How Many Memes Are in the World of Crypto?

how many memes are in the world: meme coins explained

Lead: A common question in crypto communities is “how many memes are in the world?” In the context of finance this mostly asks how many meme coins (and, secondarily, meme-driven stocks) exist. There is no single authoritative number — estimates vary from several thousand to tens of thousands depending on chains and counting rules. This article explains definitions, why counting is difficult, measurement methods, estimated ranges, market impact, risks, and a reproducible approach you can run with Bitget-friendly tooling.

Overview

The phrase "how many memes are in the world" surfaces when investors, researchers and journalists try to measure the size of the meme-driven corner of capital markets. In crypto, "memes" generally mean meme coins or meme tokens: community- or joke-branded cryptocurrencies (Dogecoin, Shiba Inu, Pepe and many thousands more). In equities, "meme stocks" are listed companies whose price movements are driven by retail social-media momentum (notable examples in past years include GameStop and AMC). This page focuses on meme coins as the primary interpretation and briefly notes meme stocks where relevant.

Counting matters because the number of meme tokens influences market surveillance, consumer-protection priorities, research reproducibility, and platform listing policies. For exchanges, wallets, and analytics teams, a defensible snapshot and transparent methodology reduce ambiguity about scope and risk.

A quick, repeated reminder of the central SEO question: "how many memes are in the world" — asked here as shorthand for "how many meme coins are in the world" — has no single answer; the best we can provide are time-stamped estimates, categorized methods, and a reproducible workflow.

Definitions

What is a "meme coin"?

A "meme coin" or meme token is a cryptocurrency whose identity, marketing and community focus derive from internet memes, cultural jokes, or strong social branding rather than from a clearly defined fundamental utility. Key characteristics often include meme-based names/imagery, emphasis on community/social channels, easy token creation on smart-contract platforms, and sometimes tokenomics that encourage viral distribution. Examples commonly cited are Dogecoin and Shiba Inu, but the category includes many smaller or short-lived projects.

What is a "meme stock"?

A "meme stock" is an equity whose trading activity and price dynamics are driven primarily by retail-led social-media coordination and heightened narrative-driven attention rather than traditional fundamentals. Meme stock episodes highlight the overlap between retail investor behavior and viral content; they are a related but distinct phenomenon from meme coins.

Related categories

• Meme NFTs: individual images/collections that gained value through meme culture.
• Community or social tokens: tokens issued to a community with some utility (may or may not be meme-branded).
• Novelty tokens: ephemeral token launches primarily for fun or marketing.
• Standard utility tokens: tokens designed with product/utility intent (outside the pure meme bucket).

For the remainder of this article, "meme" refers primarily to meme coins and meme tokens unless we explicitly note meme stocks or NFTs.

History and evolution

Origins

Meme coins trace back to early-2010s internet culture with Dogecoin as the most-cited origin story: a light-hearted crypto that later grew into a large-market participant. Meme coins evolved from jokes into community-organized assets that sometimes attracted substantial speculative capital.

Growth waves

Key phases include:

• Early novelty: small, community-driven projects attracting niche attention.
• 2020–2022 explosion: easy smart-contract deployments on EVM-compatible chains, growth of DeFi and AMMs, and broad retail interest led to a rapid proliferation of meme tokens.
• Ongoing proliferation: multi-chain launches, bridging, and repeat meme cycles keep new meme tokens continually appearing.

These patterns show why people repeatedly ask, "how many memes are in the world" — because the population is dynamic.

Why counting is hard — conceptual challenges

Rapid creation rate

New token contracts can be deployed in minutes on many blockchains. Developer tools and templates enable a high throughput of launches, so the universe of possible meme tokens expands constantly.

Lack of a central registry

There is no single global database that classifies every token by purpose or theme. Data is fragmented across explorers, DEX listings, centralized exchange listings, and community-curated trackers.

Ambiguity of classification

Whether a token is a meme coin is often subjective. Some tokens use meme imagery but have real utility; others are marketed as community tokens but lack on-chain activity. Platforms and trackers use different tags and thresholds.

Dead, abandoned, or duplicate tokens

Forks, re-deploys, renames, and scam copies make deduplication and lifecycle assessment non-trivial.

Because of these difficulties, any answer to "how many memes are in the world" must be time-stamped and methodology-bound.

Data sources and measurement methods

There are several practical approaches to counting meme tokens; each has trade-offs.

On-chain explorers and logs

Use blockchain explorers (for example, Etherscan, BscScan, Solscan) to enumerate token contracts on a chain. Explorers provide contract creation timestamps, verification status, and transfer histories. For a reproducible snapshot, query all token contract creations up to a block height or timestamp and then filter by metadata.

