Scraping Tweets By Keyword

Scraping Tweets by Keyword

Raw tweet collection for any keyword, hashtag, or boolean search query. No assumed use case — returns the full tweet dataset for downstream analysis.

Prerequisites

• APIFY_TOKEN environment variable set
• Optional: Apify MCP server installed

Inputs

Workflow

Progress:
- [ ] Step 1: Build search query string
- [ ] Step 2: Run tweet-scraper
- [ ] Step 3: Poll for SUCCEEDED
- [ ] Step 4: Fetch and deliver dataset

Step 1: Build Search Query

• Hashtag search → #keyword
• Exact phrase → "exact phrase"
• Boolean → word1 AND word2 -exclude
• From account → from:username
• Mention → @username

Step 2: Run the Actor

Recommended — run_actor.js (handles waiting, output, and file saving automatically):

# Quick answer (prints table to chat)
node scripts/run_actor.js \
  --actor "apidojo~tweet-scraper" \
  --input '{"param": "value"}'

# Save as CSV
node scripts/run_actor.js \
  --actor "apidojo~tweet-scraper" \
  --input '{"param": "value"}' \
  --output YYYY-MM-DD_results.csv --format csv

# Save as JSON
node scripts/run_actor.js \
  --actor "apidojo~tweet-scraper" \
  --input '{"param": "value"}' \
  --output YYYY-MM-DD_results.json --format json

APIFY_TOKEN must be set in environment or .env file.

If Apify MCP is available:

Tool: apify:run-actor
Actor: "apidojo~tweet-scraper"
Input:
{
  "searchTerms": ["<query>"],
  "maxItems": 200,
  "since": "<YYYY-MM-DD>",
  "lang": "<lang_code>"
}

REST API fallback:

curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~tweet-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"searchTerms": ["<query>"], "maxItems": 200}'

Save id as RUN_ID. Poll until status = SUCCEEDED:

curl "https://api.apify.com/v2/actor-runs/$RUN_ID?token=$APIFY_TOKEN" | grep '"status"'

Fetch results:

curl "https://api.apify.com/v2/actor-runs/$RUN_ID/dataset/items?token=$APIFY_TOKEN&format=json"

Step 3: Handle Edge Cases

• 0 results: Query may be too narrow, misspelled, or language-filtered. Broaden term, remove language filter, extend date range.
• < 20 results: Try removing since/until constraints. Some low-volume terms have sparse data.
• Duplicate tweet IDs: Deduplicate by id field before delivering.
• Suspended/deleted accounts: Tweets from suspended accounts return with empty author fields — flag these rows.

Output Format

# Tweet Dataset: "<query>"
Total collected: N | Date range: SINCE – UNTIL | Language: LANG

| Tweet ID | Author | Text (truncated) | Likes | Retweets | Replies | Timestamp |
|----------|--------|-----------------|-------|----------|---------|-----------|
| ...      | ...    | ...             | ...   | ...      | ...     | ...       |

Full dataset: N rows × 15 fields
Available fields: id, text, author_id, author_username, likeCount, retweetCount,
replyCount, quoteCount, lang, createdAt, tweetUrl, media, isRetweet, isQuote, source

Troubleshooting

Empty results for a valid hashtag: Twitter API indexing lag — try again after 15 minutes. Rate limit error: Reduce maxItems to 100 and retry. Timeout on large requests: Set maxItems: 500 max per run; chain multiple runs with date ranges for larger datasets.