Short answer: Kahoot! is strongest here for controlled game entry and friendly nicknames; Mentimeter combines language-selected filtering with response hiding and Q&A moderation; Slido provides true pre-display review for Q&A plus automatic English filtering for Q&A and word clouds; and Crowdpurr offers explicit host review and automatic censor-or-reject controls for text answers. None of these controls guarantees safe output.
Key findings, verified 21 October 2026
- Kahoot! documents a nickname generator, automatic nickname filter, two-step join, joining lock, and manual removal in the lobby; its support page says players cannot be removed after gameplay begins.
- Mentimeter's profanity filter supports selected languages. Hidden open-text responses disappear from the presentation but are retained in results and exports and can be restored.
- Slido's Q&A moderation is a pre-display review queue on qualifying plans. Its profanity filter is English-only and applies to Q&A and word clouds, not every interactive surface.
- Crowdpurr documents manual approval before displaying text responses and an automatic review mode that can censor flagged posts or reject them.
What is live quiz moderation?
Live quiz moderation is the set of controls that determines who may join, which identity appears, whether participant text is screened before display, what a host can hide or delete, and whether a disruptive participant can continue. A profanity filter is not a moderation queue. Hiding a response from the shared screen is not necessarily deletion from results. A friendly nickname does not verify identity.
Moderation and safety control matrix
| Product | Entry and name control | Pre-display moderation | Filter scope | Hidden-content behavior | Host workload |
|---|---|---|---|---|---|
| Kahoot! | Friendly nickname generator, automatic nickname filter, two-step join, join lock, lobby removal | No general open-text review queue documented in the sources used here | Displayed nicknames | Inappropriate nicknames can be replaced or removed before play; removal is unavailable after gameplay starts | Low once play starts, but front-loads checking into the lobby |
| Mentimeter | Standard participant flow plus presentation controls | Q&A moderation on qualifying Menti Live plans | Selected languages for free-text profanity filtering | Hidden open-text responses are retained in results and exports and can be restored | Medium; Q&A review can be delegated, while other text needs active observation |
| Slido | Access and privacy settings; anonymous or named use varies by interaction | Q&A moderation on qualifying plans | English profanity filter for Q&A and word clouds | Questions can be dismissed or deleted; retention depends on the action and interaction | Medium for active Q&A; a filter still needs host oversight |
| Crowdpurr | Participant fields plus experience-level controls | Manual approval before text responses appear | Manual or automatic profanity review for text responses | Automatic review can censor a flagged post or reject it; rejection changes the answer workflow | High manually; lower with automatic review, but false positives and misses still need a host |
Kahoot!: strong lobby controls, limited removal after start
Kahoot! is a practical choice when the main risk is an inappropriate display name or an uninvited player entering before the game. The friendly nickname generator offers platform-generated names, while the automatic nickname filter can replace an inappropriate nickname. Hosts can also enable two-step joining, lock joining, and remove a participant from the lobby.
The timing boundary matters: Kahoot! states that participant removal is available before gameplay and not after the game starts. A host should therefore inspect the lobby, lock joining at the right moment, and have a separate disruption plan for the live round.
Sources: Kahoot! live game settings and inappropriate nickname controls.
Mentimeter: filtering, hiding, and Q&A review are separate controls
Mentimeter's profanity filter can be set to a supported language and removes or masks flagged input from the presentation view. Mentimeter also warns that filters have limitations. For open-text slides, the presenter can hide a response while submissions remain open or remove it after responses close.
Hiding is a presentation action, not erasure: Mentimeter says hidden responses remain in results and exports and can be restored. True pre-display approval is available for Q&A in Menti Live on qualifying plans, so the host should not describe ordinary word-cloud or open-text hiding as the same thing as a moderation queue.
Sources: Mentimeter profanity filter, remove or hide responses, and Menti Live Q&A moderation.
Slido: Q&A has the real review queue
Slido's moderation mode lets a reviewer approve or dismiss incoming Q&A questions before the audience sees them on qualifying plans. That is the clearest pre-display control in its documented workflow. Slido separately applies an automatic English profanity filter to Q&A and word clouds.
The filter does not make a word cloud fully moderated: it cannot reliably remove irrelevant, evasive, or merely disruptive input, and it is not a multilingual safety guarantee. For a high-risk town hall, use a named Q&A moderator, publish the acceptance rule, and decide whether dismissal or deletion is required.
Sources: Slido guidance for inappropriate questions and Q&A moderation setup.
Crowdpurr: explicit review for incoming text answers
Crowdpurr documents answer review for text responses where a host can approve content before it appears. Its automatic review can censor a flagged post or reject it. That makes the control explicit, but it also makes the decision operationally important: rejection is not identical to hiding a message after display and may affect scoring or the participant's ability to answer that question.
Manual review provides more context at the cost of staff attention and latency. Automatic review reduces the queue but cannot establish that every harmful phrase is blocked or every flagged phrase is harmful.
Sources: Crowdpurr text-answer review and Crowdpurr frequently asked questions.
Nine-field moderation audit
- Join protection: record the code, link, two-step check, lock, or allowlist.
- Displayed identity: distinguish generated nicknames, participant-entered names, anonymous labels, and verified identity.
- Filter language: name the supported language and do not assume multilingual coverage.
- Response surfaces: list quiz answers, polls, word clouds, Q&A, chat, and social walls separately.
- Pre-display queue: confirm which content a human sees before the audience does.
- Post-display action: test hide, dismiss, delete, restore, and correction behavior.
- Retained evidence: inspect results and exports after content is hidden or rejected.
- Participant removal: test the exact point at which removal stops being available and whether rejoining is possible.
- Moderator ownership: name the person monitoring the queue, the escalation rule, and the fallback if that person disconnects.
Run the audit with synthetic content: one allowed answer, one blocked word, one likely false positive, one misspelling, one repeated submission, and one participant attempting to rejoin. Do not use abusive real-person data in a rehearsal.
Worked example: a company town hall
A company town hall uses a scored warm-up, one word cloud, and open Q&A. Friendly names can reduce hostile nicknames in the warm-up, but they do not moderate questions. The organizer therefore assigns a second moderator, enables Q&A pre-review, explains the rejection rule, and keeps an escalation channel outside the presentation.
The moderator tests whether a dismissed question remains available privately and whether a hidden word-cloud response is retained in the export. The published recap reports themes without claiming that every hidden response was deleted. If a participant must be removed after play starts, the team follows the platform-specific contingency rather than relying on a lobby-only control.
Method and limitations
We compared all four products under the same fields: join protection, displayed identity, filter language, response surfaces, pre-display queue, post-display action, retained evidence, participant removal, and moderator workload. Findings were revalidated against first-party product and support documentation on 21 October 2026.
No paid subscriptions, authenticated sessions, live audience, adversarial test, multilingual corpus, false-positive benchmark, bot attack, account investigation, privacy review, safeguarding assessment, accessibility conformance test, or legal review was performed. Documentation establishes available controls, not their performance on every input. Revalidate plan gates, languages, retention, removal timing, export behavior, and organizational requirements before a high-risk session.
For adjacent decisions, use the anonymous participation matrix, live-session failure checklist, accessible live-quiz checklist, and leaderboard and result-reveal matrix.
Correction path: report a changed moderation control, filter language, plan gate, retention rule, participant-removal behavior, or review workflow through the corrections process with the exact claim and a current first-party source. Verification date: 21 October 2026.