Reddit Conversation Monitor — Sprinklr Alternative

Monitor public Reddit conversations for explicit brand or topic queries. Export deduplicated posts, subreddit details, engagement, timestamps, source URLs, and rule-based sentiment and urgency cues for triage. For social-listening teams, each dataset row is one retained Reddit conversation with all matched watch queries preserved.

Workflow: put the results to work

Set explicit watch queries and exclusions, then inspect retained conversations with the subreddit and source timestamps. Use sentiment and urgency labels to order the reading queue, not as conclusions about people. Deduplication retains the queries that matched each source post.

Best fit

Use this Actor when a researcher, founder, product team, or communications lead needs a bounded public Reddit pulse in JSON for an explicit watchlist. It is useful when the next step is an internal review, spreadsheet, alert, or another API workflow that needs source URLs and clearly scoped provenance.

Choose Sprinklr or another full social-CXM suite when you need publishing, managed engagement, customer-care case handling, advertising, CRM/DAM/BI integrations, team governance, approval workflows, multi-channel dashboards, historical workspace reporting, global coverage, licensed sources, or monitored private/connected accounts.

What you get

Each dataset row is one public Reddit post returned by the configured search route. The Actor does not collect private messages, bypass login walls, scrape connected accounts, or infer a customer identity.

Field	Meaning
title, text, sourceUrl, publishedAt	Source-supplied conversation details and public source URL
matchedQueries, subreddit, author	Watch-query and available public community context
engagement	Source-supplied score, comment count, and upvote ratio when present
sentiment, intent, urgency	Transparent deterministic triage signals; not a human or AI decision
provider, sourceRequestUrl, collectedAt	Collection route and freshness provenance

Null fields mean the public source did not supply a value. The Actor does not invent missing timestamps, authors, engagement, or source metadata.

Input

{
  "watchQueries": ["OpenAI", "Anthropic"],
  "timeframe": "week",
  "sort": "new",
  "maxItems": 50,
  "excludeKeywords": ["giveaway"],
  "responseFormat": "concise"
}

maxItems is a whole-run cap across every supplied watch query. It limits persisted rows and therefore bounds the main event charge. new is appropriate for a current pulse; use top or comment_count when manual review needs more established conversations.

Best results

Use distinct, literal watch queries that name the brand, product, or competitor you intend to review. Start with timeframe: "week", sort: "new", and a modest maxItems value, then inspect the source URLs before widening the window. Add excludeKeywords only for recurring noise you have already seen. Re-run the same bounded input on your chosen cadence to create your own comparable history; returned rows remain a current collection snapshot rather than a complete conversation archive.

Outcome contract

Every terminal path writes both OUTPUT and RUN_SUMMARY. The machine-readable outcome values are:

COMPLETE
PARTIAL
VALID_EMPTY
INVALID_INPUT
UPSTREAM_FAILED
CONFIG_ERROR

Use RUN_SUMMARY to see the collected rows, source-route detail, and next workflow step for the returned outcome.

Example output

{
  "id": "reddit:abc123",
  "platform": "reddit",
  "title": "Example discussion title",
  "text": "Example discussion title\n\nSource-supplied public post text.",
  "matchedQueries": ["OpenAI"],
  "subreddit": "MachineLearning",
  "author": "public_user",
  "publishedAt": "2026-08-31T12:00:00.000Z",
  "sourceUrl": "https://www.reddit.com/r/MachineLearning/comments/abc123/example/",
  "engagement": { "score": 42, "comments": 8, "upvoteRatio": 0.93 },
  "sentiment": "neutral",
  "sentimentScore": 0,
  "sentimentMethod": "lexicon-v1",
  "intent": "discussion",
  "urgency": "normal",
  "provider": "scrapecreators",
  "sourceRequestUrl": "https://api.scrapecreators.com/v1/reddit/search?query=OpenAI&sort=new&timeframe=week&trim=true",
  "collectedAt": "2026-08-31T12:05:00.000Z"
}

API

Use the Actor endpoint with your own Apify token:

curl -X POST "https://api.apify.com/v2/acts/khadinakbar~sprinklr-alternative/run-sync-get-dataset-items?token=$APIFY_TOKEN" \
  -H 'Content-Type: application/json' \
  -d '{"watchQueries":["OpenAI"],"timeframe":"week","sort":"new","maxItems":25}'

Prompt card

Collect a current public Reddit conversation snapshot for these watch queries. Return up to 50 deduplicated source posts with their public URLs, subreddit, publication time, query labels, collection provenance, and deterministic signal fields. Keep the stated cost cap. Describe results as a bounded public-data sample and route broader social or customer-service workflows to their dedicated systems.

