A Quick Method To Extract Tweets and Replies For Free Twitter is beginning to take over the social media realm. As more communities move to Twitter, we begin to see how valuable data is to advertisers, researchers, and even consumers. Data is now the next gold rush as we begin to understand how data needs to be extracted, transformed, loaded, and for full benefit, turned into Information. In theory, like gold, data is a commodity. In this article, I plan to explain how easy it is to scrape Tweets from Twitter in Python3 using Tweepy’s Twitter API. This data can be accessed by Twitter’s API and Tweepy which ended up being the most successful method. I plan to focus on scraping specific user Tweet replies, since I have not discovered any tutorials which specifically highlight how to extract Tweet replies. If you want to jump straight into the code then you can find the full code on my Github .The Python code requires your Twitter API and consumer keys, as well as the Twitter username you plan to extract replies, and the Tweet ID. Setting up Twitter Development Account & Tweepy Make sure you have Python installed on your machine. If you do not, I suggest using , otherwise read the official to find additional resources. Anaconda Python documentation To perform Twitter operations from your machine, I suggest using . To install Tweepy, navigate to your environment and run: Tweepy Python3: pip install tweepy If you’re using Anaconda for Python: conda -c conda-forge tweepy install Developers for Twitter If you would like to interact with Twitter from a computer or machine, you will need to . The application is straight forward, be honest with your intentions within the application, and you will become approved if you are deemed trustworthy from Twitter. Once approved, you will be able to create an app on the platform which provides you with credentials to authorize from Tweepy or your Python Twitter library. apply for Twitter Developers Twitter for Developers provides access to the Twitter API in order to Publish and analyze Tweets, optimize ads, and create unique customer experiences. Check out the . Twitter API documentation here Before you are able to use the Twitter API endpoints, create a developer account and generate your API keys. . You must answer questions on how you plan to use the API and accept the Twitter Developer Agreement, and then you will be granted access to the Developer Dashboard. You can apply for a developer account directly here Once you are approved access to the Developers for Twitter, log in to the and create your App. This step will automatically generate your consumer API keys and access tokens, remember, you should keep them secret: developer site The developer account should be linked to the Twitter account where you want to have the bot active. From the Twitter Development platform, you are able to edit the app permissions. In my example, I have granted my app permission to read, write and send direct messages. Twitter Authentication with Python We must import Tweepy then OAuth interface to collect data as well as csv, and ssl. csv tweepy ssl ssl._create_default_https_context = ssl._create_unverified_context consumer_key = consumer_secret = access_token = access_token_secret = auth = tweepy.OAuthHandler(consumer_key, consumer_secret) auth.set_access_token(access_token, access_token_secret) api = tweepy.API(auth) import import import # Oauth keys "YOUR_CONSUMER_KEY" "YOUR_CONSUMER_SECRET" "YOUR_ACCESS_TOKEN" "YOUR_ACCESS_SECRET" # Authentication with Twitter To collect tweet replies for a specific user and tweet, we must list the username of the user being scraped, as well as the Tweet ID which can be found by copying from the URL. name = tweet_id = replies=[] tweet tweepy.Cursor(api.search,q= +name, result_type= , timeout= ).items( ): hasattr(tweet, ): (tweet.in_reply_to_status_id_str==tweet_id): replies.append(tweet) # update these for whatever tweet you want to process replies to 'LunarCRUSH' '1270923526690664448' for in 'to:' 'recent' 999999 1000 if 'in_reply_to_status_id_str' if Since I was hoping to analyze the responses, I decided to export all replies to a .csv file format which can be opened in Microsoft Excel or Google Sheets. 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There are a few things that can be done to improve the code, such as mapping multiple replies, or getting the responses from those that replied to the original post. Please let me know in the comments if you have any questions or suggestions. Share your knowledge, open source your projects , participate in a community (any community!), and maybe just maybe publish a blog post about it. Knowledge is Power! Thank you For Reading Constructive criticism and feedback is welcomed. Nicholas Resendez can be reached on Instagram , on and Twitter for updates on new articles. @nirholas LinkedIn , @nickresendez