Twitter is a goldmine of valuable social media data. With over 450 million monthly active users worldwide, the platform offers rich insights into people‘s opinions, behaviors, and connections. One particularly useful dataset is a Twitter account‘s followers – the users who have opted to receive that account‘s tweets in their timeline.
Follower data can shed light on an account‘s audience demographics, a brand‘s customers, a competitor‘s strategies, an influencer‘s reach, and much more. Scraping Twitter followers refers to the process of extracting this follower information from Twitter at scale and saving it in a structured format for further analysis.
In this in-depth guide, you‘ll learn everything you need to know to start scraping Twitter follower data yourself. I‘ll cover why you might want to do it, the different methods and tools available, step-by-step instructions, data analysis tips, limitations to be aware of, and more.
By the end, you‘ll be fully equipped to scrape Twitter followers to support your business intelligence, audience research, lead generation, and various other applications. Let‘s dive in!
Why Scrape Twitter Followers?
So what exactly can you do with Twitter follower data? Here are some of the most common use cases:
Audience research: Gain a deeper understanding of the types of users following an account, including their demographics, interests, locations, and other attributes. This intel is invaluable for developing marketing personas and targeted campaigns.
Competitive analysis: Uncover insights about your competitors‘ audiences to inform your own strategies. Analyze follower overlap to identify shared audiences to target. Track competitor follower growth over time to benchmark your own performance.
Influencer marketing: Evaluate an influencer‘s audience to determine their fit and reach for your brand campaigns. Engage with an influencer‘s followers to amplify your brand‘s message and drive conversions.
Lead generation: Identify potential customers or clients amongst a relevant account‘s followers. Compile lists of users to target in your sales and outreach efforts.
Trend spotting: Track the follower growth of emerging accounts in your space to stay on top of developing trends and topics. Use these insights to fuel your own thought leadership and content strategies.
Is It Legal to Scrape Twitter Followers?
Before you start scraping, it‘s important to understand the legal implications. In general, scraping publicly available data is legal in most jurisdictions. Twitter‘s terms of service allow users to share and reproduce public Twitter content subject to certain limitations.
However, you should be mindful not to abuse the Twitter platform by aggressively scraping huge volumes of data in an automated fashion. This could get your IP address blocked. You also need to comply with relevant data regulations like the GDPR when it comes to using and storing personal data.
The safest course is to scrape only public Twitter data, not utilize it for commercial purposes, and respect the privacy of individual users. Scrape follower data in moderation and analyze/apply it carefully in line with privacy best practices. Consult legal counsel if you‘re unsure about the specifics of your use case.
Methods for Scraping Twitter Followers
There are two main approaches to scraping Twitter followers:
- Using the Twitter API
- Using a web scraping tool
Twitter API Method
The official Twitter API allows developers to access Twitter data programmatically. To use the API for follower scraping, you‘ll need to:
- Create a Twitter Developer account and obtain API credentials
- Use the API to make requests to the follower endpoints and paginate through the results
- Process and store the JSON data returned by the API
Some key endpoints to utilize are:
GET followers/ids– retrieves the IDs of accounts following a specified userGET followers/list– retrieves a list of accounts following a specified userGET users/lookup– retrieves fully-hydrated user objects for up to 100 users per request
The Twitter API is a robust choice that offers comprehensive, official data. However, it does have some limitations in terms of rate limits and access levels depending on your plan. It also requires writing code to interact with the API, so you‘ll need development expertise.
Web Scraping Method
Web scraping tools allow you to extract data directly from the Twitter website. They work by loading the Twitter follower pages for an account and parsing the HTML to pull out the relevant data attributes.
The key advantages of web scraping are:
- No API access or Twitter Dev account needed
- Can overcome some of the limitations of the API
- Easier to use for non-developers with GUI tools
Some popular web scraping tools for Twitter include:
- Octoparse
- ParseHub
- Apify
- Pytheas
- Twint
Many offer both a visual interface for designing scraping workflows as well as an API/code libraries for programmatic access. With these tools, you typically just need to supply the Twitter profile URL, configure the data fields to extract, and initiate the scraping job.
