We analyzed over one million online discussions of GLP-1 medications (widely used for weight management) to understand consumer attitudes towards these increasingly popular drugs. 

GLP-1 medications continue to be one of the defining healthcare and consumer trends of 2026. The swell of online conversation supports this, however it also reveals a distinct fragmentation and wide thematic differentiation depending on where conversations are happening. 

While aggregate mention and engagement figures suggest a single massive discussion, Meltwater Explore+ social listening analysis reveals something more nuanced: each platform is hosting its own distinct interpretation of what GLP-1s represent.

Understanding GLP-1 conversations therefore means reading each platform separately rather than averaging them together. The differences between them reveal how audiences process healthcare trends through entirely different social behaviors depending on platform context.

GLP-1 conversation volume remained stable, but engagement became more volatile

Reddit has become the internet’s unofficial GLP-1 support network

Pinterest users are focused on the practical side of the GLP-1 lifestyle

How Meltwater Explore+ helps marketers understand platform-specific audience behavior

FAQ: GLP-1 consumer intelligence and social listening

<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%;">Capability</td>
      <td style="padding:12px; border:1px solid #ccc;">Explore+</td>
    </tr>
    <tr>
      <td style="padding:12px; border:1px solid #ccc; font-weight:bold;">Analysis window</td>
      <td style="padding:12px; border:1px solid #ccc;">January 1, 2026 – May 21, 2026</td>
    </tr>
    <tr>
      <td style="padding:12px; border:1px solid #ccc; font-weight:bold;">Data sources</td>
      <td style="padding:12px; border:1px solid #ccc;">
        Reddit, X and Pinterest, with all-platform totals used for broader volume and engagement context.
      </td>
    </tr>
    <tr>
      <td style="padding:12px; border:1px solid #ccc; font-weight:bold;">Brands / entities tracked</td>
      <td style="padding:12px; border:1px solid #ccc;">
        Ozempic, Wegovy, Mounjaro, Zepbound, Semaglutide, Tirzepatide, Retatrutide, Saxenda, Rybelsus, Orforglipron, Novo Nordisk, Eli Lilly, Liraglutide, Cagrisema, Pfizer, Berberine, Survodutide, TrumpRx, Metformin, Mazdutide, Roche
      </td>
    </tr>
    <tr>
      <td style="padding:12px; border:1px solid #ccc; font-weight:bold;">Key term definitions</td>
      <td style="padding:12px; border:1px solid #ccc;">
        <strong>Engagement:</strong> The number of shares, comments, likes, retweets, replies, direct messages, etc.<br><br>
        <strong>Mentions:</strong> The number of instances when keywords and phrases appeared within a piece or collection of content.<br><br>
        <strong>Share of Voice (SOV):</strong> The percentage, or share, of total mentions attributed to a particular brand, market, or entity.<br><br>
        <strong>Subreddit:</strong> A user-created community on Reddit dedicated to a particular interest or topic.
      </td>
    </tr>
  </tbody>
</table>

Download a one-page pdf summarizing the findings of this analysis

Across the three-platform analysis set, Reddit accounted for approximately 81% of total mentions, followed by X at around 16%, and Pinterest at around 3%.

GLP-1 discussion volume remained broadly stable between January and April 2026, fluctuating by only around 11% between the lowest and highest full months of analysis.

March and April represented the highest sustained discussion periods, driven by GLP-1 pill approval news, ongoing Reddit community activity and viral educational content such as the widely circulated Kurzgesagt Ozempic video.

The Uncomfortable Truth About Ozempic

At the same time, engagement became more volatile. Total engagement peaked at 24.7 million in January before falling by roughly 27% by the partial May dataset despite relatively stable mention volume. Rather than indicating declining interest, the data suggests the conversation is shifting from viral, event-driven spikes into higher-volume, lower-engagement community discussion.

1.18 million: the number of GLP-1 mentions across Reddit, X and Pinterest between January 1 and May 21, 2026

97.5 million: Total engagement actions across the analysis period

81%: Share of analyzed platform mentions occurring on Reddit

Why this matters: Monitoring strategies focused only on engagement spikes risk misreading the maturation of the GLP-1 conversation. As discussion shifts toward sustained community participation, marketers need platform-level behavioral intelligence rather than viral metrics alone.

Reddit is where the GLP-1 patient conversation lives at its deepest and most sustained level. The platform functions less like a traditional social network and more like a distributed peer-support infrastructure for people actively navigating treatment and researching treatment options. At 81%, Reddit accounts for the highest SOV in GLP-1 mentions, compared to X and Pinterest. 

Patient experience and treatment management accounted for roughly 28% of Reddit discussion themes, making it the largest conversation category in the dataset. Posts focused heavily on dosing, side effects, medication switching, injections, and compounding. Side effect discussion intensified steadily through April, suggesting users are moving deeper into long-term treatment journeys and increasingly relying on peer communities to fill information gaps — amplifying studies on side effects not captured in trials, such as this April study. 

Access, cost and insurance concerns represented another persistent theme, accounting for approximately 12% of thematic discussion. FDA action against compounded GLP-1 medications sharpened these anxieties during February and April, particularly in drug-specific subreddits.

One of the clearest structural signals is the divergence between patient language and public language. Tirzepatide-focused communities accounted for roughly 44% of mentions across the top 30 GLP-1 subreddits, compared with approximately 20% for semaglutide-focused communities, even while Ozempic remains the dominant public shorthand for the category.

Also of note is the emergence of retatrutide discussion. Despite not yet being FDA-approved, retatrutide already accounts for roughly 4% of Reddit mentions, with dedicated communities such as r/Peptides and r/ResearchCompounds discussing trial data, peer experiences, and potential future use cases. The level of interest suggests consumer demand is running ahead of the regulatory timeline, with an active community already building its own reference framework.