Workflow scenario: from watchlist to review queue

A product lead supplies a brand and one competitor, chooses a one-week window, and requests newest posts. The Actor searches the explicit queries, merges repeated source URLs while retaining every query that matched, and stores source-linked rows. The lead can then filter the dataset by an explicit negative/complaint/high signal for manual review, while retaining the underlying source URL and method label instead of relying on an unexplained dashboard score.

How this workflow compares with Sprinklr

Decision point	This Actor	Sprinklr
Bounded job	Current public Reddit post collection for an explicit watchlist	Broader social listening and unified customer-experience platform
Input	Up to five explicit queries and a bounded timeframe	Suite-level multi-channel configuration and workflows
Output	Source-linked JSON rows with deterministic signals and collection provenance	Broader monitoring, dashboards, workflows, analytics, and service outputs
Billing and same-job cost	Pay per event plus platform usage; see the Pricing tab for current rates.	Sprinklr markets an enterprise platform; an equal-workload allocation has not been recorded here
Effective efficiency	Final-build duration and usable-row evidence will be recorded before release; the workflow keeps its input and output contract small and inspectable	No authorized equal-workload timing observation is recorded here
Integrations	Standard Apify API, dataset readback, schedules, webhooks, and Actor chaining are the portable workflow surfaces; no named client integration is claimed yet	Sprinklr documents CRM, DAM, BI, and enterprise integration workflows
Automation	Repeat the same explicit input through an Apify schedule, webhook, API, or Actor chain after final-build verification	Sprinklr documents broader automated social and customer-service workflows
Better fit	An inspectable public-Reddit collection run feeding another system	Teams needing multi-channel listening, collaboration, publishing, engagement, governance, or care operations

Sprinklr documents a platform that brings social listening, publishing, engagement, commerce, advertising, customer service, and advocacy together across 30+ digital and social channels. Those capabilities remain outside this Actor’s contract. Sprinklr is a trademark of its owner; this independent Actor is not affiliated with, associated with, or endorsed by Sprinklr.

Focused workflow and next step

This Actor has one focused job: produce a current, source-linked Reddit conversation snapshot from a named watchlist. Use the dataset with your own review, alerting, or reporting workflow; schedule only an input whose watch queries and public-data scope you are authorized to monitor.

For a thread-level follow-up after you identify a specific source URL, use the Reddit Posts & Comments Scraper to collect the public post and comments under its own documented contract. That sibling workflow is a better fit for expanding one known thread than repeating a keyword-monitoring run.

Agent and automation handoff

An AI agent can call this run-model Actor when the request is specifically for a current public Reddit listening snapshot. Use the OUTPUT record for the terminal outcome and dataset ID, then retrieve the default dataset to review source rows. Preserve sourceUrl, matchedQueries, provider, sourceRequestUrl, and collectedAt when passing the data onward, so the next workflow retains query and collection provenance. Stop or clarify when the requested job needs another platform, account access, a customer-service response, or a broader team workspace.

Builder's note

I built the workflow around inspectability. A compact listening dataset is most useful when every returned row shows where it came from, why it matched, when it was collected, and how a simple triage label was produced. That is a better foundation for review than an unlabeled score—and it keeps broader multi-channel and operational needs easy to route to a product designed for them.

Responsible use

Collect public sources you are authorized to monitor and respect platform terms, privacy expectations, and applicable law. Keep returned public usernames within an authorized review workflow, and include human review of source text and triage labels before material actions.

Pricing and run costs

This Actor uses Pay per event plus Apify platform usage. The Pricing tab lists the current event rates and billing terms.

Event	Billing unit	When it applies
apify-actor-start	Actor Start	Charged when the Actor starts running. Number of events charged depends on Actor memory (one event per GB, minimum one event).
public-conversation-collected	Public conversation collected	Charged only when one normalized, deduplicated public Reddit conversation is persisted in the dataset.

Run cost combines the charged events and Apify platform usage. Review the run charge limit and requested result count before starting.

Connect an AI agent

Use the Apify MCP configurator to choose an available client connection. Inspect this Actor’s current input schema and required credentials before running it.