If you are a science marketer at a Life Science or Biotech company, you might have wondered recently: Do hashtags still matter? Well, I did too and so I looked into how a whole group of different B2B marketers would answer this question in 2025.
The short answer is yes, but the role of hashtags has chnaged and continues to evolve. They help in classifying content and signalling community, but they are no longer the lever that influences algorithms. This article presents an evidence-based overview of what to avoid doing now, how B2B differs from B2C, where hashtags might be heading, and how they affect large language models (LLMs).
Briefly, on the use of hashtags right now
Multiple industry sources now view hashtags as useful but secondary. Content Marketing Institute summarizes it well: “Hashtags still matter, but not like they used to… they’re supporting players in a bigger, better visibility strategy.” [1]
Ignite Social Media’s 2025 review clearly states that on Facebook, hashtags “do not significantly impact reach or engagement.” [2] Across different platforms, they now function as a supporting tactic while conversations, video, and SEO carry more weight.
LinkedIn’s own public best-practices page still recommends them for exposure, advising brands to “…identify 3–5 hashtags and add them to your posts when relevant.” [3] This is a useful guideline for B2B teams planning their editorial standards right now.
There are also shifts in platform policies. For instance, X banned hashtags in paid ads in June 2025, [4] although organic posts can still use them. This indicates a trend towards stylistic and platform maturity, even as organic tagging continues.
In the words of the experts
“Hashtags still matter, but not like they used to.” [1]
“Hashtags… are now a supporting tactic rather than the cornerstone of social media marketing.” [2]
“Hashtags are a great way to expose your brand to a wider audience… identify 3- 5.” [3]
The do’s and don’ts for 2025
DO use fewer, highly relevant hashtags. On LinkedIn, two to five at the end of the post remains a solid range. It improves clarity and categorization without clutter. [3]
DO treat hashtags as contextual and cultural markers. They should frame what the post is about for the right audience rather than chase generic volume. CMI and Ignite both emphasize content relevance and the resulting conversation quality over raw tag counts. [1,2].
DO measure performance. Use native analytics on platforms like Sprout Social, Hootsuite and others to identify which hashtags are associated with visibility or conversation in your niche. Brandwatch Enterprise or Meltwater Explore are good examples of tools suitable for corporate and healthcare marketing environments that are highly regulated, offering compliance and privacy controls. (To enhance your measurement framework, explore our article on attribution modelling and data-driven ROI.)
DON’T overstuff. Overuse hurts readability and does not unlock distribution. This aligns with guidance found across LinkedIn resources and industry roundups. [3]
DON’T rely on hashtags for reach. Conversations, saves, dwell time, and video features now carry more algorithmic weight. [2]
DON’T assume every platform treats hashtags the same way. X still supports them in organic posts. Their impact on Facebook is minimal. Instagram has reduced their discovery influence compared with earlier years, so check your platform marketing channel’s mix before setting internal rules for your team. [2]
B2B vs B2C: how hashtag strategies differ
On LinkedIn, where most B2B engagement now takes place, hashtags primarily help audiences discover professional content by topic. LinkedIn’s recommended best practices advise using only a few targeted hashtags related to your products, services, or areas of expertise, noting that this helps posts “show up in relevant searches and feeds” rather than drive broad viral reach. [3]
For a deeper look at how precision targeting drives B2B success in scientific markets, read Supercharge B2B Science Marketing with Account-Based Marketing.
B2C marketers, by contrast, continue to use hashtags as participation tools, to encourage user-generated content, increase campaign visibility, or connect with cultural moments. Platforms like Instagram still reward creative participation and interaction more than sheer tag volume. Recent benchmarking studies show that “hashtag stuffing” yields diminishing returns, while short-form video, interactive formats, and conversation quality through comments deliver stronger engagement scores. [2][6]
The future of hashtags: automation, intelligence and invisible tagging
Hashtags are entering a new phase driven by computer science rather than marketing. Researchers are developing AI systems that automatically generate and adapt hashtags as topics evolve, addressing the inconsistency and effort involved in manual tagging.
At the University of Calabria, the H-ADAPTS framework identifies topic shifts and updates hashtag recommendations in real time. The research team there found that static tag lists quickly become outdated, fragmenting data, confusing analytics, and disconnecting content from the language professionals actually use, which is a serious issue in fast-moving, regulated fields like MedTech and Life Sciences. H-ADAPTS uses natural-language processing and trend-shift detection to keep metadata current [8].
The Chinese Academy of Sciences developed the RIGHT model that combines a large language model with a database of trending tags to generate more precise, consistent hashtags. In trials, it consistently produced hashtags that were better aligned with content meaning and current user behaviour than traditional recommendation systems [9].
Both projects aim to replace static tagging with dynamic, intelligent metadata. For marketers, that means better categorization of technical content, more reliable analytics, and automated consistency across campaigns.
Visible hashtags may fade, but their logic will endure as machine-generated metadata powers personalization, analytics, and AI search. X’s removal of hashtags from paid ads already points to this shift. Hashtags are evolving into an invisible data layer that quietly organizes how information is located and understood. That same metadata logic underpins how LifeScience companies scale digital growth; our Biotech eCommerce blueprint outlines how structured data enhances long-term visibility and performance.
What developers are doing that marketers can borrow
Developers are increasingly treating hashtags as functional metadata, using them to organize, automate and connect information.
