Scrape Twitter
How to Scrape Tweets From Twitter | by Martin Beck – Towards …
A Basic Twitter Scraping TutorialA quick introduction to scraping tweets from Twitter using PythonSocial 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, 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 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! TweepyBefore 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 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 of various functionality offered through Tweepy’s standard 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 tOldTweets3UPDATE: 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 tOldTweets3 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 of information that is retrievable in GetOldTweet3’s tweet 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 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 right, enough with the explanations. This is a scraping tutorial so let’s jump into the from PexelsUPDATE: 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 Jupyter Notebooks for the following section are available on my GitHub here. I created functions around exporting CSV files from these example 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 ining Credentials for TweepyIn 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 itter developer landing 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 developer raping Using TweepyGreat, you have your Twitter Developer credentials and can finally get started scraping some tting 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 nsumer_key = “XXXXXXXXX”consumer_secret = “XXXXXXXXX”access_token = “XXXXXXXXX”access_token_secret = “XXXXXXXXX”auth = tweepy. OAuthHandler(consumer_key, consumer_secret)t_access_token(access_token, access_token_secret)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 ername = ‘jack’count = 150try: # Creation of query method using parameters tweets = (er_timeline, id=username)(count) # Pulling information from tweets iterable object tweets_list = [[eated_at,, ] for tweet in tweets] # Creation of dataframe from tweets list # Add or remove columns as you remove tweet information tweets_df = Frame(tweets_list)except BaseException as e: print(‘failed on_status, ‘, str(e)) (3)If you want to further customize your search you can view the rest of the search parameters available in the er_timeline method raping 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 = (, q=text_query)(count) # Pulling information from tweets iterable object tweets_list = [[eated_at,, ] for tweet in tweets] # Creation of dataframe from tweets list # Add or remove columns as you remove tweet information tweets_df = Frame(tweets_list) except BaseException as e: print(‘failed on_status, ‘, str(e)) (3)If you want to further customize your search you can view the rest of the search parameters available in the method 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 = (, q=text_query)(count)# Pulling information from tweets iterable tweets_list = [[eated_at,,,, tweet. favorite_count] for tweet in tweets]# Creation of dataframe from tweets listtweets_df = Frame(tweets_list)UPDATE: DUE TO CHANGES IN TWITTER’S API GETOLDTWEETS3 IS NO LONGER FUNCTIONING. MY ARTICLE IS AVAILABLE HERE FOR GetOldTweets3 does not require any authorization like Tweepy does, you just need to pip install the library and can get started right raping 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 ername = ‘jack’count = 2000# Creation of query objecttweetCriteria = eetCriteria(). setUsername(username)\. setMaxTweets(count)# Creation of list that contains all tweetstweets = tTweets(tweetCriteria)# Creating list of chosen tweet datauser_tweets = [[, ] for tweet in tweets]# Creation of dataframe from tweets listtweets_df = Frame(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 = eetCriteria(). setQuerySearch(text_query)\. setMaxTweets(count)# Creation of list that contains all tweetstweets = tTweets(tweetCriteria)# Creating list of chosen tweet datatext_tweets = [[, ] for tweet in tweets]# Creation of dataframe from tweets listtweets_df = Frame(text_tweets)Queries can be further customized by combining TweetCriteria search parameters. All the current search parameters available are shown rrent TweetCriteria search parameters. 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 = eetCriteria(). setQuerySearch(text_query). setSince(since_date). setUntil(until_date). setMaxTweets(count)# Creation of list that contains all tweetstweets = tTweets(tweetCriteria)# Creating list of chosen tweet datatext_tweets = [[, ] for tweet in tweets]# Creation of dataframe from tweets listtweets_df = Frame(text_tweets)If you want to reach out don’t be afraid to connect with me on LinkedInIf you’re interested, sign up for our Socialscrapr mailing list: follow up article that does a deeper dive into both packages: article that helps setup and provides a couple of example queries: containing this tutorial’s Twitter scraper’s: Tweepy’s standard API search limit: GitHub: GitHub:
How to Scrape Tweets From Twitter | by Martin Beck – Towards …
A Basic Twitter Scraping TutorialA quick introduction to scraping tweets from Twitter using PythonSocial 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, 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 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! TweepyBefore 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 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 of various functionality offered through Tweepy’s standard 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 tOldTweets3UPDATE: 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 tOldTweets3 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 of information that is retrievable in GetOldTweet3’s tweet 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 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 right, enough with the explanations. This is a scraping tutorial so let’s jump into the from PexelsUPDATE: 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 Jupyter Notebooks for the following section are available on my GitHub here. I created functions around exporting CSV files from these example 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 ining Credentials for TweepyIn 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 itter developer landing 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 developer raping Using TweepyGreat, you have your Twitter Developer credentials and can finally get started scraping some tting 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 nsumer_key = “XXXXXXXXX”consumer_secret = “XXXXXXXXX”access_token = “XXXXXXXXX”access_token_secret = “XXXXXXXXX”auth = tweepy. OAuthHandler(consumer_key, consumer_secret)t_access_token(access_token, access_token_secret)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 ername = ‘jack’count = 150try: # Creation of query method using parameters tweets = (er_timeline, id=username)(count) # Pulling information from tweets iterable object tweets_list = [[eated_at,, ] for tweet in tweets] # Creation of dataframe from tweets list # Add or remove columns as you remove tweet information tweets_df = Frame(tweets_list)except BaseException as e: print(‘failed on_status, ‘, str(e)) (3)If you want to further customize your search you can view the rest of the search parameters available in the er_timeline method raping 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 = (, q=text_query)(count) # Pulling information from tweets iterable object tweets_list = [[eated_at,, ] for tweet in tweets] # Creation of dataframe from tweets list # Add or remove columns as you remove tweet information tweets_df = Frame(tweets_list) except BaseException as e: print(‘failed on_status, ‘, str(e)) (3)If you want to further customize your search you can view the rest of the search parameters available in the method 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 = (, q=text_query)(count)# Pulling information from tweets iterable tweets_list = [[eated_at,,,, tweet. favorite_count] for tweet in tweets]# Creation of dataframe from tweets listtweets_df = Frame(tweets_list)UPDATE: DUE TO CHANGES IN TWITTER’S API GETOLDTWEETS3 IS NO LONGER FUNCTIONING. MY ARTICLE IS AVAILABLE HERE FOR GetOldTweets3 does not require any authorization like Tweepy does, you just need to pip install the library and can get started right raping 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 ername = ‘jack’count = 2000# Creation of query objecttweetCriteria = eetCriteria(). setUsername(username)\. setMaxTweets(count)# Creation of list that contains all tweetstweets = tTweets(tweetCriteria)# Creating list of chosen tweet datauser_tweets = [[, ] for tweet in tweets]# Creation of dataframe from tweets listtweets_df = Frame(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 = eetCriteria(). setQuerySearch(text_query)\. setMaxTweets(count)# Creation of list that contains all tweetstweets = tTweets(tweetCriteria)# Creating list of chosen tweet datatext_tweets = [[, ] for tweet in tweets]# Creation of dataframe from tweets listtweets_df = Frame(text_tweets)Queries can be further customized by combining TweetCriteria search parameters. All the current search parameters available are shown rrent TweetCriteria search parameters. 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 = eetCriteria(). setQuerySearch(text_query). setSince(since_date). setUntil(until_date). setMaxTweets(count)# Creation of list that contains all tweetstweets = tTweets(tweetCriteria)# Creating list of chosen tweet datatext_tweets = [[, ] for tweet in tweets]# Creation of dataframe from tweets listtweets_df = Frame(text_tweets)If you want to reach out don’t be afraid to connect with me on LinkedInIf you’re interested, sign up for our Socialscrapr mailing list: follow up article that does a deeper dive into both packages: article that helps setup and provides a couple of example queries: containing this tutorial’s Twitter scraper’s: Tweepy’s standard API search limit: GitHub: GitHub:
Scrape tweets without using the API – Simon Lindgren
Grabbing tweets, live, from Twitter’s Streaming API is a very useful and powerful way to collect rich social data. But for some Twitter research needs, it is better to use the regular Twitter Search function to get the needed tweets.
There is a very useful Python repository for this, built using Scrapy, by jonbakerfish on Github. It is called TweetScraper and while it will not get as rich data as that obtained through the API, its benefits are that it can access historical tweets, and also bypass the API’s rate limits and restrictions. The creator(s) of the repo underline the importance of using the scraper with caution and to follow the crawler’s politeness policy. Set up the scraperIf you don’t already have them, make sure to install the required repositories:$ pip3 install scrapy
$ pip3 install pymongo
Then, open Terminal in a folder of your choosing. Clone the repo from Github. It will be unpacked as a subfolder called ‘TweetScraper’ within your chosen folder:$ git clone to the newly created subfolder:$ cd TweetScraperEnter ‘scrapy list’ at the prompt. If the response is ‘TweetScraper’ the scraper is properly set the scraperIf you just want tweets, run this command at the bash prompt replacing the query with your desired string. Available search operators are described on the repo page. Two examples:$ scrapy crawl TweetScraper -a query=”foo, #bar”
$ scrapy crawl TweetScraper -a query=”foo OR #bar since:2017-01-01 until:2017-01-02″If you want it to also crawl for user data, run this:$ scrapy crawl TweetScraper -a query=”foo, #bar” -a crawl_user=True
Parsing the scrape resultsTweetScraper will save the downloaded tweets as separate text files in a subfolder called ‘Data/tweet’, and downloaded user data in ‘Data/user’. The data inside these files are in JSON format. A downloaded tweet will look like this:{“usernameTweet”: “_SEV8”, “ID”: “922638779508232192”, “text”: “Jaguar claws? 2 inches. Giant anteater claws? 5 inches. Don’t f*ck with giant anteaters. “, “url”: “/_SEV8/status/922638779508232192”, “nbr_retweet”: 1, “nbr_favorite”: 2, “nbr_reply”: 1, “datetime”: “2017-10-24 03:40:01”, “is_reply”: false, “is_retweet”: false, “user_id”: “2911331278”}And user data will look like this:{“ID”: “876771799”, “name”: “A N T E A T E R”, “screen_name”: “AnteaterComms”, “avatar”: “}It is useful here to convert all files in the folder (for tweets or/users) into a Pandas dataframe. I wrote the below Jupyter Notebook, which will do exactly that. Download the notebook from Github, and point it to the folder that you want to process.
Frequently Asked Questions about scrape twitter
Does Twitter allow scraping?
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.
How do I scrape Twitter without API?
Scrape tweets without using the APISet up the scraper. If you don’t already have them, make sure to install the required repositories: $ pip3 install scrapy $ pip3 install pymongo. … Run the scraper. … Parsing the scrape results.Jul 11, 2017
How do I use Twitter to scrape with Python?
How to start scraping Twitter?Create an account. Want to start scraping Twitter right now? … Configure your scraping. Once your account has been created, go to Documentation, to the “Data Scraper API” section to be able to start scraping what you want. … Let’s scraping! Your web scraping setup is now ready to use!Aug 10, 2021
