All-in-1 Twitter Scraper and Analysis Tool

Tool 1: Twitter Bio Scraper Tool

Tool 2: Twitter Profile Scraper Tool

Tool 3: Recent Tweets Scraper Tool

Features

Use Cases

Pricing



Tool 1: Twitter Bio Scraper Tool


→ Keyword based search bar on Twitter is an excellent way to find relevant users.


Figure 1: Screenshot of first few search results from Twitter search in people tab.



→ Our Twitter bio scraper tool exports this information into a CSV file for further analysis


Figure 2: Specrom Twitter bio exporter tool


→ Just specify the search keywords and maximum number of rows to fetch and generate a CSV file that is emailed to you directly.


Table 1: Table created out of the exported CSV file for search term “beauty influencer”.


Information contained in the CSV.
Fields directly scraped from Twitter.com:

  • t_author_name: Name of the Twitter account.
  • t_twitter_username: Username of the Twitter account.
  • t_profile_join_date: Date and time of Twitter account creation (in UTC).
  • t_verified: Whether the Twitter account is verified or not.
  • t_bio: Bio of the Twitter account which is mentioned in its Twitter profile.
  • t_profile_location: Location which is mentioned by the Twitter account.
  • t_profile_website: URL of website mentioned by the Twitter user.
  • t_following: Number of Twitter accounts the user is Following.
  • t_followers: Number of Followers of the Twitter account.
  • t_likes: Number of tweets which are liked by the Twitter account.
  • t_no_of_posts: number of tweets posted by the Twitter account.
  • Profile URL: URL of the Twitter account’s profile.
  • Email: email address of the user (if mentioned in author bio).
  • search_term: search term entered in our scraper tool.
  • data_analysis_date: date when our tool extracted the data.

Analytics columns

  • follower_following_ratio: A ratio of number of followers to number of people you are following. Typically, an influencer should have more followers then number of accounts being followed; aka ratio of lower than 1. Successful (micro)influencers have this number higher than 10.
  • likes_to_posts_ratio: A ratio of number of likes by the user over number of posts by the user. A value higher than 5 typically is associated with an active Twitter user who is less likely to be an influencer and typically follows other influencers.
  • account_age_in_days: Age of the account in days.
  • avg_posts_per_day: We get this value by dividing the number of posts by account age in days. Typically, an influencer/active Twitter user posts atleast 3 Twitter posts a week, so a value of 0.4 or higher is a great surrogate indicator of user engagement.
  • avg_likes_per_day: We get this value by dividing the number of posts by account age in days.


  • Tool 2: Twitter Profile Scraper Tool


    → Input a list of Twitter profile URLs and get back a CSV by email containing the same fields as figure 1.


    Figure 3: Twitter Profile Scraper Tool




    Tool 3: Recent Tweets Scraper Tool


    → Input a list of Twitter profile URLs and get back a CSV by email containing recent tweets and analytics data.


    Figure 4: Recent Tweets Scraper Tool


    → Analytics based on recent tweets provide a complementary filtering capability to the overall profile activity info provided by tools 1 and tools 2.


    Table 2: Table created from CSV file from three Twitter profiles.