Aggregators and market trackers

Services such as CoinGecko and CoinMarketCap include token tags and market-cap/volume metrics; their "meme" tag can be a starting point for inclusion. These aggregators implement vetting and can be used to collect market-cap and volume figures for tagged tokens.

DEX listings and liquidity pools

Automated market maker (AMM) pool indices and DEX tools list tokens with liquidity pairs. Searching AMM pools provides a broad view, including many tokens not yet covered by aggregators.

Community/tagging approaches

Community-curated lists and social tags (e.g., Reddit/Telegram/Discord lists) are useful for capturing tokens that rely on viral channels rather than formal listings.

Automated scraping and classification

Large-scale identification can use heuristics: token name keywords (dog, shiba, pepe, flo), logo similarity, presence of certain social links, and activity metrics. Natural language processing and image similarity can scale classification but require careful tuning to reduce false positives.

Classification criteria — toward an objective count

Because the raw universe is noisy, define objective filters for counting. Here are practical criterion layers you can use and adjust.

Possible objective filters

• Contract verification: contract source code verified on-chain.
• Chain and standard: ERC-20, BEP-20, SPL, etc.
• Nonzero liquidity: token has at least one active liquidity pool above a minimal TVL threshold.
• Transfer activity: >N transfers in the prior M days.
• Market visibility: listed on at least one major aggregator with a "meme" tag or community-confirmed listing.
• Social presence: an official community channel (Twitter/X, Telegram, Discord) linked from contract metadata or site.

Each filter tightens the set; conservative counts require several filters, while broad counts accept minimal metadata.

Suggested thresholds for counting

• Conservative count: verified contract + active liquidity (> $1,000) + >10 transfers in last 30 days + at least one aggregator listing. This reduces false positives and excludes many dead or scam contracts.
• Moderate count: verified contract + any liquidity + some transfers or social link.
• Broad count: any contract with meme-related keywords or imagery regardless of activity.

When answering "how many memes are in the world," specify which threshold you used.

Estimated counts and ranges

A short, careful answer: depending on your criteria and snapshot time, counts range from several thousand (conservative) to tens of thousands (broad).

• As of 2025-11-30, industry trackers and community reports cited by PassiveSecrets estimated that aggregator-tagged meme tokens across major EVM-compatible chains numbered in the low tens of thousands when using broad inclusion rules; conservative snapshots that required verified contracts and minimum liquidity typically identified several thousand tokens. (Source: PassiveSecrets, reporting date 2025-11-30.)

• As of 2025-11-27, ElectroIQ summarized market commentary noting the meme-coin market remains fragmented: a handful of large meme tokens captured most market cap while tens of thousands of smaller meme-labeled contracts existed with negligible liquidity and high turnover. (Source: ElectroIQ, reporting date 2025-11-27.)

• As of 2025-11-25, Amra And Elma highlighted that well-known meme coins such as Dogecoin, Shiba Inu and Pepe occupy outsized attention and capital relative to thousands of smaller projects launched for short-lived viral campaigns. (Source: Amra And Elma, reporting date 2025-11-25.)

Because of the rapid turnover, snapshot language is essential: any figure should be accompanied by the chain list, timestamp, and inclusion criteria.

Temporal volatility

The universe changes daily. New launches, rug pulls, burned tokens, and renamings create churn. A dataset taken today may differ materially in size and composition one week later.

Example sampling approaches used by researchers

• Aggregator-tag sampling: collect tokens tagged "meme" on CoinGecko/CoinMarketCap and deduplicate.
• AMM pool crawl: enumerate all pools on a chain and apply keyword/image filters to token metadata.
• Contract-creation crawl: take all token contract creations and filter using on-chain heuristics and off-chain social link validation.

Each approach trades coverage for noise control.

Market size and economic impact

Market-cap concentration

Market-cap distribution in meme markets is usually highly concentrated: a small set of headline meme coins (e.g., Dogecoin, Shiba Inu, Pepe) typically hold a dominant share of total meme market capitalization, while the long tail contains many micro-cap, low-liquidity tokens.

Meme market / broader industry value

Industry write-ups and brand-marketing studies have estimated the broader meme economy (including NFTs and meme marketing) in the mid- to high- billions by several measures in the mid-2020s. These figures often mix on-chain token market caps with cultural/marketing valuations and should be interpreted accordingly. As of 2025-11-20, BrandWell and similar marketing analyses emphasized the cultural value of meme content but noted methodological differences versus token-native market-cap measures. (Source: BrandWell/Marketing LTB, reporting date 2025-11-20.)

