How to Scrape Tweets From Twitter | by Martin Beck | Towards Data Science

Social media can be a gold mine of data in regards to consumer sentiment. Platforms such as Twitter lend themselves to holding useful information since users may post unfiltered opinions that are able to be retrieved with ease. Combining this with other internal company information can help with providing insight into the general sentiment people may have in regards to companies, products, etc.
This tutorial is meant to be a quick straightforward introduction to scraping tweets from Twitter in Python using Tweepy’s Twitter API or Dmitry Mottl’s GetOldTweets3. To provide direction for this tutorial I decided to focus on scraping through two avenues: scraping a specific user’s tweets and scraping tweets from a general text search.
Due to the interest in a non-coding solution for scraping tweets, my team is creating an application to fulfill that need. Yes, that means you don’t have to code to scrape data! We are currently in Alpha testing for our app Socialscrapr. If you want to participate or be contacted when the next testing phase is open please sign up for our mailing list below!
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Before we get to the actual scraping it is important to understand what both of these libraries offer, so let’s breakdown the differences between the two to help you decide which one to use.
Tweepy is a Python library for accessing the Twitter API. There are several different types and levels of API access that Tweepy offers as shown here, but those are for very specific use cases. Tweepy is able to accomplish various tasks beyond just querying tweets as shown in the following picture. For the sake of relevancy, we will only focus on using this API to scrape tweets.
There are limitations in using Tweepy for scraping tweets. The standard API only allows you to retrieve tweets up to 7 days ago and is limited to scraping 18,000 tweets per a 15 minute window. However, it is possible to increase this limit as shown here. Also, using Tweepy you’re only able to return up to 3,200 of a user’s most recent tweets. Using Tweepy is great for someone who is trying to make use of Twitter’s other functionality, making complex queries, or wants the most extensive information provided for each tweet.
UPDATE: DUE TO CHANGES IN TWITTER’S API GETOLDTWEETS3 IS NO LONGER FUNCTIONING. SNSCRAPE HAS BECOME A SUBSTITUTE AS A FREE LIBRARY YOU CAN USE TO SCRAPE BEYOND TWEEPY’S FREE LIMITATIONS. MY ARTICLE IS AVAILABLE HERE FOR SNSCRAPE.
GetOldTweets3 was created by Dmitry Mottl and is an improvement fork of Jefferson Henrqiue’s GetOldTweets-python. It does not offer any of the other functionality that Tweepy has, but instead only focuses on querying tweets and does not have the same search limitations of Tweepy. This package allows you to retrieve a larger amount of tweets and tweets older than a week. However, it does not provide the extent of information that Tweepy has. The picture below shows all the information that is retrievable from tweets using this package. It is also worth noting that as of now, there is an open issue with accessing the geo data from a tweet using GetOldTweets3.
Using GetOldTweets3 is a great option for someone who’s looking for a quick no-frills way of scraping, or wants to work around the standard Tweepy API search limitations to scrape larger amount of tweets or tweets older than a week.
While they focus on very different things, both options are most likely sufficient for the bulk of what most people normally scrape for. It’s not until one is scraping with specific purposes in mind should one really have to choose between using either option.
Alright, enough with the explanations. This is a scraping tutorial so let’s jump into the coding.
UPDATE: I’ve written a follow-up article that does a deeper dive into how to pull more information from tweets like user information and refining queries for tweets such as searching for tweets by location. If you read this section and decide you need more, my follow-up article is available here.
The Jupyter Notebooks for the following section are available on my GitHub here. I created functions around exporting CSV files from these example queries.
There are two parts to scraping with Tweepy because it requires Twitter developer credentials. If you already have credentials from a previous project then you can ignore this section.
In order to receive credentials, you must apply to become a Twitter developer here. This does require that you have a Twitter account. The application will ask various questions about what sort of work you want to do. Don’t fret, these details don’t have to be extensive, and the process is relatively easy.
After finishing the application, the approval process is relatively quick and shouldn’t take longer than a couple of days. Upon being approved you will need to log in and set up a dev environment in the developer dashboard and view that app’s details to retrieve your developer credentials as shown in the below picture. Unless you specifically have requested access to the other API’s offered, you will now be able to use the standard Tweepy API.
Great, you have your Twitter Developer credentials and can finally get started scraping some tweets.
Setting up Tweepy authorization:
Before getting started you Tweepy will have to authorize that you have the credentials to utilize its API. The following code snippet is how one authorizes themself.
consumer_key = "XXXXXXXXX"consumer_secret = "XXXXXXXXX"access_token = "XXXXXXXXX"access_token_secret = "XXXXXXXXX"auth = tweepy.OAuthHandler(consumer_key, consumer_secret)auth.set_access_token(access_token, access_token_secret)api = tweepy.API(auth,wait_on_rate_limit=True)
Scraping a specific Twitter user’s Tweets:
The search parameters I focused on are id and count. Id is the specific Twitter user’s @ username, and count is the max amount of most recent tweets you want to scrape from the specific user’s timeline. In this example, I use the Twitter CEO's @jack username and chose to scrape 100 of his most recent tweets. Most of the scraping code is relatively quick and straight forward.
username = 'jack'count = 150try:      # Creation of query method using parameters tweets = tweepy.Cursor(api.user_timeline,id=username).items(count)  # Pulling information from tweets iterable object tweets_list = [[tweet.created_at, tweet.id, tweet.text] for tweet in tweets]  # Creation of dataframe from tweets list # Add or remove columns as you remove tweet information tweets_df = pd.DataFrame(tweets_list)except BaseException as e:      print('failed on_status,',str(e))      time.sleep(3)
If you want to further customize your search you can view the rest of the search parameters available in the api.user_timeline method here.