    Information contained in the CSV.
  • t_twitter_username: Username of the Twitter account.
  • t_url: input URL of the Twitter profile.
  • recent_cashtags: Cashtags are a Twitter feature that allows users to click on stock symbols like hashtags. This is a list of all the cashtags mentioned by the user in the recent tweets.
  • recent_hashtags: A list of all the hashtags mentioned by the user in the recent tweets.
  • number_of_recent_tweets_analyzed: number of tweets analyzed by our tool.
  • recent_tweet_photo_percent: percentage of recent tweets that contained a photo.
  • recent_tweet_url_percent: percentage of recent tweets that contained an url.
  • recent_tweet_video_percent: percentage of recent tweets that contained a video.
  • recent_avg_tweet_likes: average number of likes received by the user on the recent tweets (including retweets).
  • recent_avg_tweet_reply: average number of replies received by the user on the recent tweets (including retweets).
  • recent_avg_tweet_retweet: average number of retweets received by the user on the recent tweets (including retweets).
  • most_recent_tweet_days_ago: most recent tweet by the user in days, including retweets.
  • avg_days_for_all_recent_tweets: average age of all the recent tweets in days including retweets.
  • recent_avg_tweet_likes_ex_retweets: average number of likes received by the user on the recent tweets (excluding retweets).
  • recent_avg_tweet_reply_ex_retweets: average number of replies received by the user on the recent tweets (excluding retweets).
  • most_recent_tweet_days_ago_ex_retweets: most recent tweet by the user in days, excluding retweets.
  • avg_days_for_all_recent_tweets_ex_retweets: average age of all the recent tweets in days excluding retweets.
  • percent_of_recent_retweets_to_all_tweets: the ratio of number of recent retweets by the user to the total number recent tweets by the user.
  • recent_tweet_text: A list of fulltext of all the recent tweets (including retweets).
  • data_analysis_date: analysis date when the tool was run.


  • Features


    ✓ No Twitter login details required. All data obtained from public facing webpages.


    ✓ All major Twitter advanced search queries supported, so search for exact keywords or for users located in a city or area.


    ✓ Everything runs on our cloud servers so there is no question of your IP address being banned by Twitter.


    ✓ Use this tool to find the right Twitter influencers, discover the target audience, or find people to follow.


    ❌ Stop paying for stale information from databases

    Our tool will scrape data from live Twitter site, and not from some stale database that was updated days or months ago.



    Use Cases


    Some of the popular use cases for our all-in-1 Twitter Scraper and Analysis Tool are:

    Find Influencers to power your influencer marketing


    Build connections with influencers in your particular niche. Since all the data is already in a spreadsheet, you can sort by certain words or phrases related to your business in their bio description. You will also be able to filter by location and see the amount of post engagement and number of followers.

    Search for potential Customers


    Searching for potential customers is necessary for building up your customer base or finding the initial beta users. Once you have exported all the search results for a particular keyword in a CSV file, you can further filter Twitter bios column to find consumers who define themselves with a similar niche and may be interested in your product/service, or by location.

    Advanced Twitter searching to find businesses


    Our tool supports advanced Twitter Bio searches that can let you find more relevant results. For example, you can do a search on “WordPress | Blogger | Blogspot” for an in-depth and highly specific searches.

    Find email addresses


    Our tool will extract email addresses from profiles that have mentioned it in their Twitter bio. So you can supercharge your email marketing and cold outreach efforts.

    Great for journalists and content creators


    Are you trying to do research for a story or a content you are creating? our twitter bio search tool is perfect to find twitter users to interview or research for your story.

    Identify and follow relevant users


    Identify relevant users on Twitter and grow your number of followers by using follow/unfollow strategy.


    Why us?


    ✓ We have sold thousands of data lists and premium plans to digital marketing agencies, small businesses and fortune 500 companies in last 7 years in business with great customer reviews and a very high repeat customer rate.


    ✓ Some of our customers


    ✓ Other data companies literally refer to the book we wrote on web scraping!!

    Our cofounder and Principal Data Scientist, Jay M. Patel, has published a bestselling book with Apress (Springer imprint) called “Getting Structured Data from the Internet Running Web Crawlers/Scrapers on a Big Data Production Scale” (2020).

    This book has become a go-to reference book for data scientists, database developers and web scraping programmers to learn about how to manage large scale web scraping tasks to get the freshest data possible.

    We use rigorous in house processes to go beyond industry standards for quality assurance and get you high quality data that is ready to power your business.


    ✓ We offer 100% customer satisfaction guarantee for the first month and if you are not satisfied with us for any reason than we will issue a refund without any terms or conditions.



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