- Auto-tagging at scale: Academic research demonstrates how multimodal AI models can generate captions and hashtags simultaneously – a technology that is already being adopted into marketing platforms for faster, more accurate tagging. [10,11]
- Trend-adaptive systems: Dynamic models identify emerging topics and update tags automatically, helping marketers to keep their content aligned with fast-moving scientific conversations.[10]
- Retrieval-augmented tagging: AI tools that extract candidate tags from knowledge bases and refine them for context could soon drive enterprise content systems and scheduling. [10]
For B2B teams in regulated industries, the practical takeaway for 2025 and 2026 is this: create a small taxonomy of reusable tags that map to your product areas, standards, and buyer roles. Use them consistently across your CMS, social scheduling tool, and knowledge base so that both humans and machines can find and organize your content quickly.
For a quick guide on reviewing where your leads are in their journey to help improve personalization and automation efforts, read: Lead mapping guide for science marketers.
Are hashtags useful for LLMs?
Hashtags do not instruct an LLM on how to rank or promote your content. In general LLM training, they are simply tokens like any other word. Instead, large language models learn associations such as #MedicalDevices co-occurring with words like ‘sterilization’, or ‘quality’, and regulatory topics. This makes tags weak as instructions for LLMs but useful as context builders and validation points.
Where hashtags (or their behind-the-scenes equivalents, structured tags) really start to matter is in how AI finds and uses information. In most organizations, retrieval simply involves searching: an AI system scans internal documents, databases, or campaign files to locate what matches a question. Retrieval-augmented generation (RAG) takes that a step further. It doesn’t just find information; it reads it, interprets it, and writes an answer or summary based on what it has found.
For this to work well, your marketing content needs clear and consistent tagging. These tags act like road signs, telling the AI where to look and what’s relevant. It’s the same idea researchers are exploring in “retrieval-augmented hashtag” models, where they combine search and language generation to create hashtags that reflect what people are actually talking about in real time. In both cases, the principle remains the same: well-structured tags make both human and machine discovery quicker, smarter, and more reliable.
Expect more tools that silently convert visible hashtags into standardized metadata fields. If you invest in a simple taxonomy now, your team will be prepared for when these features are integrated into enterprise content systems (or when you finally purchase one).
Action checklist for science marketers
- Standardize your core hashtags. Identify two to five industry- specific and branded hashtags for LinkedIn, and keep a short, approved list for each product line in your editorial guide.
- Use hashtags to curate meaning and community. Treat them as context cues, rather than growth hacks. Place them at the end of your posts and keep them clear. E.g. #PatientSafety and #QualityAssurance as opposed to #patientsafetyfirstforall and #QA.
- Measure and refine performance. Review hashtag performance quarterly and retire underperformers, testing one new niche tag per cycle. Use a unified analytics platform (Hootsuite, Brandwatch, etc.) if you manage multiple channels.
- Prepare for automation. As AI capabilities become embedded in enterprise marketing platforms, start asking vendors how their systems handle automatic tagging, trend tracking, and metadata export. You don’t need to build these tools yourself, but understanding how they work and how they align with your approved terminology will help safeguard your content strategy.
- Lay the groundwork for AI-ready marketing. Align your existing tags and taxonomies with your internal knowledge base now, so when AI tools are integrated into your marketing platforms, they can accurately filter and generate content by product, indication, regulation, or audience. This ensures that your AI tools will deliver more relevant results, faster, personalization will be excellent, and analytics will remain unified.
The bottom line: #Conclusion
Hashtags are not dead. They have a narrower but still useful role in 2025. Use them to organize and categorize content, to signal the right community, and to create a consistent metadata trail for both humans and machines. Focus most of your energy on storytelling, expert insights, providing value, engaging formats, and aim to start and participate in conversations. [12] The companies that succeed will combine simple editorial discipline today with forward-looking metadata that plays nicely with the AI systems of tomorrow. Learn more about building that foundation in Creating a content strategy for science and tech marketers.
References
1. Content Marketing Institute (2025). How to Use Hashtags on Social Media Now.
2. Ignite Social Media (2025). Hashtags in 2025: Are They Still Relevant for Social Media Marketing?
3. LinkedIn Marketing Solutions (2025). LinkedIn Pages Best Practices.
4. X Business / Twitter (June 2025). X Ads Policy Update: Removal of Hashtags in Paid Ads.
5. Search Logistics (2025). Hashtag Statistics: Should You Use Hashtags in 2025?
6. Sprout Social (2024). How to Measure Hashtag Performance: Hashtag Analytics Guide.
7. Determ Blog (2025). The Ultimate Guide to Hashtag Analytics in 2025.
8. H-ADAPTS Framework. Hashtag Recommendation Using Dynamic Trend Shift Detection. arXiv preprint arXiv:2504.00044 (2025).
9. Z. Zhai et al. (2024). Retrieval-Augmented Hashtag Recommendation. arXiv preprint arXiv:2409.16614.
10. A. Jain et al. (2024). Automatic Hashtag Recommendation Using Transformers and Graph Neural Networks. arXiv preprint arXiv:2411.08464.
11. Shetty, N., & Li, Y. (2024). Detailed Image Captioning and Hashtag Generation. Future Internet, MDPI.
12. Hibu (2025). Are Hashtags Dead for Social Media in 2025?