Scraping tweets from a text search query:
The search parameters I focused on are q and count. q is supposed to be the text search query you want to search with, and count is again the max amount of most recent tweets you want to scrape from this specific search query. In this example, I scrape the 100 of the most recent tweets that were relevant to the 2020 US Election.
text_query = '2020 US Election'count = 150try: # Creation of query method using parameters tweets = tweepy.Cursor(api.search,q=text_query).items(count)  # Pulling information from tweets iterable object tweets_list = [[tweet.created_at, tweet.id, tweet.text] for tweet in tweets]  # Creation of dataframe from tweets list # Add or remove columns as you remove tweet information tweets_df = pd.DataFrame(tweets_list) except BaseException as e:    print('failed on_status,',str(e))    time.sleep(3)
If you want to further customize your search you can view the rest of the search parameters available in the api.search method here.
What other information from the tweet is accessible?
One of the advantages of querying with Tweepy is the amount of information contained in the tweet object. If you’re interested in grabbing other information than what I chose in this tutorial you can view the full list of information available in Tweepy’s tweet object here. To show how easy it is to grab more information, in the following example I created a list of tweets with the following information: when it was created, the tweet id, the tweet text, the user the tweet is associated with, and how many favorites the tweet had at the time it was retrieved.
tweets = tweepy.Cursor(api.search, q=text_query).items(count)# Pulling information from tweets iterable tweets_list = [[tweet.created_at, tweet.id, tweet.text, tweet.user, tweet.favorite_count] for tweet in tweets]# Creation of dataframe from tweets listtweets_df = pd.DataFrame(tweets_list)
UPDATE: DUE TO CHANGES IN TWITTER’S API GETOLDTWEETS3 IS NO LONGER FUNCTIONING. SNSCRAPE HAS BECOME A SUBSTITUTE AS A FREE LIBRARY YOU CAN USE TO SCRAPE BEYOND TWEEPY’S FREE LIMITATIONS. MY ARTICLE IS AVAILABLE HERE FOR SNSCRAPE.
Using GetOldTweets3 does not require any authorization like Tweepy does, you just need to pip install the library and can get started right away.
Scraping a specific Twitter user’s Tweets:
The two variables I focused on are username and count. In this example, we scrape tweets from a specific user using the setUsername method and setting the amount of most recent tweets to view using setMaxTweets.
username = 'jack'count = 2000# Creation of query objecttweetCriteria = got.manager.TweetCriteria().setUsername(username)\                                        .setMaxTweets(count)# Creation of list that contains all tweetstweets = got.manager.TweetManager.getTweets(tweetCriteria)# Creating list of chosen tweet datauser_tweets = [[tweet.date, tweet.text] for tweet in tweets]# Creation of dataframe from tweets listtweets_df = pd.DataFrame(user_tweets)
Scraping tweets from a text search query:
The two variables I focused on are text_query and count. In this example, we scrape tweets found from a text query by using the setQuerySearch method.
text_query = 'USA Election 2020'count = 2000# Creation of query objecttweetCriteria = got.manager.TweetCriteria().setQuerySearch(text_query)\                                            .setMaxTweets(count)# Creation of list that contains all tweetstweets = got.manager.TweetManager.getTweets(tweetCriteria)# Creating list of chosen tweet datatext_tweets = [[tweet.date, tweet.text] for tweet in tweets]# Creation of dataframe from tweets listtweets_df = pd.DataFrame(text_tweets)
Queries can be further customized by combining TweetCriteria search parameters. All the current search parameters available are shown below.
Example of a query using several search parameters:
The following stacked query will return 2,000 tweets relevant to USA Election 2020 that were tweeted between January 1st 2019 and October 31st 2019.
text_query = 'USA Election 2020'since_date = '2019-01-01'until_date = '2019-10-31'count = 2000# Creation of query objecttweetCriteria = got.manager.TweetCriteria().setQuerySearch(text_query).setSince(since_date).setUntil(until_date).setMaxTweets(count)# Creation of list that contains all tweetstweets = got.manager.TweetManager.getTweets(tweetCriteria)# Creating list of chosen tweet datatext_tweets = [[tweet.date, tweet.text] for tweet in tweets]# Creation of dataframe from tweets listtweets_df = pd.DataFrame(text_tweets)
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If you’re interested, sign up for our Socialscrapr mailing list: https://upscri.be/3kcmqx
My follow up article that does a deeper dive into both packages: https://towardsdatascience.com/how-to-scrape-more-information-from-tweets-on-twitter-44fd540b8a1f
snscrape article that helps setup and provides a couple of example queries: https://medium.com/better-programming/how-to-scrape-tweets-with-snscrape-90124ed006af
GitHub containing this tutorial’s Twitter scraper’s: https://github.com/MartinBeckUT/TwitterScraper/tree/master/BasicScraper
Increasing Tweepy’s standard API search limit: https://bhaskarvk.github.io/2015/01/how-to-use-twitters-search-rest-api-most-effectively./
Tweepy GitHub: https://github.com/tweepy/tweepy
GetOldTweets3 GitHub: https://github.com/Mottl/GetOldTweets3