28%: Share of Reddit thematic signal focused on treatment management

44%: Share of top-30 subreddit mentions tied to Tirzepatide communities

4%: Share of Reddit mentions already discussing retatrutide despite lacking FDA approval

Why this matters: Reddit reveals the gap between cultural perception and patient reality. For marketers and healthcare communicators, understanding side effects, access anxiety and treatment management behavior requires listening to patient-led communities rather than relying solely on broader social commentary. The growing retatrutide conversation also offers an early view into how patient expectations and narratives can develop well before a treatment reaches the market.

GLP-1s have become a cultural flashpoint on X

X is hosting a fundamentally different GLP-1 conversation from Reddit though, importantly, mention volume on X accounts for lower overall SOV (16%). Rather than functioning as a patient support environment, the platform operates as a cultural commentary arena. 

High-engagement posts frequently came from media personalities discussing celebrity weight loss trends, political commentators framing GLP-1 adoption as an economic or healthcare policy issue, and wellness influencers debating the long-term implications of pharmaceutical weight management. Viral moments were often sparked by reactions to celebrity transformations, discussions of Ozempic's impact on beauty standards, or commentary on the growing market power of companies such as Novo Nordisk and Eli Lilly.

Body image, culture, and social commentary represented roughly 19% of thematic signals within the X discussions analyzed, and generated a disproportionately large share of engagement. Conversations around the perceived collapse of body positivity in the era of Ozempic resurfaced repeatedly throughout January, March and May without any single triggering event, suggesting the topic has become a recurring structural feature of GLP-1 discourse on the platform.

Political and economic framing represented another highly engaging discussion category on X particularly around pharmaceutical dependency, healthcare economics and policy speculation. Unlike Reddit, where discussions were largely driven by patients sharing treatment experiences and navigating day-to-day realities of GLP-1 use, the highest-engagement X posts were shaped by journalists, political commentators, and general-interest accounts that repositioned GLP-1 topics into broader cultural narratives.

Skeptical or anti-GLP-1 viewpoints made up a relatively small share of overall discussions on X, but they generated outsized engagement. In other words, posts criticizing GLP-1 medications were more likely to be shared, commented on, or debated, highlighting how a relatively small group of voices can exert a disproportionate influence on public perception.

19%: Share of thematic posts tied to body image and cultural commentary

5%: Share tied to political and economic framing

3%: Share tied to skepticism and opposition narratives

Why this matters: The highest-engagement GLP-1 discussion on X is being shaped by cultural commentators rather than healthcare experts. Brands need culturally fluent communications strategies capable of responding to recurring narrative cycles around body image, celebrity culture, and economic anxiety.

Pinterest functions less like a debate platform and more like a lifestyle implementation engine for people already committed to GLP-1 treatment or adjacent health routines. Pinterest discussions also occupy just 3% of total mentions, meaning the lowest SOV between all 3 platforms.

Unlike Reddit and X, Pinterest users are focused on putting GLP-1 routines into practice. Approximately 96% of sampled Pinterest posts referenced a specific GLP-1 medication, suggesting users have largely moved beyond the awareness and decision phases and are now focused on optimizing daily routines around treatment.

The dominant content categories on Pinterest tend to be protein recipes, meal planning, exercise, body composition, and low-carb nutrition. These themes remained stable throughout the analysis period, reinforcing their role as foundational GLP-1 lifestyle behaviors rather than short-lived trends.

The strongest emerging signal on Pinterest was the rapid growth of product review and personal experience content, which increased by roughly 60% between January and April. At the same time, interest in “natural Ozempic” alternatives and wellness-adjacent discovery content declined sharply, suggesting audiences are moving away from speculative exploration and toward long-term treatment management.

Pinterest also showed dramatically more positive sentiment than the broader platform ecosystem. However, the data suggests much of this positivity reflects Pinterest’s save-based mechanics and coordinated affiliate-style content amplification rather than true patient emotional sentiment.

It's also worth noting that many of Pinterest's highest-engagement GLP-1 posts were near-identical versions of the same keto cookbook content, which contains mention of “wegovy diet plan”, and has been republished across multiple accounts. This pattern is more consistent with coordinated affiliate-style distribution than a diverse creator ecosystem, and likely contributes to both the platform's unusually high engagement figures.

+69%: Share of sampled posts featuring protein recipes

+60%: Growth in review and personal experience content from January to April

-66%: Drop in natural GLP-1 alternative content between January and May

Why this matters: Pinterest users are actively searching for practical lifestyle support around GLP-1 treatment, but credible guidance remains limited. Brands that provide useful, savable, and platform-native educational content have an opportunity to fill a growing information gap.

Use Case 1: Identifying platform-specific audience dynamics

GLP-1 audiences behave differently across Reddit, X, and Pinterest, making aggregate social listening insufficient for understanding consumer behavior.

Monitor platform-specific conversation themes, and sentiment shifts in real time

Track how narratives evolve differently across patient, cultural, and lifestyle-oriented communities

Identify emerging behavioral trends before they appear in mainstream reporting

Audience strategies informed by how consumers actually behave on each platform, rather than relying on blended social averages, deliver better results. 

Use Case 2: Detecting emerging cultural narratives

Recurring cultural narratives around GLP-1s, including body image commentary, and anxiety around affordability, can reshape public perception rapidly.

Surface fast-moving cultural narratives through real-time social listening

Detect early spikes tied to policy decisions, research studies, and viral commentary

Benchmark evolving discussion themes against broader industry conversation

Respond earlier to emerging reputational risks and cultural shifts, staying in control of evolving conversations rather than constantly playing catch-up.

