</td>
      <td>linkedin.com</td>
      <td>80.3k</td>
      <td>14%</td>
      <td>+67.8% ↑</td>
      <td>+28.6% ↑</td>
      <td>+27.3% ↑ (9% rate score)</td>
    </tr>
    <tr>
      <td>6</td>
      <td>wikipedia.org</td>
      <td>66.6k</td>
      <td>12%</td>
      <td>-14.5% ↓</td>
      <td>-12.5% ↓</td>
      <td>-7.69% ↓ (7% rate score)</td>
    </tr>
    <tr>
      <td>7</td>
      <td>forbes.com</td>
      <td>62.0k</td>
      <td>12%</td>
      <td>+15.9% ↑</td>
      <td>0% →</td>
      <td>+9.09% ↑ (7% rate score)</td>
    </tr>
    <tr>
      <td>8</td>
      <td>yahoo.com</td>
      <td>54.8k</td>
      <td>11%</td>
      <td>+5.98% ↑</td>
      <td>0% →</td>
      <td>0% → (7% rate score)</td>
    </tr>
    <tr>
      <td>9</td>
      <td>instagram.com</td>
      <td>53.2k</td>
      <td>11%</td>
      <td>-2.37% ↓</td>
      <td>0% →</td>
      <td>0% → (6% rate score)</td>
    </tr>
    <tr>
      <td>10</td>
      <td>statista.com</td>
      <td>50.5k</td>
      <td>11%</td>
      <td>-7.32% ↓</td>
      <td>-14.3% ↓</td>
      <td>0% → (6% rate score)</td>
    </tr>
  </tbody>
</table>

YouTube produced the strongest single-source result in July, reaching 229.3K citations, up 24.3% month over month, making it the highest-volume source tracked so far.

This July rebound also resolves one of the open questions from June, when we saw YouTube’s first recorded dip, raising the possibility that its citation growth had started to slow. July’s data showed YouTube clearly gaining in citation volume, citation rate and visibility score at the same time.

Google AI Mode generated 65,340 YouTube citations in July. Google AI Overviews followed with 54,873, Gemini generated 51,761 and Perplexity generated 44,103. That spread shows how broadly YouTube now supports AI visibility across high-reach models.

Brands should treat YouTube as a core GEO asset. The main work is practical: improve transcripts, use clear titles, write detailed descriptions, cover topics thoroughly and structure videos so AI systems can extract the key answer quickly.

LinkedIn produced the largest multi-metric gain of any established top-10 source since we began tracking this data in April. Citation volume grew 67.8%, citation rate increased 28.6% and visibility score rose 27.3%. LinkedIn also jumped from #8 in June to #5 in July, reaching 80.3K citations.

The model-level data shows where that growth came from. Perplexity generated 31,311 LinkedIn citations, while Google AI Mode generated 21,202. AI Overviews added 12,530, Copilot generated 8,283 and Grok generated 5,269.

This shows that LinkedIn now functions as an AI citation source across high-visibility models, especially for professional topics, business content and social proof. We explored the importance of LinkedIn as a B2B AI citation driver in a recent report.

That report, along with this more recent research, highlights some best practices for getting your LinkedIn content cited: Posts should state the core point early, include clear claims, use specific data where possible and avoid burying useful information in vague commentary. Executive posts, company research, industry explainers and practical POV content all have stronger GEO potential when they use direct structure and plain language.

<table>
  <thead>
    <tr>
      <th>Source Type</th>
      <th>July Count</th>
      <th>July %</th>
      <th>June %</th>
      <th>MoM Change</th>
      <th>Multi-Month Trend</th>
      <th>Trend</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Other</td>
      <td>4,500,000</td>
      <td>56.4%</td>
      <td>55.2%</td>
      <td>+1.2 pts</td>
      <td>Third consecutive month of growth</td>
      <td>↑ Confirmed ascending</td>
    </tr>
    <tr>
      <td>Earned/News</td>
      <td>2,700,000</td>
      <td>33.1%</td>
      <td>35.0%</td>
      <td>-1.9 pts</td>
      <td>Second consecutive decline from 37–40% band</td>
      <td>↓ Under pressure</td>
    </tr>
    <tr>
      <td>Social</td>
      <td>626,700</td>
      <td>7.8%</td>
      <td>6.9%</td>
      <td>+0.9 pts</td>
      <td>New 5-month high; corroborated by individual sources</td>
      <td>↑ Confirmed ascending</td>
    </tr>
    <tr>
      <td>Reviews</td>
      <td>126,700</td>
      <td>1.6%</td>
      <td>1.6%</td>
      <td>0 pts</td>
      <td>Stable across all 5 months</td>
      <td>→ Structural floor</td>
    </tr>
    <tr>
      <td>Wiki</td>
      <td>68,200</td>
      <td>0.9%</td>
      <td>1.0%</td>
      <td>-0.1 pts</td>
      <td>Second consecutive pullback from May’s 1.1% peak</td>
      <td>↓ Declining</td>
    </tr>
    <tr>
      <td>Press Release</td>
      <td>17,700</td>
      <td>0.2%</td>
      <td>0.2%</td>
      <td>0 pts</td>
      <td>Flat for 3 months at new low base</td>
      <td>→ Stabilised</td>
    </tr>
  </tbody>
</table>


Social reached 7.8% of citation domain count in July, its highest share so far, and unlike April’s short-lived spike, July’s growth came from several sources moving together.

