Guide to Email Scraping Methods from Social Media and Maps
Unlock the potential of email scraping with Socleads' platform. Our guide summarizes the four main scraping methods across six major data sources, helping you choose the best techniques tailored to your needs.
Table of Contents
Introduction
Socleads offers a versatile platform for data scraping across various social media and online platforms. Four primary scraping methods: keywords, hashtags, followers, and location—allow you to tailor your data collection to meet specific needs. Socleads supports six primary data sources: Instagram, Facebook, Google Maps, LinkedIn, Twitter, YouTube, and TikTok. Each source has its unique scraping processes and capabilities. This guide provides an in-depth look at how each scraping method works within these platforms, presented in a tabular format for clarity.
Instagram Scraper Methods
Method 1: Instagram Hashtag Scraping (Fast Mode)
Advice: use general hashtags to get more results (fashion, music, makeup, modeling, UGC, pilates, instagirl, etc.)
Download the result sample for the “fashion” hashtag without the data enrichment and email validation feature.
Download the result sample for the “fashion” hashtag with the data enrichment and email validation feature.
| Aspect | Details |
|---|
| Overview | Ideal for popular hashtags with over 1 million posts, this method is cost-effective for quickly collecting up to 5,000 emails. |
| Pros | - Fast execution
- Low cost per email
|
| Cons | - Limited to a maximum number of results
|
| Input Data | - Hashtag
- Country (optional)
- Number of results (emails)
|
| Output Data | - Email
- Full name
- Profile/post link
|
| With Enrich Data Feature | - Full name
- Phone (if available)
- Bio
- Bio link
- Category
- Number of followers and following
- Username and ID
- Account and verification type
|
| Billing | 1 credit per email |
| Additional Options | - Avoid duplicates from previous searches
- Enrich data (additional credits)
- Include synonyms in the search
- Email validation
|
Method 2: Instagram Hashtag Scraping (Slow Mode)
Advice: use for unlimited hashtag scraping
Download the result sample for the “socialmediaagency” hashtag with email validation.
Typically, around 40% of profiles have emails available for scraping.
| Aspect | Details |
|---|
| Overview | This method works well for any hashtag and allows you to collect many emails by visiting each profile that has used the hashtag. |
| Pros | - Unlimited emails
- Comprehensive data
|
| Cons | - Credits are consumed for each profile scraped, regardless of email availability
|
| Input Data | - Hashtag
- Number of results (scraped posts)
|
| Output Data | - Full name
- Email (if available)
- Email status
- Phone (if available)
- Bio
- Bio link
- Category
- Number of followers and following
- Username and ID
- Account and verification type
- Postcode
- Post url
- Time
- Caption
- Like and comment count
|
| Billing | 1 credit per scraped post |
| Additional Options | - Avoid duplicates from previous searches
- Email validation
- Avoid username duplicates (exclude profiles with multiple posts in the results)
|
Method 3: Instagram Followers/Following Scraping
Advice: use for unlimited scraping leads from competitors
Download the result sample for the Alex Hormozi Instagram profile’s followers with email validation
Typically, around 20% of profiles have emails for scraping. However, if an account’s followers include a high number of bots, this percentage may decrease. Please note that only public accounts can be scraped, and it’s impossible to scrape 100% of the followers. Nevertheless, you can scrape up to 70% of the followers under optimal conditions.
| Aspect | Details |
|---|
| Overview | Scrape the followers of a specific Instagram profile to gather extensive data. |
| Pros | - Unlimited emails
- Detailed data
- Scrape verified and non-verified profile’s followers
|
| Cons | - Credits are used for each profile, even if no email is found
- Scrapes only public followers
|
| Input Data | |
| Output Data | - Email (if available)
- Phone (if available)
- Full name
- Profile and post links
- Number of post likes and comments
- Biography
- Category
- Number of followers and followings
|
| Billing | 1 credit per scraped post |
| Additional Options | - Avoid duplicates from previous searches
- Email validation
|
Method 4: Instagram Keywords Scraping
Advice: use general keywords to get more results (fashion, music, makeup, modeling, UGC, pilates, instagirl, etc.)
