Apify Social Listening

Social Listening

Turn a brand/topic and a few options into a social-listening report: Reddit mentions with sentiment, the subreddits driving the conversation, emerging themes, and verbatim voice-of-customer quotes — plus optional YouTube channel/video engagement analysis.

This skill is Reddit-first because Reddit is where unprompted, candid opinions about products and brands live, and the routed Actor scores sentiment in the same run. YouTube is a secondary, engagement-analysis module (it inspects specific channels/videos you name — it does not keyword-search YouTube for mentions).

Prerequisites

• Apify account (sign up)
• Authentication via one of:
  • apify login (OAuth, if using the Apify CLI)
  • APIFY_TOKEN environment variable
  • Token from Apify Console → Settings → Integrations

Two execution paths, same Actors:

• MCP path (default in Claude sessions). If the Apify MCP server is connected, no setup is needed — auth runs through the user's account. Use the call-actor and get-dataset-items tools.
• CLI path (portable / scheduled / non-Claude). Requires the Apify CLI and a token. Every CLI call in this skill uses three flags: --json, --user-agent apify-awesome-skills/apify-social-listening, and 2>/dev/null.

Workflow

Copy this checklist and track progress:

Task Progress:
- [ ] Step 1: Collect the listening brief (subject, scope, window, depth, modules)
- [ ] Step 2: Route each module to the right Actor + input
- [ ] Step 3: Run the Actor(s), confirm cost if the run is large
- [ ] Step 4: Aggregate sentiment, themes, communities, and quotes
- [ ] Step 5: Deliver the listening report

Step 1: Collect the listening brief

Ask these as one block before any Actor call:

1. Subject — the brand, product, competitor, or topic to listen for (e.g. "Notion AI", "my-saas", "electric bikes"). This becomes the Reddit searchQueries term(s). Accept several.
2. Scope — whole-of-Reddit (keyword search everywhere, default) or specific communities (the user names subreddits, e.g. r/SaaS, r/productivity). Communities give depth; keyword search gives reach. You can do both.
3. Time window — today / week (default) / month / year / all. Maps to the Reddit timeFilter.
4. Depth — how many posts to pull per query/subreddit (maxPostsPerSubreddit, default 100). Larger = more cost and more signal.
5. Voice-of-customer — include comments? (includeComments). Default yes for listening, since the candid opinions live in comment threads. Comments add cost per post.
6. YouTube module (optional) — if the user wants engagement on specific channels/videos (theirs or a competitor's), collect the channel or video URLs. Skip if they only care about mentions.

If the subject is ambiguous (a common word that will pull noise, e.g. "apple"), say so and suggest narrowing via communities or a more specific query.

Step 2: Route each module to the right Actor

Tier = apify (Apify-maintained) or community (third-party). Both Actors are public on the Apify Store.

Note: the YouTube Actor takes channel/video URLs, not search keywords — it analyzes engagement on content you name, it does not find mentions of your brand on YouTube. Set expectations accordingly.

Step 3: Run the Actor(s)

Build the Reddit input from the brief. Keyword listening + voice-of-customer, last week:

apify actors call "renzomacar/reddit-scraper" \
  -i '{"searchQueries": ["Notion AI"], "sortBy": "relevance", "timeFilter": "week", "maxPostsPerSubreddit": 100, "includeComments": true, "maxCommentsPerPost": 50}' \
  --user-agent apify-awesome-skills/apify-social-listening \
  --json 2>/dev/null

Specific-community monitoring instead of (or in addition to) keyword search:

apify actors call "renzomacar/reddit-scraper" \
  -i '{"subreddits": ["SaaS", "productivity"], "sortBy": "top", "timeFilter": "month", "maxPostsPerSubreddit": 75, "includeComments": true}' \
  --user-agent apify-awesome-skills/apify-social-listening \
  --json 2>/dev/null

Optional YouTube engagement module:

apify actors call "renzomacar/youtube-scraper" \
  -i '{"channelUrls": ["https://www.youtube.com/@competitor"], "maxVideosPerChannel": 30}' \
  --user-agent apify-awesome-skills/apify-social-listening \
  --json 2>/dev/null

MCP path equivalent: call call-actor with the same actor id and the same input object, then get-dataset-items for the run's dataset.

Cost guardrails and recovery are in references/gotchas.md. If a run looks large (high depth × many queries × comments), state the rough scale and confirm before launching.

Step 4: Aggregate sentiment, themes, communities, quotes

The Reddit dataset has one item per post and (when includeComments) per comment, distinguished by dataType. Each carries score, upvoteRatio, commentCount, subreddit, body, postTitle, author, createdUtc, url. Build the report from these:

• Sentiment — the Actor scores community reception via score and upvoteRatio. Treat high-score / high-upvoteRatio items as positively received and low/negative as contested. Read the text of the top and bottom items to label sentiment (positive / neutral / negative) rather than trusting the number alone — a high score can sit on a critical post.
• Volume & trend — count mentions over the window; note spikes by createdUtc.
• Communities — group by subreddit; report which communities drive the conversation.
• Themes — cluster recurring topics from postTitle + body (praise, complaints, comparisons, feature requests, questions).
• Voice-of-customer — pull 3–8 verbatim quotes from high-signal posts/comments, each with its subreddit and url so the user can verify.

For the YouTube module, summarize per video: viewCount, likeCount, commentCount, publishDate, and engagement rate (likes+comments / views).

Step 5: Deliver the listening report

Render a compact report:

1. Summary — subject, window, total mentions, overall sentiment split (e.g. 60% positive / 25% neutral / 15% negative).
2. Top communities — subreddits by mention volume.
3. Themes — 3–6 bullets, each with a representative quote + link.
4. Voice-of-customer — the strongest verbatim quotes with subreddit + url.
5. Notable posts — highest-score and most-discussed items, linked.
6. YouTube engagement (if run) — per-channel/video table.
7. Dataset link — the Apify dataset/console URL for the full data.

Always link back to source posts so claims are verifiable. State the data window and that this is public-Reddit data, not a representative survey.

Responsible use

Only public Reddit/YouTube content is collected. Don't use this to target, dox, or harass individuals — aggregate sentiment and themes, not profiles of named private users. Respect each platform's terms.