Glossary: Bot Filtering

What is bot filtering?

Bot filtering is the process of detecting automated traffic and deciding whether to exclude, label, or analyze it separately. Filtering can improve estimates of human product usage, but every rule can also remove legitimate activity or miss sophisticated automation.

Web and onchain automation

Web analytics bots include crawlers, uptime monitors, scrapers, and scripted requests. Onchain automation is broader: contracts, arbitrage systems, relayers, trading bots, and scripted wallets can create real transactions while still not representing a human product user. These cases require different detection signals and should not be collapsed into one generic bot category.

How to apply filters

Use signals such as known crawler user agents, request patterns, event cadence, data-center sources, contract type, or repeated transaction behavior. Preserve an exclusion reason and, where possible, retain a filtered view for auditing. Validate rules against known human journeys and review changes over time.

Example

A site excludes known search-engine crawlers from visitor metrics but reports high-frequency arbitrage wallets separately from consumer cohorts of wallet addresses. This keeps web reach and human activation measures clearer without erasing real onchain activity.

Common mistakes

Do not assume every high-frequency wallet is malicious or every smart contract is a bot. Avoid silently changing filters between reporting periods. State whether bot filtering applies to sessions, visitors, wallet profiles, or transactions.

Related reading

Explore Bot Traffic, Transaction Frequency, Sybil Wallet, and Unique Visitor.

FAQs

What is bot filtering?

Bot filtering applies rules or classifications to separate or exclude automated activity from a measurement. Rules should be documented because crawlers, trading bots, scripts, and other automation can be legitimate or relevant depending on the metric.

How can analytics identify bot activity?

Signals can include known crawler identifiers, request patterns, event timing, repeated transaction behavior, and address or contract context. No single signal proves that a visitor or wallet is a bot; use multiple indicators and track confidence.

Should bot activity always be removed from reports?

Not always. Keep raw and filtered views when automation matters to the question, such as protocol load or trading volume. Apply consistent rules and state whether filtering affects visits, accounts, addresses, or transactions.