Use Case 3: Understanding long-term consumer behavior trends

The GLP-1 conversation is shifting from viral moments toward sustained lifestyle and treatment-management behavior.

Analyze long-term thematic changes across social and community platforms

Track shifts in patient concerns, lifestyle behaviors, and treatment expectations

Use cross-platform intelligence to identify changing consumer needs over time

Develop more durable marketing, communications, and consumer intelligence strategies grounded in evolving audience behavior.

Want to understand how your audiences behave differently across platforms? Explore the Meltwater platform and learn how our social listening capabilities help brands uncover emerging trends, cultural narratives and consumer behavior shifts in real time. 

Book a meeting with one of our product experts by filling out the form below

GLP-1 discussion is not a single unified conversation. As of 2026, audiences are interpreting the same healthcare phenomenon through entirely different platform behaviors: patient support on Reddit, cultural commentary on X, and lifestyle optimization on Pinterest.

That fragmentation matters because it changes how brands, marketers and healthcare communicators should interpret online conversations — and shift their approach based on where and how conversations are evolving. The next phase of GLP-1 discussion will likely be shaped less by explosive viral moments and more by sustained behavioral patterns around treatment management, affordability, cultural identity, and lifestyle integration. Emerging discussions around next-generation treatments such as retatrutide further suggest that online communities are already looking beyond today's leading offerings and actively shaping expectations for what comes next.

Detect emerging cultural narratives and tap into long-term consumer behavior trends with real-time intelligence. 

How Reddit, X, and Pinterest Are Shaping Completely Different GLP-1 Conversations

AI Visibility is no Longer Just about Search Rankings

Earned Media is Still the Backbone of AI Visibility

Social is Rising, but not Equally Across Every Model

YouTube has Become AI Citation Infrastructure

Conclusion: AI Visibility is Fragmented, but Measurable, and Strategic

Generative AI is changing how consumers discover brands, compare products, and make decisions. But the real strategic issue for communications and marketing leaders has evolved somewhat from “Are we showing up in AI answers?”

The big question is now: which AI platforms are citing us, and what sources do they trust when that happens?

Meltwater’s April 2026 GenAI Lens analysis of approximately 5.35 million citations across eight major LLMs (ChatGPT, Grok, Claude, Perplexity, Gemini, Google AI Overviews, Google AI Mode, and CoPilot)  reveals a rapidly evolving citation landscape. The findings show that large language models do not behave like a single universal search layer. Instead, they act like distinct information tribes, each with its own trust hierarchy.

For PR and marketing teams, that is worth paying attention to. AI visibility is becoming a new proxy for brand authority, market relevance, and information advantage.

Traditional search rewarded a relatively familiar playbook: keyword relevance, backlinks, domain authority, content freshness, and technical optimization.

Generative AI is different. LLMs synthesize answers from an ecosystem of sources, and those ecosystems vary dramatically by model. According to Meltwater’s April 2026 research, ChatGPT behaves like an institutional authority engine, Grok has become heavily social-first, Claude continues to prioritize structured data, Perplexity remains video-led, and Google AI Mode has emerged as a major reach engine.

That means a brand can be highly visible in one AI platform and nearly invisible in another. So the current challenge of Generative Engine Optimization: AI visibility is now model-specific.

One of the most commercially important findings is that earned media remains central to AI citation behavior.

In April, Earned/News accounted for 39.5% of citations, up from 38.3% in March. Meanwhile, the “Other” category, which includes corporate websites, blogs, documentation, and long-tail sites, declined from 53.2% to 50.3%.

That does not mean owned content is irrelevant, but it does suggest that third-party validation is becoming more important as AI systems decide what to trust. For PR teams, this is a strong signal: media coverage is becoming essential fuel for AI visibility.

It also changes the role of brand content. A blog post alone may not be enough any more, because to be cited by AI, brand narratives increasingly need reinforcement from news coverage, social discussion, video transcripts, Wikipedia, data aggregators, and trusted third-party sources.

March vs. April citation share by source type 

<table class="source-table">
  <thead>
    <tr>
      <th>Source Type</th>
      <th>April Count</th>
      <th>April %</th>
      <th>March %</th>
      <th>MoM<br>Change</th>
      <th>Trend</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Other</td>
      <td>2,700,000</td>
      <td>50.3%</td>
      <td>53.2%</td>
      <td class="down">-2.9 pts</td>
      <td class="trend down-bg">↓</td>
    </tr>
    <tr>
      <td>Earned/News</td>
      <td>2,100,000</td>
      <td>39.5%</td>
      <td>38.3%</td>
      <td class="up">+1.2 pts</td>
      <td class="trend up-bg">↑</td>
    </tr>
    <tr>
      <td>Social</td>
      <td>385,500</td>
      <td>7.2%</td>
      <td>5.6%</td>
      <td class="up">+1.6 pts</td>
      <td class="trend up-bg">↑</td>
    </tr>
    <tr>
      <td>Reviews</td>
      <td>91,600</td>
      <td>1.7%</td>
      <td>1.6%</td>
      <td class="up">+0.1 pts</td>
      <td class="trend up-bg">↑</td>
    </tr>
    <tr>
      <td>Wiki</td>
      <td>52,900</td>
      <td>1.0%</td>
      <td>0.8%</td>
      <td class="up">+0.2 pts</td>
      <td class="trend up-bg">↑</td>
    </tr>
    <tr>
      <td>Press Release</td>
      <td>18,800</td>
      <td>0.4%</td>
      <td>0.4%</td>
      <td class="flat">0 pts</td>
      <td class="trend flat">→</td>
    </tr>
  </tbody>
</table>

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Social citations grew from 5.6% to 7.2% month-over-month. That may sound modest at the aggregate level, but the model-level differences are striking.