How to Scrape Twitter Followers with Octoparse
Let‘s walk through an example of scraping Twitter followers using Octoparse, one of the most powerful and user-friendly web scraping tools. Octoparse offers both a desktop app and cloud-based service for no-code data extraction.
Step 1: Install Octoparse and create a new task
First, download and install the Octoparse app on your computer. Launch the app and click "Create a new task". Choose "Advanced Mode" and enter the URL of the Twitter profile whose followers you want to scrape, e.g. https://twitter.com/elonmusk/followers.
Step 2: Configure the scraping workflow
Octoparse uses a visual point-and-click workflow designer. The follower page will load in the central browser pane. To extract follower data, locate a follower entry in the HTML tree, hover over the element, and click the "Select" button. Expand the field options to choose the attributes to extract, such as name, username, bio, location, etc.
To get continual follower data, you‘ll also need to set up pagination handling. Octoparse supports this with a "Loop click next page" function under the "Workflow" settings accessible from the left side toolbar.
Step 3: Run the scraping job
Once you‘ve set up the workflow, you can test it on a small batch of pages. If the data looks correct, save the task and queue up a larger scraping job. You can run it locally or in the Octoparse cloud, as well as schedule recurring jobs to keep your follower data fresh.
Step 4: Export the data
Finally, export your scraped Twitter follower data in your preferred structured format like CSV or JSON. You‘re now ready to perform analysis to derive actionable insights!
How to Scrape Twitter Followers with Twint
For a more programmatic approach, let‘s look at scraping Twitter followers in Python using the powerful Twint library. Twint is an open-source tool that allows you to scrape Twitter data without needing API authentication.
Step 1: Install Twint
First, make sure you have Python installed on your machine. Then, open a terminal and use pip to install the latest version of Twint:
pip install twint
Step 2: Scrape follower data
Twint offers a command line interface for scraping Twitter data. The basic syntax is:
twint -u username
However, to scrape follower data specifically, you can use the following flags:
twint -u username --followers -o followers.csv --csv
This will scrape the followers of the specified username and save the data to a CSV file named followers.csv.
You can also add parameters to filter the follower data scraped, such as:
--limit: Max number of followers to scrape--since: Only scrape followers followed since a certain date--verified: Only scrape verified followers--output-format: Specify output file format (csv, json, sqlite, etc.)
Step 3: Analyze the follower data
With your follower data saved to a structured file, you can proceed to analyze it using a tool like Excel, Google Sheets, Python Pandas, or a BI/data viz platform. Some ideas for analysis include:
- Plotting follower growth over time
- Visualizing the geographic distribution of followers
- Examining the most common words in follower bios/names
- Calculating average follower/following ratios
- Identifying the most influential followers based on their own metrics
Challenges of Scraping Twitter Followers
While scraping Twitter followers can yield valuable insights, there are some challenges to be aware of:
Data quality: The quality and completeness of follower data can vary depending on the scraping method used. API access will generally provide more complete data, but scraped data can have gaps or inconsistencies.
Rate limiting: To prevent abuse, Twitter limits the speed and volume of requests you can make to its platform. This applies to both API and web scraping. You may need to throttle your requests or use proxy rotation to avoid hitting limits.
Data drift: Twitter‘s web page structure can change over time, which may break web scraping workflows. It‘s important to monitor and update scrapers accordingly.
Account blocking: In egregious cases of aggressive or abusive scraping, Twitter may block or suspend your account. Always scrape ethically and in moderation.
Data privacy: Twitter follower data can contain personally identifiable information. It‘s critical to handle this data responsibly and in compliance with relevant laws like the GDPR.
Conclusion
Twitter follower data is a rich source of audience and competitive insights that you can harness through web scraping. Whether you use the Twitter API, a no-code tool, or a library like Twint, you have multiple options for extracting this valuable data.
The keys to success are identifying the right use cases, choosing a scraping method that matches your technical capabilities, and responsibly leveraging the follower data in accordance with ethical and legal standards.
By following the guidance in this article, you‘re well on your way to unlocking the power of Twitter follower scraping. The insights you surface can propel your business and research to new heights.
Happy scraping!