apidojo-io/apidojo-skills

building-journalist-outreach-list-from-twitter

Builds targeted journalist and media contact lists from Twitter/X using apidojo's Twitter scrapers on Apify.

Ver código-fonte
Documento original do Skill

Renderizado do repositório de origem, preservando títulos, exemplos, código, tabelas, links e imagens.

Building a Journalist Outreach List from Twitter

Finds journalists and media professionals on Twitter/X who cover a specific beat, industry, or topic. Journalists are the heaviest users of Twitter for professional networking — it's the best platform to build a media contact list.

Prerequisites

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

Inputs

ParameterTypeRequiredDefaultNotes
startUrlsarrayOptional[]Twitter profile or tweet URLs
twitterHandlesarrayOptional[]Twitter usernames (without @)
twitterUserIdsarrayOptional[]Twitter user IDs
getFollowersbooleanOptionalfalseExtract follower lists
getFollowingbooleanOptionalfalseExtract following lists
getRetweetersbooleanOptionalfalseExtract retweeters of a tweet URL
includeUnavailableUsersbooleanOptionalfalseInclude unavailable/suspended users
maxItemsnumberOptionalUnlimitedMaximum users to return
customMapFunctionstringOptionalJavaScript function to transform each output object

Workflow

Progress:
- [ ] Step 1: Define beat, industry, and target publications
- [ ] Step 2: Search for journalists tweeting about the beat
- [ ] Step 3: Enrich profiles to extract publication affiliation
- [ ] Step 4: Filter and verify journalist signals
- [ ] Step 5: Deliver press contact list

Step 1: Clarify Parameters

Ask the user for:

  • Beat/topic (e.g., "artificial intelligence", "climate tech", "fintech", "B2B SaaS")
  • Target publications (optional — e.g., "TechCrunch", "Forbes", "Wired", "any tier-1 tech media")
  • Type — staff writers, freelancers, or both
  • Geography (optional — US, UK, global)
  • List size (default: 30 contacts)

Step 2: Search for Beat-Relevant Tweets

Find journalists actively tweeting about the beat — recency of coverage matters for outreach.

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

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

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

# Save as JSON
node scripts/run_actor.js \
  --actor "apidojo~twitter-user-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": [
    "[TOPIC] journalist",
    "[TOPIC] reporter",
    "writing about [TOPIC]",
    "[TOPIC] story [current year]"
  ],
  "maxItems": 300,
  "tweetLanguage": "en"
}

Also run a second query for article link tweets (journalists tweet their published work):

Input:
{
  "searchTerms": ["[TOPIC] [publication_domain OR article link signal]"],
  "maxItems": 200
}

REST API fallback:

bash
curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~tweet-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "searchTerms": ["[TOPIC] journalist", "[TOPIC] reporter", "writing about [TOPIC]"],
    "maxItems": 300
  }'

Collect unique author usernames from all results.

Step 3: Enrich Profiles

If Apify MCP is available:

Tool: apify:run-actor
Actor: "apidojo~twitter-user-scraper"
Input:
{
  "usernames": ["[username1]", "...", "up to 150 usernames"]
}

REST API fallback:

bash
curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~twitter-user-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"usernames": ["[username1]", "[username2]"]}'

Step 4: Filter for Journalist Signals

Keep accounts where bio contains ANY of:

  • Publication names (e.g., "TechCrunch", "@Forbes", "Bloomberg", "@Wired")
  • Journalist titles: "reporter", "journalist", "editor", "correspondent", "writer at", "staff writer", "freelance writer", "contributing editor"
  • Byline signals: "bylines at", "writes for", "covering"
  • Press credentials: "press", "media"

Follower range: 1,000–500,000 (removes complete unknowns and major TV anchors who don't respond to pitches)

Remove: PR/comms people (bio says "PR", "communications", "agency"), brand accounts, academics without journalist credentials.