Download the result sample for the “fashion” keyword without the data enrichment and email validation feature.
Download the result sample for the “fashion” keyword with the data enrichment and email validation feature.
| Aspect | Details |
|---|
| Overview | Extract emails from Instagram profiles or posts that contain a specific keyword. Broad keywords are recommended for better results. |
| Pros | |
| Cons | - Limited to about 5,000 emails
|
| Input Data | |
| Output Data | - Email
- Full name
- Profile/post link
|
| With Enrich Data Feature | - Full name
- Phone (if available)
- Bio
- Bio link
- Category
- Number of followers and following
- Username and ID
- Account and verification type
|
| Billing | 1 credit per email |
| Additional Options | - Avoid duplicates from previous searches
- Email validation
- Data Enrichment
|
Each new search scrapes data from the same starting point, which causes it to include duplicates from previous ones (for the same search parameters). To avoid this, do not repeat the exact search multiple times or use the “Avoid duplicates from previous searches” feature.
Facebook Scraper
Advice: use general keywords to get more results (fashion, music, makeup, modeling, UGC, pilates, etc.)
Download the result sample for the “fashion” keyword with email validation.
| Aspect | Details |
|---|
| Overview | Scrape emails from Facebook by searching for a specific keyword in profiles, groups, and posts. Broad keywords give better results. |
| Pros | |
| Cons | - Results are typically limited to around 5,000 emails
- Scraping of specific groups is not supported
|
| Input Data | |
| Output Data | - Email
- Full name
- Profile/post link
|
| Billing | 1 credit per email |
| Additional Options | - Avoid duplicates from previous searches
- Email validation
- Include synonyms in the search
|
Each new search scrapes data from the same starting point, which causes it to include duplicates from previous ones (for the same search parameters). To avoid this, do not repeat the exact search multiple times or use the “Avoid duplicates from previous searches” feature.
Google Maps Scraper
Advice: use general keywords to get more results (restaurant, gym, school, etc.)
Download the result sample for the “gym” keyword in London, UK, with email scraping and validation.
| Aspect | Details |
|---|
| Overview | Collect emails from Google Maps listings based on keywords and locations if a business listing includes a website, the scraper visits the site to extract the email. |
| Pros | - Ideal for finding emails of local businesses
|
| Cons | - Limited results per location
- Not all listings have websites or emails
|
| Input Data | |
| Output Data | - Email (if available on website)
- Phone (if available on Maps)
- Website
- Name
- Address
- Rating
- Category
|
| Billing | 1 credit per place |
| Additional Options | - Avoid duplicates from previous searches
- Email validation
- Email scraping
|
Each new search scrapes data from the same starting point, which causes it to include duplicates from previous ones (for the same search parameters). To avoid this, do not repeat the exact search multiple times or use the “Avoid duplicates from previous searches” feature.
Advice: Use general keywords (fashion, music, makeup, modeling, UGC, pilates, etc.) to get more results.
Download the result sample for the “fashion” keyword
| Aspect | Details |
|---|
| Overview | Extract emails from Twitter by searching for a keyword in profiles or tweets. Broad keywords are recommended. |
| Pros | |
| Cons | - Limited to approximately 5,000 emails
|
| Input Data | |
| Output Data | - Email
- Full name
- Profile/post link
|
| Billing | 1 credit per email |
| Additional Options | - Avoid duplicates from previous searches
- Email validation
- Include synonyms in the search
|
Each new search scrapes data from the same starting point, which causes it to include duplicates from previous ones (for the same search parameters). To avoid this, do not repeat the exact search multiple times or use the “Avoid duplicates from previous searches” feature.
LinkedIn Scraper
Advice: Use general keywords (fashion, music, makeup, modeling, UGC, pilates, etc.) to get more results.