Grok’s top five sources are now entirely social platforms: X, Reddit, YouTube, LinkedIn, and Facebook. LinkedIn citations on Grok grew 217% month-over-month, the highest single-source increase in the dataset.

Google AI Overviews and Google AI Mode also show meaningful social intake, with YouTube, Reddit, LinkedIn, and Facebook appearing prominently in their citation patterns.

The takeaway isn’t that every brand should post more on every platform, but simply that brands need to understand which AI models are listening to which social ecosystems.

For real-time reputation, Grok visibility depends heavily on social presence. For Google’s AI experiences, YouTube and Reddit are essential. For business-oriented AI visibility, LinkedIn is becoming increasingly important.

<table class="model-table">
  <thead>
    <tr>
      <th>Model</th>
      <th>Other</th>
      <th>Earned/News</th>
      <th>Social</th>
      <th>Reviews</th>
      <th>Wiki</th>
      <th>PR</th>
    </tr>
  </thead>
  <tbody>
    <tr class="overall-row">
      <td><strong>Overall (April)</strong></td>
      <td><strong>50.3%</strong></td>
      <td><strong>39.5%</strong></td>
      <td><strong>7.2%</strong></td>
      <td><strong>1.7%</strong></td>
      <td><strong>1.0%</strong></td>
      <td><strong>0.4%</strong></td>
    </tr>

    <tr>
      <td>ChatGPT</td>
      <td>40.9%</td>
      <td class="highlight">51.1%</td>
      <td>0.5%</td>
      <td>2.6%</td>
      <td>3.7%</td>
      <td>1.2%</td>
    </tr>

    <tr>
      <td>Claude</td>
      <td class="highlight">53.0%</td>
      <td>43.1%</td>
      <td>1.1%</td>
      <td>1.3%</td>
      <td>1.2%</td>
      <td>0.5%</td>
    </tr>

    <tr>
      <td>CoPilot</td>
      <td class="highlight">56.7%</td>
      <td>38.3%</td>
      <td>2.4%</td>
      <td>1.6%</td>
      <td>0.6%</td>
      <td>0.3%</td>
    </tr>

    <tr>
      <td>Perplexity</td>
      <td class="highlight">49.8%</td>
      <td>41.6%</td>
      <td>4.4%</td>
      <td>1.7%</td>
      <td>1.8%</td>
      <td>0.7%</td>
    </tr>

    <tr>
      <td>Grok</td>
      <td class="highlight">41.6%</td>
      <td>40.5%</td>
      <td>15.1%</td>
      <td>2.0%</td>
      <td>0.6%</td>
      <td>0.2%</td>
    </tr>

    <tr>
      <td>Google Gemini</td>
      <td class="highlight">58.6%</td>
      <td>34.1%</td>
      <td>4.0%</td>
      <td>2.0%</td>
      <td>1.1%</td>
      <td>0.2%</td>
    </tr>

    <tr>
      <td>Google AI<br>Overviews</td>
      <td class="highlight">47.8%</td>
      <td>37.8%</td>
      <td>11.9%</td>
      <td>1.7%</td>
      <td>0.7%</td>
      <td>0.2%</td>
    </tr>

    <tr>
      <td>Google AI Mode</td>
      <td class="highlight">47.7%</td>
      <td>39.4%</td>
      <td>10.3%</td>
      <td>1.6%</td>
      <td>0.6%</td>
      <td>0.3%</td>
    </tr>
  </tbody>
</table>

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YouTube appears in the top five cited sources for six of the eight models analyzed, and is the number one source for Perplexity, Gemini, Google AI Overviews, and Google AI Mode.

This is one of the most actionable findings for marketers. AI platforms are increasingly drawing from structured, publicly available video content. But this is about more than the video itself, what really makes an impact is a combination of the transcript, description, metadata, and clarity of the information presented.

This means video strategy and AI visibility strategy are converging.

Product explainers, executive interviews, “how to choose” content, comparison videos, research summaries, and category education can all become citation surfaces if they are structured clearly enough for AI systems to parse.

Wikipedia represented just 1.0% of total citations in April, but it remains disproportionately important because of where it appears. It ranks in the top five sources for ChatGPT, Claude, Gemini, and Perplexity. On Perplexity, Wikipedia citations surged 412% month-over-month.

This makes Wikipedia a strategic visibility asset, not a vanity channel.

The lesson is simple but often overlooked: factual, neutral, well-maintained public information matters. A Wikipedia presence will not replace earned media, data, social engagement, or owned content, but for certain models, it can function as a foundational trust layer.

1. Match the content strategy to the model

ChatGPT rewards institutional authority. Grok rewards social presence. Claude rewards data. Perplexity rewards video and structured sources. CoPilot leans toward business-professional sources such as Statista, Forbes, LinkedIn, YouTube, and USNews.

A single content strategy cannot serve all of those systems equally.

Earned media is one of the strongest pathways into AI citation ecosystems.

The rise in Earned/News citations suggests that high-quality media coverage can improve not only public perception but also machine-mediated discoverability.

YouTube’s prominence across six of eight models means brands should prioritize clear titles, structured descriptions, accurate transcripts, and educational formats.

The transcript is often the AI-readable asset.

4. Take LinkedIn seriously as a citation source

LinkedIn citations grew across multiple models, including Grok, Google AI Mode, CoPilot, and Google AI Overviews. 

For B2B brands, executive visibility and professional thought leadership may increasingly influence AI-generated answers. Learn more about LinkedIn’s importance for LLM visibility in our blog post on the topic.

The April data shows major shifts in a single month, including Grok’s social surge and Perplexity’s Wikipedia spike.

Quarterly reporting may be too slow for AI visibility management. Brands need monthly audits by model, source type, and citation pathway.