Step 5: Format Output

Output Format

# Journalist Outreach List: [BEAT/TOPIC]
Contacts found: [N] | Publication filter: [if any] | Date: [DATE]

## Contact List

| # | Name | @Handle | Publication | Beat | Followers | Recent Coverage |
|---|------|---------|-------------|------|-----------|-----------------|
| 1 | [name] | @[handle] | [pub] | [beat] | [N] | [recent tweet/article topic] |

## By Publication

### [Publication Name] ([N] contacts)
1. **[Name]** (@[handle]) — [title from bio] | [N] followers
   Recent tweet: "[excerpt showing beat relevance]"

2. **[Name]** (@[handle]) — [title] | [N] followers

### Freelancers / Independent ([N] contacts)
1. **[Name]** (@[handle]) — writes for [pubs mentioned in bio] | [N] followers

## Pitch Angle Notes
Based on recent tweets, the top 5 contacts are actively covering:
1. @[handle]: recently tweeted about [specific topic] — pitch angle: [suggestion]
2. @[handle]: covering [topic] — pitch angle: [suggestion]

Personalization Tip

Before pitching, read the journalist's last 3 tweets and their most recent published article. Reference something specific. Generic pitches fail. The recent tweet data in this output enables that personalization without additional research.

Troubleshooting

Too many non-journalists in results: Strengthen bio filter — require "reporter" OR "journalist" OR "editor" as exact words, not just a publication name. Can't find journalists for obscure beats: Widen the search to adjacent beats or try the publication name directly as a search term. Follower counts seem stale: For final outreach list, spot-check the top 10 contacts directly on Twitter.

do mesmo repositório

Mais Skills

Todos os Skills
apidojo-io
Comunidade

building-twitter-prospect-lists

Builds targeted B2B prospect lists from Twitter/X profiles and posts using apidojo's Twitter scrapers on Apify. Triggers when the user asks to: find Twitter users with a specific job title or keyword in bio, build a list of founders or executives on Twitter, find people tweeting about a topic for outreach, identify potential customers on X, scrape Twitter profiles matching an ICP description, find decision-makers in a specific industry on Twitter, or export a list of leads from Twitter bios. Returns name, username, bio, follower count, location, and recent tweet samples per prospect. Ideal for B2B SDRs, growth hackers, founder-led sales teams, and partnership managers.

instalações
1
GitHub Stars
0
Atualizado
13 de mai.
apidojo-io
Comunidade

extracting-google-paa-questions-for-seo

Extracts Google People Also Ask questions for SEO content planning using apidojo's Google Search scraper on Apify. Triggers when the user asks to: find People Also Ask questions on Google for SEO, extract PAA questions for keyword research, discover what questions Google shows for a topic, find long-tail SEO questions from Google, research FAQ content opportunities from Google SERP, build a list of questions to answer in blog content from Google, or extract Google autocomplete and PAA data for content planning. Returns PAA questions, SERP position, related keywords, and content structure recommendations. Ideal for SEO strategists, content writers, and blog editors building search-optimized content.

instalações
1
GitHub Stars
0
Atualizado
13 de mai.
apidojo-io
Comunidade

extracting-tiktok-comments-for-research

Extracts and analyzes TikTok comments from any video or creator using apidojo's TikTok Comments scraper on Apify. Triggers when the user asks to: scrape TikTok comments from a video, analyze what viewers say about a TikTok post, extract comment data for sentiment analysis, find top comments on a viral TikTok video, collect TikTok user feedback from comments, build a dataset of TikTok community reactions, study audience sentiment on TikTok content, or research what a target audience cares about from TikTok comments. Returns commenter username, comment text, likes on comment, reply count, and timestamp. Ideal for market researchers, brand managers, content creators, and academic researchers.

instalações
1
GitHub Stars
0
Atualizado
13 de mai.
apidojo-io
Comunidade

finding-speaking-opportunities-on-twitter

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.

instalações
1
GitHub Stars
0
Atualizado
13 de mai.