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finding-speaking-opportunities-on-twitter

Name: finding-speaking-opportunities-on-twitter
Author: apidojo-io

Finds speaking opportunities and event organizer contacts on Twitter using apidojo's Twitter scrapers. Triggers when the user asks to: find speaking opportunities on Twitter, discover conferences looking for speakers on X, find event organizers calling for speaker submissions, identify call-for-speakers announcements in an industry on Twitter, find podcast or summit hosts looking for guests, discover virtual event opportunities for thought leadership, or build a speaking opportunity pipeline from Twitter. Returns event name, organizer handle, topic focus, deadline signals, event size, and submission URL. Ideal for startup founders, executives, coaches, and consultants building thought leadership through speaking.

What it does

Finding Speaking Opportunities On Twitter

The skill document

Finding Speaking Opportunities On Twitter

Executes finding speaking opportunities on twitter using apidojo scrapers. Part of the apidojo intelligence skills library.

Prerequisites

APIFY_TOKEN environment variable set
Optional: Apify MCP server installed

Inputs

Parameter	Type	Required	Default	Notes
`searchTerms`	array	✅	`[]`	Twitter advanced search queries (e.g. `["#AI lang:en", "from:NASA"]`)
`sort`	string	Optional	`Top`	Sort order: `Latest`, `Top`, or `Latest+Top`
`tweetLanguage`	string	Optional	—	ISO 639-1 language code (e.g. `en`)
`maxItems`	number	Optional	Unlimited	Maximum tweets to return
`onlyVerifiedUsers`	boolean	Optional	`false`	Only tweets from verified users
`onlyTwitterBlue`	boolean	Optional	`false`	Only Twitter Blue subscribers
`onlyImage`	boolean	Optional	`false`	Only tweets with images
`onlyVideo`	boolean	Optional	`false`	Only tweets with videos
`onlyQuote`	boolean	Optional	`false`	Only quote tweets
`author`	string	Optional	—	Filter to a specific author handle
`inReplyTo`	string	Optional	—	Tweets replying to a specific handle
`mentioning`	string	Optional	—	Tweets mentioning a specific handle
`geotaggedNear`	string	Optional	—	Tweets near a location
`withinRadius`	string	Optional	—	Radius around geotaggedNear
`geocode`	string	Optional	—	Lat/lng + radius string
`placeObjectId`	string	Optional	—	Tweets tagged with a place
`minimumRetweets`	number	Optional	—	Minimum retweet count
`minimumFavorites`	number	Optional	—	Minimum like count
`minimumReplies`	number	Optional	—	Minimum reply count
`start`	string	Optional	—	Tweets after this date (YYYY-MM-DD)
`end`	string	Optional	—	Tweets before this date (YYYY-MM-DD)
`includeSearchTerms`	boolean	Optional	`false`	Add the matched search term to each tweet
`customMapFunction`	string	Optional	—	JavaScript function to transform each output object

Workflow

Progress:
- [ ] Step 1: Define parameters
- [ ] Step 2: Run tweet-scraper
- [ ] Step 3: Filter and classify results
- [ ] Step 4: Score by quality and relevance
- [ ] Step 5: Deliver output

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": ["call for speakers [TOPIC]", "looking for speakers [TOPIC]", "CFP [TOPIC]", "speaker applications [TOPIC]"],
  "maxItems": 100
}

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": ["call for speakers [TOPIC]", "looking for speakers [TOPIC]", "CFP [TOPIC]", "speaker applications [TOPIC]"], "maxItems": 100}'

Wait for SUCCEEDED. Fetch dataset:

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

Step 3: Classify Results

classification: KEYNOTE (large event, competitive) | PANEL (group format) | PODCAST_GUEST (audio interview) | VIRTUAL_SUMMIT (online event) | WORKSHOP (hands-on, smaller)

Step 4: Score Each Result

score = opportunity_score = (event_audience_size_signal: large=1, mid=0.6, small=0.3) * 0.40 + (deadline_is_future ? 1 : 0) * 0.35 + (topic_alignment ? 1 : 0.5) * 0.25

Step 5: Edge Cases

CFP deadlines are time-sensitive; flag any opportunity with a deadline in the past as EXPIRED; prioritize by days until deadline

Additional fallbacks:

< 20 results: Broaden search terms; remove secondary filters
No results: Verify the search terms are correct; try alternate phrasings
Data quality issues: Remove entries with missing key fields; note count in output

Output Format

# Finding Speaking Opportunities On Twitter
Results: [N] | Date: [DATE]

| # | [Key Field] | [Metric 1] | [Metric 2] | [Classification] | [Score] |
|---|------------|-----------|-----------|-----------------|---------|
| 1 | [value] | [value] | [value] | [type] | [0.XX] |

## Summary
Top result: [description]
Key finding: [insight]

Troubleshooting

Too few results: Broaden the primary search term; remove restrictive filters. Low quality results: Apply minimum score threshold (≥ 0.50) to filter noise. Actor fails to run: Verify API key; check actor status at apify.com/apidojo.

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