The April 2026 data points to a clear conclusion: LLMs are not converging around one universal source of truth, but rather developing distinct citation behaviors.

That creates complexity, but also opportunity. Brands that understand how each model sources information can design smarter PR, content, social, video, and data strategies.

AI visibility is becoming a new layer of brand intelligence, showing where authority is being built, where reputational signals are being amplified, and where companies risk disappearing from AI-mediated discovery.

To win in generative search visibility your brand needs to show up in the sources AI already trusts.

1. Why do different AI models cite different sources?

Each model is built, trained, connected, and optimized differently. As a result, models develop different trust hierarchies. For example, ChatGPT shows stronger institutional authority signals, Grok is more social-led, Claude favors data sources, and Perplexity relies heavily on video and structured content.

2. Is earned media still important for AI visibility?

Yes. Meltwater’s April 2026 analysis found that Earned/News accounted for 39.5% of citations and grew month-over-month. That suggests PR remains one of the strongest levers for improving brand visibility in AI-generated answers.

3. How should brands measure AI visibility?

Brands should measure AI visibility by model, not just in aggregate. Tracking “Share of Model” helps identify where a brand is gaining or losing visibility across ChatGPT, Grok, Claude, Perplexity, Gemini, Google AI Overviews, Google AI Mode, and CoPilot.

We used Meltwater GenAI Lens to track monthly changes in LLM citations between March and April 2026

AI Search Visibility Report: Earned Media, YouTube, and LinkedIn Are Reshaping AI Visibility

Have you been lifemaxxing lately? Or how about fibermaxxing? Would you say you’re readingmaxxing right now? If you haven’t yet, you can expect new and unexpected phrases ending with “maxxing” to come across your social feeds in 2026. Our Media Intelligence analysis found that usage of, and engagement with, the emphatic suffix have surged in the first five months of 2026. 

Culturally, this growth signals the explosion of a niche turn of phrase beyond its problematic origins to mainstream usage. And for marketers, the rise of “maxxing” illustrates how niche internet language can re-emerge as mainstream trend vocabulary once platforms, creators, and audiences reinterpret it. Read on to learn more about what the rise of all things “maxxing” reveals about the lifecycle of modern digital trends.  

Usage of “maxxing” words increased by 96% between January and May 2026

How are social media users using the term “maxxing”?

Which social media platforms use “maxxing” words the most?

What the “maxxing” trend signals for brands

“Maxxing” trend insights: Marketer questions about emerging internet language

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  <tbody>
    <tr>
      <td style="padding:12px; border:1px solid #ccc; font-weight:bold; width:30%;">Capability</td>
      <td style="padding:12px; border:1px solid #ccc;"><a href="https://www.meltwater.com/en/capabilities/media-intelligence">Media Intelligence</a></td>
    </tr>
    <tr>
      <td style="padding:12px; border:1px solid #ccc; font-weight:bold;">Analysis window</td>
      <td style="padding:12px; border:1px solid #ccc;">January 1 – May 25, 2026</td>
    </tr>
    <tr>
      <td style="padding:12px; border:1px solid #ccc; font-weight:bold;">Data sources</td>
      <td style="padding:12px; border:1px solid #ccc;">
        Reddit, X, Forums, Bluesky, YouTube, Instagram, Pinterest, Twitch, TikTok, Facebook, Sina Weibo, WeChat, Snapchat, LinkedIn, Comments, Blogs, Product Reviews, Red, Bilibili, Douyin, LINE, Threads, and Telegram
      </td>
    </tr>
    <tr>
      <td style="padding:12px; border:1px solid #ccc; font-weight:bold;">Keyword tracked</td>
      <td style="padding:12px; border:1px solid #ccc;">*maxxing</td>
    </tr>
    <tr>
      <td style="padding:12px; border:1px solid #ccc; font-weight:bold; vertical-align:top;">Key term definitions</td>
      <td style="padding:12px; border:1px solid #ccc;">
        <p><strong>Engagement</strong>: the number of shares, comments, likes, retweets, replies, direct messages, etc.</p>
        <p><strong>Mentions</strong>: the number of instances when key words and phrases appeared within a piece or collection of content</p>
        <p><strong><a href="https://www.meltwater.com/en/blog/share-of-voice-definition-measurement">Share of Voice (SOV)</a></strong>: the percentage, or share, of total mentions attributed to a particular brand, market, or entity</p>
        <p><strong>Estimated Media Value (EMV)</strong>: a metric quantifying the <a href="https://www.meltwater.com/en/blog/measuring-influencer-marketing">ROI of influencer marketing</a> by showing what it would cost to achieve the same reach and engagement through paid media</p>
        <p><strong>Subreddit</strong>: a user-created community on Reddit dedicated to a particular interest or topic</p>
      </td>
    </tr>
  </tbody>
</table>

Usage of the suffix “maxxing” has nearly doubled across social media sources, including X, TikTok, Pinterest, and Reddit since January 2026. Much of that conversation is related to the phrase’s primary association with looksmaxxing, a controversial, online trend focused on obsessive improvement of one’s appearance. The trend got its start on extremist message boards nearly a decade ago but gained attention this year due to the highly publicized legal troubles of its controversial leading influencer Clavicular as well as increased concern about the impact on teen boys. However, as we’ll explore next, usage of the phrase has expanded beyond looksmaxxing to become a new part of internet semantics. Hence the 96% growth in mentions and 208% growth in engagement from January 1 to May 25, 2026.

In a combined sample of 2,000 of the most engaging posts and 2,000 randomized posts mentioning “maxxing”, about 36% of them were appearance related, including looksmaxxing and its similarly controversial derivations of heightmaxxing and hairmaxxing. However, the remaining 64% were unrelated to appearance, showing just how much the phrase has become a cultural catchall for focusing on a topic or interest to an extreme.