Download the result sample for the “fashion” keyword
| Aspect | Details |
|---|
| Overview | Gather emails from LinkedIn by searching for a keyword in profiles or posts. Using broad terms increases the likelihood of finding more emails. |
| Pros | |
| Cons | - Typically gives up to 5,000 emails
|
| Input Data | |
| Output Data | - Email
- Full name
- Profile/post link
|
| Billing | 1 credit per email |
| Additional Options | - Avoid duplicates from previous searches
- Email validation
- Include synonyms in the search
|
Each new search scrapes data from the same starting point, which causes it to include duplicates from previous ones (for the same search parameters). To avoid this, do not repeat the exact search multiple times or use the “Avoid duplicates from previous searches” feature.
YouTube Scraper
Advice: Use general keywords (fashion, music, makeup, modeling, UGC, pilates, etc.) to get more results.
Download the result sample for the “fashion” keyword
| Aspect | Details |
|---|
| Overview | Scrape emails from YouTube by searching for a keyword in channel profiles or video descriptions. Broad keywords are advisable. |
| Pros | |
| Cons | - Limited to around 5,000 emails
|
| Input Data | |
| Output Data | - Email
- Full name
- Profile/post link
|
| Billing | 1 credit per email |
| Additional Options | - Avoid duplicates from previous searches
- Email validation
- Include synonyms in the search
|
Each new search scrapes data from the same starting point, which causes it to include duplicates from previous ones (for the same search parameters). To avoid this, do not repeat the exact search multiple times or use the “Avoid duplicates from previous searches” feature.
TikTok Scraper
Advice: Use general keywords (fashion, music, makeup, modeling, UGC, pilates, etc.) to get more results.
Download the result sample for the “fashion” keyword
| Aspect | Details |
|---|
| Overview | Extract emails from TikTok by searching for a keyword in profiles or video captions. Broad keywords are recommended for optimal results. |
| Pros | |
| Cons | - Results are typically capped at 5,000 emails
|
| Input Data | |
| Output Data | - Email
- Full name
- Profile/post link
|
| Billing | 1 credit per email |
| Additional Options | - Avoid duplicates from previous searches
- Email validation
- Include synonyms in the search
|
Each new search scrapes data from the same starting point, which causes it to include duplicates from previous ones (for the same search parameters). To avoid this, do not repeat the exact search multiple times or use the “Avoid duplicates from previous searches” feature.
Multiple Channels Scraper
This method allows simultaneous scraping across multiple channels using keywords, including all the above keyword scraping methods: Instagram, Facebook, LinkedIn, Twitter, YouTube, and TikTok.
All scraping methods are unified under a single framework. The scraping process sequentially goes through each social network, streamlining data collection across these platforms.
Advice: Use general keywords (fashion, music, makeup, modeling, UGC, pilates, etc.) to get more results.
Download the sample of results for the “fashion” keyword.
| Aspect | Details |
|---|
| Overview | Scrape emails from multiple sources. Broad keywords are advisable. |
| Pros | |
| Cons | - Limited to around 20,000 emails
|
| Input Data | |
| Output Data | - Email
- Full name
- Profile/post link
|
| Billing | 1 credit per email |
| Additional Options | - Avoid duplicates from previous searches
- Email validation
- Include synonyms in the search
|
Each new search scrapes data from the same starting point, which causes it to include duplicates from previous ones (for the same search parameters). To avoid this, do not repeat the exact search multiple times or use the “Avoid duplicates from previous searches” feature.
Additional Features
- Avoid duplicates from previous searches
Each new search scrapes data from the same starting point, which causes it to include duplicates from previous ones (for the same search parameters). To avoid this, do not repeat the exact search multiple times or use the “Avoid duplicates from previous searches” feature.
Email validation ensures that an email address is accurate and can receive messages.
Statuses:
– Verified: The email is valid.
– Unverifiable: Unable to verify the email.
– Invalid: The email is not valid.
- Include synonyms in the search
Synonym matching using AI is applied to increase the number of results.
Conclusion
Socleads provides a robust email scraping tool suite tailored to different platforms and needs. Whether targeting specific hashtags on Instagram, searching for local businesses on Google Maps, or mining data from social media profiles, Socleads offers flexible options to maximize your data collection efforts efficiently and cost-effectively. Use the tables above to select the scraping method that best aligns with your objectives.