Role in volatility and retail participation

Meme tokens and meme stocks exhibit outsized volatility and are associated with intense retail flows. That amplifies both upside narratives and downside risk. Transaction volumes for top meme coins can be substantial on high-attention days, while the long tail often records little trading activity.

Risks, abuses, and quality control

The meme-token space carries a set of structural risks.

Fraudulent tokens and rug pulls

High issuance speed and low audit coverage mean many meme tokens are created for quick exit scams. Rug pulls (where liquidity is withdrawn by deployers) are common enough that they form a top consumer-protection concern.

Wash trading and deceptive liquidity

Apparent trading volumes or liquidity can be manipulated by wash trading or circular transactions that do not reflect genuine user demand.

Legal and consumer-protection concerns

Regulators have expressed concern about investor protection, and enforcement actions have targeted deceptive or securities-like token offerings. Platforms and researchers must apply careful vetting when presenting counts or market metrics.

Regulation and exchange responses

Exchange listings and delistings

Centralized exchanges (including Bitget) apply listing criteria and due diligence. Listing decisions affect visibility and liquidity for meme tokens; delistings can materially change a token’s accessible market.

When evaluating meme-token counts, note whether you include only exchange-listed tokens or also DEX-only tokens.

Regulatory scrutiny

Securities-law frameworks and consumer-advisories vary by jurisdiction; some authorities issue warnings about speculative and highly promotional tokens.

Industry self-regulation

Audits, reputation services, verified badges on aggregators, and community governance can reduce risk but are not foolproof. Audits and verified source code are helpful filters in counting.

How to research or produce your own count (practical guide)

Below is a reproducible workflow you can run to produce a time-stamped snapshot count of meme tokens. The steps emphasize reproducibility and conservative defaults; you may loosen thresholds for broader coverage.

Step-by-step methodology

1. Define scope and chains to include (e.g., Ethereum mainnet, selected EVM chains, Solana, etc.).
2. Select snapshot timestamp (e.g., 2025-12-01T00:00:00Z) and record block heights for chains included.
3. Pull contract-creation data up to the snapshot block/timestamp for each chain.
4. Filter contracts by token standard (ERC-20, BEP-20, SPL, etc.).
5. Apply initial keyword/image metadata filter for meme-like tokens (optional for broad counts).
6. Check contract verification status and gather token metadata (name, symbol, decimals).
7. Query liquidity: identify AMM pools or centralized-exchange listings and record TVL or quoted liquidity. Exclude tokens with liquidity below your threshold if using conservative rules.
8. Check activity: number of transfers, active wallet holders in the prior 30/90 days.
9. Check aggregator listings and tags (e.g., whether CoinGecko or CoinMarketCap applied a "meme" tag) and record market cap/day volume where present.
10. Deduplicate by contract address and canonicalize tokens across chains if bridging introduces duplicate identifiers.
11. Produce counts for each classification level (conservative, moderate, broad) and publish CSV/JSON with timestamp and filters used.

Tools and resources

• Blockchain explorers APIs: Etherscan, Solscan, etc., for contract and transfer histories.
• Aggregator APIs: CoinGecko, CoinMarketCap for tags and market data.
• AMM and DEX indexers: graph-indexed endpoints and subgraph queries to enumerate pools.
• Indexers and nodes: The Graph, chain-specific indexers for large crawls.
• Data processing: Python, Node.js, and SQL for deduplication and analysis.

When building datasets, record API endpoints, query parameters, and snapshot timestamps to allow reproducibility.

Caveats and reproducibility

Document everything: the exact timestamp, chain block heights, inclusion/exclusion rules, and data source versions. A published count without reproducible rules is of limited research value.

Notable examples

Major meme coins

• Dogecoin — early meme pioneer and long-running meme asset.
• Shiba Inu — an icon of meme-token proliferation with organized communities and ecosystem experiments.
• Pepe and Floki — show how pop-culture names can drive rapid attention.

These headline projects dominate attention and capital compared with the long tail.

Meme stocks

• GameStop and AMC serve as prominent examples of equities whose price dynamics were driven by social-media retail coordination. These are relevant to the broader meme-finance story but are outside the primary token-count focus here.

Research and academic perspectives

Academic work on meme diffusion, social contagion, and market microstructure provides tools to analyze meme markets. Cross-disciplinary studies often combine social-media datasets with on-chain transaction data to map how meme narratives map to capital flows.

Open research questions include: how to define a robust taxonomy of meme tokens, what long-term price-formation mechanisms operate in meme markets, and how regulation affects market structure.