<blockquote class="tiktok-embed" cite="https://www.tiktok.com/@jesssfamofficial/video/7623133797562830093" data-video-id="7623133797562830093" style="max-width: 605px;min-width: 325px;" > <section> <a target="_blank" title="@jesssfamofficial" href="https://www.tiktok.com/@jesssfamofficial?refer=embed">@jesssfamofficial</a> A midnight SURPRISE (or 3am) setting up + giving my twins 14 gifts the night before their 14th birthday 🥺❤️‍🩹  birthdaymaxxing?? 🤭 I AM UNWELL! Tears and it’s not even tomorrow yet 😭 <a title="millennialmom" target="_blank" href="https://www.tiktok.com/tag/millennialmom?refer=embed">#millennialmom</a> <a title="birthdayfairy" target="_blank" href="https://www.tiktok.com/tag/birthdayfairy?refer=embed">#birthdayfairy</a> <a title="healing" target="_blank" href="https://www.tiktok.com/tag/healing?refer=embed">#healing</a> <a title="twinmom" target="_blank" href="https://www.tiktok.com/tag/twinmom?refer=embed">#twinmom</a> <a title="genalpha" target="_blank" href="https://www.tiktok.com/tag/genalpha?refer=embed">#genalpha</a> <a target="_blank" title="♬ Nice and Easy - Louis Adrien" href="https://www.tiktok.com/music/Nice-and-Easy-7187898690403567618?refer=embed">♬ Nice and Easy - Louis Adrien</a> </section> </blockquote> <script async src="https://www.tiktok.com/embed.js"></script>

For example, the most engaging “maxxing” post during the time period analyzed (the first five months of 2026) mentioned “birthdaymaxxing”. The TikTok from mom influencer Jessica Skube, featuring her setting up for her twin children’s 14th birthday, generated over three million engagement actions. 

That attention, and the resulting $7.5 million in EMV generated, was an especially significant win for the brands featured as Skube showed off the gifts, including items from Timberland, Steelseries, Zumiez, DC Shoes, Valentino, and over a dozen more. 

Overall, in our 4,000-post sample, about 23% of “maxxing” uses were generic ways of emphasizing doing a particular activity to its most intense extent. And notably, this usage was largely a joyful, or even ironic, reframing of its harmful origins. 

Take #tiramisumaxxing, as found in the caption of second-most engaged post: a TikTok of the world’s longest tiramisu, which debuted in London this April. Or #boomermaxxing, tagging the third-most engaged post: a TikTok skit about getting older and adopting the mannerisms of your parents. Or more generally, lifemaxxing, mentioned across a handful of high-performing posts in reference to relishing simply living life. Even celebrities are getting in on the new lingo: Pop star Dua Lipa mentioned “funmaxxing” in the caption of a recent Instagram post showing her vacationing with friends.

To be sure, maxxing isn’t always used to emphasize positive topics. However, it has expanded to describe everything from workplace trends (tokenmaxxing, 0.57% of sampled posts) to wellness initiatives (sleepmaxxing, fibermaxxing, proteinmaxxing, and other variants, 1.6% of sampled posts) to embracing specific cultures (Chinamaxxing, 0.8% of sampled posts).

Reddit and X are the dominant homes for maxxing conversation, with about 34% SOV each. Bluesky is a notable third at 8% of total mentions, punching significantly above its overall social media weight. It is followed by YouTube, with 6% of total mentions.

Though maxxing mentions are concentrated on text-heavy platforms, it’s the video-first ones that drive the most engagement. TikTok accounts for only 2% of total mentions volume, but produces some of the highest-engagement individual posts, as noted in the previous section. Pinterest, meanwhile, dominated the top-engagement sample overall, accounting for a majority of the top 2,000 posts, followed by TikTok at 18.4%. 

So while Reddit and X are where maxxing discussion is happening, TikTok and Pinterest are where the term is being disseminated to wider audiences. That difference in context is reflected in the sentiment data. Reddit and forums had the highest share of negative “maxxing” mentions, at 34% and 36% respectively. On Pinterest, where uncritical looksmaxxing and wellness routine boards dominate, the phrase has 63% positive sentiment. Meanwhile, TikTok mentions, where the phrase is skewered or used humorously, are 42% positive.

For marketers, the rise of “maxxing” is a notable example of the boundlessness of today’s internet trends. Language, memes, and other in-group signals are dynamic in digital spaces, rarely staying exclusive for long. Instead, they can evolve quickly from niche to mainstream discourse, often taking on entirely new meanings along the way. 

That makes early trend detection more critical than ever for brands keeping up with digital culture. Understanding not just what is trending, but where trends originate, how they spread, and how audiences reinterpret them across channels can help brands identify emerging opportunities before they peak. Media and consumer intelligence tools play a key role in that process, enabling marketers to track fast-moving conversations in real time. With that intelligence, marketers can more quickly contextualize cultural shifts, and make more informed decisions about when, how, and even if to engage.

Whether you’re identifying the next viral moment or evaluating how your brand should respond, Meltwater can help you turn cultural intelligence into smarter marketing decisions. 

Get in touch to see how Meltwater can help your team stay ahead of what’s next.

“Maxxing” is increasingly used as shorthand for pursuing an interest, identity, or activity to an extreme degree. According to Meltwater Explore+ data, usage of “maxxing” terms nearly doubled between January and May 2026, reflecting how internet-native slang evolves into mainstream social media vocabulary.

Which platforms use “maxxing” the most?

Reddit and X account for the largest share of “maxxing” mentions online. Meltwater Explore+ data shows those platforms each drove roughly 34% SOV for “maxxing” conversation, while TikTok and Pinterest generated outsized engagement relative to mention volume.

