Apify X Twitter Data

X/Twitter Data Collection

Collect public X/Twitter tweet and profile-relation datasets with Xquik Apify Actors, then summarize or export the results.

Xquik is an independent third-party service. Not affiliated with X Corp. "Twitter" and "X" are trademarks of X Corp.

CLI rules: Always pass --user-agent apify-awesome-skills/apify-x-twitter-data, --json for Actor metadata and calls, and --format json or --format csv for dataset exports. Append 2>/dev/null so CLI progress messages do not break JSON parsing.

Prerequisites

• Apify CLI v1.5.0+ (npm install -g apify-cli)
• jq for inspecting JSON samples
• Authentication through apify login or an APIFY_TOKEN environment variable

Workflow

Copy this checklist and track progress:

Task Progress:
- [ ] Step 1: Classify the user's X/Twitter data goal
- [ ] Step 2: Pick the right Xquik Actor
- [ ] Step 3: Fetch the Actor schema and build capped input
- [ ] Step 4: Run the Actor and fetch the dataset
- [ ] Step 5: Deliver a concise summary or saved export

Step 1: Classify the Goal

If the request mixes tweet content and follower data, run the tweet Actor first, then run the follower Actor only if the user still needs relationship data.

Step 2: Pick an Actor

Step 3: Fetch Schema and Build Input

Fetch the current schema before composing input:

apify actors info "xquik/x-tweet-scraper" \
  --input --user-agent apify-awesome-skills/apify-x-twitter-data --json 2>/dev/null

apify actors info "xquik/x-follower-scraper" \
  --input --user-agent apify-awesome-skills/apify-x-twitter-data --json 2>/dev/null

Use conservative defaults unless the user asks for more:

Confirm before large runs because they use more Apify credits and can take longer. maxItems caps the whole run, including several search terms or handles. For balanced multi-handle timelines, use explicit profileTweets mode with maxItemsPerTarget, or split handles into separate approved runs. Check each Actor's live Apify pricing box before starting a paid run.

Step 4: Run the Actor

Tweet search example:

apify actors call "xquik/x-tweet-scraper" \
  -i '{"searchTerms":["from:apify since:2026-01-01"],"queryType":"Latest","maxItems":50,"includeSearchTerms":true}' \
  --user-agent apify-awesome-skills/apify-x-twitter-data --json 2>/dev/null

Tweet URL example:

apify actors call "xquik/x-tweet-scraper" \
  -i '{"startUrls":[{"url":"https://x.com/apify/status/1234567890"}],"maxItems":25}' \
  --user-agent apify-awesome-skills/apify-x-twitter-data --json 2>/dev/null

Follower export example:

apify actors call "xquik/x-follower-scraper" \
  -i '{"twitterHandles":["apify"],"relation":"followers","maxItems":200,"outputMode":"compact"}' \
  --user-agent apify-awesome-skills/apify-x-twitter-data --json 2>/dev/null

From the run JSON, capture .id, .status, .defaultDatasetId, and .consoleUrl. If .status is not SUCCEEDED, open .consoleUrl and inspect the run logs before retrying.

Step 5: Fetch and Deliver Results

Fetch a small sample first:

apify datasets get-items DATASET_ID --limit 5 \
  --user-agent apify-awesome-skills/apify-x-twitter-data --format json 2>/dev/null

Save a JSON export:

apify datasets get-items DATASET_ID \
  --user-agent apify-awesome-skills/apify-x-twitter-data --format json 2>/dev/null > x-twitter-data.json

Save a CSV export:

apify datasets get-items DATASET_ID \
  --user-agent apify-awesome-skills/apify-x-twitter-data --format csv 2>/dev/null > x-twitter-data.csv

For tweet datasets, summarize count, query or target, time range, top authors, engagement fields present, and representative URLs. For profile-relation datasets, summarize count, relation type, target handles or URLs, verification split if present, follower-count range if present, and the export filename.

Troubleshooting

• Auth error: run apify login, or set APIFY_TOKEN.
• Actor not found: verify the Actor ID from the routing table.
• Empty results: reduce filters, test one handle or URL, and switch queryType from Top to Latest for search.
• Large or slow run: lower maxItems or split targets into smaller batches.