See also

• Internet meme
• Meme stock
• Token standard (ERC-20, BEP-20, SPL)
• Blockchain explorers (Etherscan, Solscan)
• Coin aggregators (CoinGecko, CoinMarketCap)

References

• PassiveSecrets — industry coverage of meme-token proliferation and market dynamics. As of 2025-11-30, PassiveSecrets reported aggregated tracker estimates and discussed the difference between broad and conservative counting approaches. (Source: PassiveSecrets, reporting date 2025-11-30.)
• ElectroIQ — market commentary summarizing concentration effects in the meme-token market and noting fragmentation. As of 2025-11-27, ElectroIQ emphasized that a handful of tokens capture most market cap while a long tail persists. (Source: ElectroIQ, reporting date 2025-11-27.)
• Amra And Elma — feature articles noting prominent meme coins and community dynamics. As of 2025-11-25, Amra And Elma highlighted major meme-token cases and community-led events. (Source: Amra And Elma, reporting date 2025-11-25.)
• BrandWell / Marketing LTB — research and commentary on meme-culture and marketing value. As of 2025-11-20, BrandWell contextualized the cultural value versus token-market measures. (Source: BrandWell / Marketing LTB, reporting date 2025-11-20.)

Note: the reporting dates above timestamp the secondary-source context used in this article. All numerical estimates should be considered provisional and dependent on the snapshot methodology described earlier.

Notes and limitations

Any count answering "how many memes are in the world" must be explicitly time-stamped and accompanied by the inclusion rules. Differences in chain coverage, token standards, and liquidity thresholds lead to materially different counts. This article avoids absolute claims and focuses on reproducible methods and ranges.

How Bitget users and researchers can act

If you are analyzing meme tokens using Bitget tools or managing risk across meme markets:

• Use Bitget's token listing and market data (where available) as a vetted feed for higher-confidence inclusion.
• Secure assets with the Bitget Wallet when interacting with meme tokens, especially new contracts with limited audit history.
• For researchers building datasets, publish your snapshot time, chain list, and filters; include Bitget-market data where relevant to show centralized-exchange visibility.

Explore token discovery on Bitget and secure asset custody with Bitget Wallet to reduce exposure to risky deployer-controlled liquidity pools. This advice is informational and not investment advice.

Practical appendix: example scripts and queries (pseudocode)

Below are example pseudocode steps for an EVM chain using an explorer API and aggregator API to build a conservative snapshot. Replace API_KEY and endpoints per your environment.

1. Determine snapshot block for timestamp "2025-12-01T00:00:00Z".

2. Query all token contract creations up to that block (paginated). Save contract addresses.

3. For each contract address:

   • Check contract verification status via explorer API.
   • Fetch name, symbol, decimals.
   • Query transfer count and holder count in the prior 30 days.
   • Query AMM indexers for liquidity pools involving the token. Record TVL.
   • Query aggregator API for listings and tags.

4. Apply filters:

   • Verified == true
   • TVL >= $1,000 OR market cap reported by aggregator
   • Transfers_last_30_days >= 10
   • Aggregator_tag includes "meme" OR name/symbol matches meme-keywords

5. Deduplicate and export CSV (contract_address, chain, name, symbol, verified, tvl_usd, transfers30d, holders, aggregator_tags).

6. Count rows and record snapshot metadata (timestamp, chain blocks, API versions).

Sample query snippet (pseudocode)

• explorer_get_contracts(block_before=SNAPSHOT_BLOCK)
• for contract in contracts: metadata = explorer_get_token_metadata(contract) transfers = explorer_get_transfer_count(contract, since=SNAPSHOT_TS - 30d) holders = explorer_get_holders(contract) pools = amm_indexer_get_pools(token=contract) tg = aggregator_get_tags(contract)

This pseudocode is a reproducible starting point; production-ready code should handle rate limits, retries and robust error handling.

Final notes and action steps

If you asked "how many memes are in the world" expecting a single number: the right answer is that there is no single definitive number. Use a clear methodology, pick conservative or broad inclusion rules depending on your needs, and always timestamp your snapshot.

For practitioners who want to track meme-token markets while minimizing exposure to scams, combine on-chain filters (verified contracts, transfer activity, liquidity thresholds) with aggregator tags and reputable custody solutions. Bitget and Bitget Wallet can be components of a safer analysis and custody workflow.

To learn more or to run a reproducible snapshot with Bitget-friendly tooling, consider using the appendix pseudocode as a starting point and reach out to Bitget support or documentation for platform-specific market-data feeds.

Note on source reporting dates: As of the dates listed in the References section above, the cited outlets summarized market fragmentation and gave snapshot-style estimates that motivated the methodology and ranges presented here. Any hard counts reported elsewhere should be validated against the exact snapshot methodology used.

Call to action: Want a reproducible snapshot template or a runnable script tailored to your chain list? Request a data-methodology draft and sample Bitget-friendly export format to get started.