Is “maxxing” still mainly associated with looksmaxxing?

No, the term has expanded far beyond appearance-focused communities. According to Meltwater’s GenAI Lens analysis of a randomized 4,000-post sample, most “maxxing” usage in 2026 was unrelated to physical appearance and instead focused on humor, lifestyle, wellness, hobbies, and everyday experiences.

What kinds of “maxxing” terms are trending?

Lifestyle and humorous variations are among the fastest-growing uses. According to Meltwater’s GenAI Lens analysis, phrases like “birthdaymaxxing,” “lifemaxxing,” “fibermaxxing,” and “tiramisumaxxing” demonstrate how users adapt trend language into playful expressions of identity and enthusiasm.

How can brands use trend detection to monitor internet slang?

Cross-platform monitoring is critical for understanding how online language evolves. Meltwater Explore+ and GenAI Lens analysis help marketers track emerging keywords, measure sentiment shifts, identify high-engagement creators, and contextualize how trends spread across Reddit, X, TikTok, Pinterest, and other platforms.

Why does platform context matter for emerging trends?

The same trend can carry different meanings depending on the platform. Meltwater Explore+ data shows “maxxing” conversation on Reddit and forums skewed more negative, while TikTok and Pinterest usage was more humorous, aspirational, and engagement-driven.

Explore how internet slang spreads and what brands can learn from online trends.

Why 2026 Is the Year of “Maxxing”

LLMs have become hugely influential in consumer decision making, but that impact doesn’t stop at ecommerce. Nearly half of all prospective college students are using AI to help decide which institution is right for them, according to one survey. For universities, that begs the question of what these students are seeing. 

We used GenAI Lens, Meltwater’s AI Visibility Tracking capability, to dig into the data behind LLM recommendations to UK and US students seeking schools with strong alumni networks and the promise of high salaries. 

What we found highlights just how dynamic AI visibility is, shifting across markets, prompts, and even the LLM models themselves. For all organizations, including schools, understanding how that landscape operates is a key capability for remaining competitive.

Stanford and Harvard dominated LLM responses across both prompts and markets analyzed.

How do LLMs recommend the top universities for alumni networks?

How do LLMs recommend the top universities for future earnings?

How do college recommendations vary across LLM models?

How Meltwater AI Visibility Tracking helps universities own their AI visibility

FAQ: AI Visibility in Higher Education Recommendations

<table style="width:auto; border-collapse:collapse; font-family:Inter, Helvetica, Arial, sans-serif; font-size:12px; line-height:1.4; color:#222;">
  <tbody>

    <tr>
      <td style="padding:8px 14px 8px 10px; border:1px solid #d9d9d9; font-weight:700; white-space:nowrap; vertical-align:top; font-size:12px;">
        Capability
      </td>
      <td style="padding:8px 14px 8px 10px; border:1px solid #d9d9d9; font-size:12px;">
        AI Visibility Tracking
      </td>
    </tr>

    <tr>
      <td style="padding:8px 14px 8px 10px; border:1px solid #d9d9d9; font-weight:700; white-space:nowrap; vertical-align:top; font-size:12px;">
        Analysis window
      </td>
      <td style="padding:8px 14px 8px 10px; border:1px solid #d9d9d9; font-size:12px;">
        April 27 – May 18, 2026
      </td>
    </tr>

    <tr>
      <td style="padding:8px 14px 8px 10px; border:1px solid #d9d9d9; font-weight:700; white-space:nowrap; vertical-align:top; font-size:12px;">
        Markets
      </td>
      <td style="padding:8px 14px 8px 10px; border:1px solid #d9d9d9; font-size:12px;">
        US &amp; UK
      </td>
    </tr>

    <tr>
      <td style="padding:8px 14px 8px 10px; border:1px solid #d9d9d9; font-weight:700; white-space:nowrap; vertical-align:top; font-size:12px;">
        Data sources
      </td>
      <td style="padding:8px 14px 8px 10px; border:1px solid #d9d9d9; font-size:12px;">
        <ul style="margin:0; padding-left:16px; font-size:12px;">
          <li style="margin-bottom:1px;">Claude</li>
          <li style="margin-bottom:1px;">DeepSeek</li>
          <li style="margin-bottom:1px;">ChatGPT</li>
          <li style="margin-bottom:1px;">Grok</li>
          <li style="margin-bottom:1px;">Llama</li>
          <li style="margin-bottom:1px;">Google AI Mode</li>
          <li style="margin-bottom:1px;">Google AI Overviews</li>
          <li>Perplexity</li>
        </ul>
      </td>
    </tr>

    <tr>
      <td style="padding:8px 14px 8px 10px; border:1px solid #d9d9d9; font-weight:700; white-space:nowrap; vertical-align:top; font-size:12px;">
        Prompts
      </td>
      <td style="padding:8px 14px 8px 10px; border:1px solid #d9d9d9; font-size:12px;">
        <ul style="margin:0; padding-left:16px; font-size:12px;">
          <li style="margin-bottom:1px;">
            “Which universities have the best alumni networks?”
          </li>
          <li>
            “Which universities lead to the highest salaries?”
          </li>
        </ul>
      </td>
    </tr>

    <tr>
      <td style="padding:8px 14px 8px 10px; border:1px solid #d9d9d9; font-weight:700; white-space:nowrap; vertical-align:top; font-size:12px;">
        Key term definitions
      </td>

      <td style="padding:8px 14px 8px 10px; border:1px solid #d9d9d9; font-size:12px;">

        <p style="margin:0 0 5px 0; font-size:12px;">
          <span style="font-weight:700;">Visibility / Visibility Score:</span>
          A score from 0 to 99 measuring how prominently an entity appears in AI-generated responses
        </p>

        <p style="margin:0; font-size:12px;">
          <span style="font-weight:700;">Mentions:</span>
          the number of instances when key words and phrases appeared within a piece or collection of content
        </p>

      </td>
    </tr>

  </tbody>
</table>

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

Across models in both the US and UK, the US-based Stanford and Harvard Universities have the highest visibility scores, followed by the Massachusetts Institute of Technology (MIT). With all three being internationally recognized schools that have among the highest numbers of billionaire alumni, their prominence highlights how much LLM recommendations currently reflect established prestige, with few surprises in the top rankings. 