Facebook held the #3 position overall with 88.7K citations, up 19.6% month over month. LinkedIn surged into the top five. Instagram held at #9 with 53.2K citations, down slightly after June’s large increase but still elevated compared with earlier months. Reddit also continued its impressive growth run, reaching 128.2K citations and holding the #2 position.

Social content now affects AI visibility in more than one way and the practical takeaway is this: you should track which social platforms feed which AI systems. Reddit may support category conversation, LinkedIn may support executive authority and business context, Facebook may carry high-volume model visibility, and Instagram may support consumer and visual categories.

July brought a clear reversal for several reference and institutional sources.

Wikipedia fell 14.5% month over month to 66.6K citations, its second consecutive decline after the March to May ascent. Statista fell 7.32% to 50.5K citations, also marking its second consecutive decline. NIH declined 9.65% to 81.1K citations, its first negative month after the major gains seen earlier in the series.

These sources still carry authority. NIH remains #4 overall, Wikipedia remains #6 and Statista remains in the top 10. The issue is growth priority. Brands should maintain accurate, neutral and well-structured presence across reference and data sources, but July shows that the strongest current growth sits elsewhere.

The right response is rebalancing. Keep Wikipedia accurate. Keep data partnerships and authoritative references current. Then direct incremental GEO effort toward LinkedIn, YouTube, social content and strong owned content.

Perplexity changed more than any other model across June and July.

In July, Facebook led Perplexity’s citations with 45,049. YouTube followed closely at 44,103, LinkedIn reached 31,311, Instagram reached 25,527 and Reddit reached 24,010. NIH fell to 1,793 for Perplexity, down sharply from earlier months.

That source mix gives Perplexity a new profile, so we can no longer think of it as a purely research-led model, since it now pulls heavily from social, professional and video sources.

The practical consequences of this are that a Perplexity visibility strategy should include LinkedIn posts, public social content, YouTube transcripts and topic-specific content designed for quick extraction. Brands that only optimize for institutional citations may miss Perplexity’s current source behavior.

Earned/News fell to 33.1% in July, down from 35.0% in June and below the 37% to 40% band seen from March through May. This marks the second consecutive decline.

PR still plays a major role in AI visibility because earned coverage provides third-party validation, category authority and source diversity. But the July data shows that PR needs to watch whether earned media is settling into a lower share of citation activity.

This creates a measurement challenge. PR professionals should continue pursuing high-quality editorial coverage, but they should also connect that coverage to other AI-citable surfaces. Strong media stories can feed LinkedIn posts, YouTube explainers, data pages, executive commentary and owned content. That content system gives AI models more ways to find and cite the same brand authority.

Press Release remained flat at 0.2% for the third consecutive month, suggesting that the category has stabilized at a low base. Press releases can support disclosure and media operations, but the data does not support treating them as a primary AI visibility lever.

LinkedIn now deserves a place in the core GEO plan. Treat company pages, executive profiles and employee thought leadership as public citation surfaces.

Lead with the main point. Use specific claims. Add data when available. Keep posts focused on one idea. Create content that answers real category questions instead of posting generic brand updates.

2. Keep YouTube at the center of AI visibility planning

YouTube’s lead continues to grow. Brands should prioritize transcripts, descriptions, metadata, chaptering and topic coverage.

For GEO, the transcript often carries the most value. Videos should answer questions directly, use clear terminology and cover topics with enough depth for AI systems to extract reliable information.

Wikipedia, NIH and Statista still support authority, but July shows declining momentum for all three.

Maintain accuracy and completeness across these sources. Then place new growth effort into LinkedIn, YouTube, social platforms and owned content that can answer high-intent questions clearly.

Perplexity now relies heavily on Facebook, YouTube, LinkedIn, Instagram and Reddit. Brands should measure Perplexity visibility on its own rather than folding it into a general AI visibility score.

A blended view can hide large model-level differences. Track source pathways by model so you know whether visibility comes from social, professional, video, earned or institutional sources.

July’s data shows a more divided AI citation environment.

YouTube remains the strongest source by a wide margin. LinkedIn has become a major source for professional visibility. Facebook, Reddit and Instagram continue to support social discovery. Owned and other web sources keep growing in aggregate.

At the same time, Wikipedia, NIH and Statista lost ground together, while Earned/News moved below its earlier band.

Brands should respond with a broader, model-specific GEO strategy. Build video content that AI can parse. Make LinkedIn content more structured. Keep reference sources accurate. Strengthen owned content. Track Perplexity, Gemini, ChatGPT, Claude, Copilot, Grok and Google’s AI experiences separately.

The brands that win in AI search will earn visibility across the sources each model already uses.

1. What did the July 2026 AI citation data show?

The July data showed YouTube reaching an all-time high at 229.3K citations, LinkedIn growing 67.8% and Social reaching its highest share across the research. Wikipedia, NIH and Statista all declined in the same month.

2. Why is LinkedIn more important for GEO now?

LinkedIn moved to #5 overall in July and gained strongly across citation volume, citation rate and visibility score. Perplexity and Google AI Mode drove much of that growth, which means LinkedIn content now supports visibility across high-reach AI models.

3. Should brands stop investing in Wikipedia, NIH or Statista?

Brands should maintain those sources as authority assets. July shows they should not receive the same level of growth focus they had earlier in the year. Incremental effort should move toward LinkedIn, YouTube, social content and well-structured owned content.

4. How should brands measure AI visibility after July’s data?

Brands should measure AI visibility by model, source type and citation pathway. Track where each model pulls information from, because ChatGPT, Claude, Gemini, Perplexity, Copilot, Grok and Google’s AI experiences now show very different citation patterns.

LinkedIn appears in a lot more AI search citations this month.

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