Advanced Scraper Filters: How to Build Ultra-Targeted Lead Lists

Basic follower scraping gives you a list of usernames. Advanced scraping gives you a list of qualified prospects who are actually likely to respond to your outreach.

The difference? Filters.

Raw scraping pulls everyone — bots, inactive accounts, people who can't receive DMs, users in the wrong geographic market. You end up wasting half your daily DM limit on prospects who will never convert.

Advanced filters strip away the noise and leave you with a clean, targeted list that maximizes every message you send.

The Cost of Unfiltered Lists

Let's put this in perspective. Say you have a daily DM limit of 200 messages across your accounts. Without filters:

• ~20% of scraped users have DMs closed — 40 messages wasted
• ~15% are inactive accounts — 30 messages wasted
• ~10% are bots or spam accounts — 20 messages wasted
• ~15% are outside your target market — 30 messages wasted

That's 120 out of 200 messages going to people who will never become leads. You're operating at 40% efficiency.

With proper filtering, you flip that ratio. 180+ out of 200 messages go to real, active, reachable prospects in your target market. Same daily limit, 3-4x better results.

Filter 1: DM Availability

This is the most critical filter. There's zero point in adding someone to a DM campaign if they can't receive DMs.

X users can restrict who sends them DMs:

• Open DMs — anyone can message them
• Followers only — only people they follow can DM them
• Closed — DMs are disabled entirely

Twittrz's scraper checks DM availability for each user and filters out anyone who can't be messaged. This alone can eliminate 15-25% of a raw follower list, saving you hundreds of wasted DMs per campaign.

Filter 2: Gender Targeting

Depending on your offer, gender targeting can dramatically improve conversion rates.

For example, if you're an OFM agency promoting female creators, your target audience is likely male. Sending campaign DMs to other female users would be a waste.

The scraper analyzes profile data to estimate gender and lets you exclude one gender from your list. This isn't perfect — some profiles are ambiguous — but it typically achieves 80-90% accuracy, which is more than enough to significantly improve targeting.

When to use gender filters:

• OFM and creator promotion campaigns
• Products or services with gender-specific appeal
• Dating or relationship-focused offers
• Fashion, beauty, or grooming niches

Filter 3: Geographic Targeting

Not all markets are equal for every offer. Geographic filtering lets you focus on users from regions where your offer converts best.

Common use cases:

• Exclude low-purchasing-power regions — if your offer requires payment, target users from markets where people are more likely to convert
• Language alignment — target users from regions where your DM language is spoken
• Service availability — if your offer is only available in certain countries, exclude everyone else
• Time zone matching — target users who are active during your response hours

The scraper uses profile location data, language settings, and other signals to estimate geographic origin. You can exclude specific regions or focus on specific markets.

Filter 4: Verification Status

Verified accounts (Blue checkmarks) behave differently than unverified ones:

• They're less likely to engage with cold DMs
• They often have higher follower counts and more noise in their inboxes
• They may be public figures or businesses with different needs

For most outreach campaigns, excluding verified accounts improves targeting because:

• Regular users are more likely to read and respond to DMs
• Verified accounts often have teams managing their inbox
• The conversion rate from verified accounts is typically much lower

However, there are exceptions. If you're targeting businesses or influencers specifically, you'd want to include only verified accounts.

Filter 5: Account Activity

Inactive accounts are dead weight in your list. The scraper can check for recent activity:

• Last post date — has the user tweeted recently?
• Engagement signals — are they liking, retweeting, or replying?
• Following activity — are they actively following new accounts?

Focusing on recently active users ensures your DMs land in inboxes that are actually being checked.

Combining Filters for Maximum Precision

The real power comes from combining multiple filters. Here's an example for an OFM agency campaign:

| Filter | Setting | Effect | |---|---|---| | DM Availability | Open DMs only | Ensures every scraped user can receive messages | | Gender | Exclude female | Targets male audience for creator promotion | | Geography | Exclude low-purchasing-power regions | Focuses on markets likely to convert | | Verification | Exclude verified | Targets regular users more likely to engage | | Activity | Active in last 30 days | Ensures recipients are real, active users |

With all five filters applied, your 10,000-user raw scrape might become 3,000 ultra-qualified prospects. And those 3,000 will outperform the unfiltered 10,000 every single time.

Reading Your Scraping Statistics

After a scrape completes, Twittrz provides a detailed breakdown:

• Total scraped — raw number of followers processed
• Included — users who passed all your filters
• No-DM — users filtered out due to closed DMs
• Verified — users filtered out due to verification status
• Gender breakdown — how many male/female/unknown were detected
• Geographic distribution — regional breakdown of scraped users

These statistics help you understand the composition of your source account's followers and refine your scraping strategy over time.

For example, if a source account has 70% no-DM users, it's a poor scraping target. If another source has 80% open-DM users in your target demographic, it's a goldmine.

Building a Scraping Strategy

Step 1: Identify 10+ Source Accounts

Don't rely on a single source. Find multiple accounts whose followers match your target audience.

Step 2: Test Scrape with Filters

Run a small scrape (500-1,000 users) from each source with your filters applied. Compare the yield — what percentage passes through?

Step 3: Rank Your Sources

Sources with higher filter pass-through rates are your best targets. Prioritize these for larger scrapes.

Step 4: Scrape in Batches

Don't burn through your daily scraping limit on a single source. Distribute across your top sources to build a diverse prospect list.

Step 5: Deduplicate

If you scrape from multiple related accounts, the same users may appear on multiple lists. Remove duplicates before feeding into a campaign.

Step 6: Refresh Monthly

Follower lists change. New followers appear, old ones become inactive. Re-scrape your top sources monthly to keep lists fresh.

The ROI of Filtering

Agencies that implement advanced filtering consistently report:

• 2-3x higher response rates compared to unfiltered campaigns
• 50-70% fewer blocks and reports from better-targeted recipients
• Lower cost per lead since fewer messages are wasted
• Longer account lifespan due to fewer spam signals

The time you spend configuring filters saves exponentially more time (and accounts) down the line. It's the highest-ROI step in your entire campaign workflow.