More revealing is how each school’s prominence shifts based on prompt and geography, as seen in the table above. Overall, Stanford is the most consistently strong institution across all four prompt breakdowns.

Notably, a non-US competitor only emerges when we narrow our focus to the UK. There, the Institut Européen d'Administration des Affaires, a graduate business school in France better known as INSEAD, achieves an aggregate 43% Visibility Score on the alumni network prompt. On ChatGPT alone, however, its visibility jumps to 94%.
INSEAD’s AI narrative consistently emphasizes a multinational alumni network spanning 90+ nationalities with strong European consulting and finance placement. Among the content informing that narrative is up-to-date, AI-crawlable content, including owned assets like its employment statistics and UK-based alumni network websites.

Why this matters: AI visibility is highly shaped by prompt context and intent, with established reputation playing a large part. In the case of higher education, schools looking to improve their prominence and mention frequency need to build up their online footprint with published assets that speak to their target audience’s biggest concerns. Websites with content like data-backed salary reports and alumni network resources can prove to be the strongest LLM magnets.

Sources featuring user-generated content (UGC) and niche expertise are most influential in LLM recommendations for schools with the top alumni networks. Meanwhile, owned and earned media are largely absent. 

Because “best alumni network” is a relatively subjective distinction, LLMs seek out first-person testimonials to determine its answers. As a result, Reddit tops the source list in the US with 188 citations, followed by Almabase, an alumni engagement CRM, with 177 citations. That top two ranking is swapped in the UK, with 179 citations for Almabase and 174 citations for Reddit.

Almabase’s prominence is particularly notable as it is the only B2B higher education brand with this level of influence. Its website features a robust ecosystem of public-facing blogs and other resources with structured information focused on alumni engagement. Thanks to this established footprint on the topic, LLMs trust it for focused expertise. 

Why this matters: In this higher education use case, prompts that lean more subjective, LLMs prioritize sources where real people can share their lived experiences and subject matter experts can share their insights. For organizations, driving alumni engagement on platforms like Reddit, LinkedIn, and Facebook can have a meaningful impact on AI visibility.

Finance-focused, data-rich, and niche information sources are most influential on LLM responses to the prompt "Which universities lead to the highest salaries?"

Unlike the alumni network prompt, the question of earnings comes down to hard numbers. So, instead of looking to user-generated content, LLMs rely on news and statistical reporting. Specifically, CNBC was the top source in the US and the UK with 199 and 143 citations respectively. Statista was also a trusted source, generating 102 citations in the US and 71 in the UK. 

Interestingly, results for each region also leaned on student-focused websites alongside broader media and data sources. In the UK, Save the Student, a UK-based student deals website, was cited 94 times, making it the second leading source. In the US, Poets & Quants for Undergrads, a news and resource hub for undergraduate business students, achieved 85 citations and ranked third. The prominence of both speaks to the importance of including niche, expert-led digital outlets in earned media strategies.

Another meaningful geographic difference emerged in the terms LLMs used in their responses. In the US, answers primarily associated higher salaries with leading tech, followed by finance (288 vs. 215 mentions). In the UK, finance and tech were almost equally visible (301 vs. 302 mentions). This variation highlights how LLMs adapt recommendations to regional economic and employment priorities.

Why this matters: In the above example, prompts seeking more objective information and data tend to lean on structured content rich with statistics and figures. Organizations looking to influence LLM responses around measurable outcomes should prioritize publicizing relevant data and research, complete with tailored messaging, in the financial and student-focused media outlets that influence them most.

DeepSeek has the most polarized recommendations while others, including ChatGPT (a college student favorite) offer more measured, distributed visibility between the top schools.

For example, in the above visibility score heatmap for US and UK LLM responses to our alumni network prompt, three schools achieve over 90% visibility on DeepSeek, more than on any other model. But while MIT and Stanford score high there, their visibility plunges on ChatGPT, where no school scored above 58%.

Similar discrepancies emerged in our salary prompt, with Stanford scoring 99% in DeepSeek versus just 17% in ChatGPT in the US, and 99% versus 16% in the UK.
Why this matters: There is no single AI search result or visibility score. Instead, different models cite and weigh sources differently, creating meaningful narrative variations. Identifying and addressing these gaps are possible, but it all begins with model- and prompt-specific analyses instead of aggregate ones. 

Use Case 1: Identifying influential LLM sources

Meltwater AI Visibility Tracking, also known as GenAI Lens, automatically identifies the most-cited sources as it analyzes prompts. 

Intelligence Hub archive

How Reddit, X, and Pinterest Are Shaping Completely Different GLP-1 Conversations

AI Search Visibility Report: Earned Media, YouTube, and LinkedIn Are Reshaping AI Visibility

Why 2026 Is the Year of “Maxxing”

AI Visibility in Higher Education Recommendations

How Popular Is TikTok Shop? Data Insights & Trends 2026

The Top Branded Influencer Posts of Coachella 2026

How LLMs Recommend Sunscreen Brands: An Analysis of AI Search Visibility in SPF

Platform Deep Dive: 7 Trends Defining Beauty Reddit Right Now

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