How To Scrape Twitter Data

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How to Extract Data from Twitter Without Coding | Octoparse

In this tutorial, I’ll show you how to scrape Twitter data in 5 minutes without using Twitter API, Tweepy, Python, or writing a single line of code.
To extract data from Twitter, you can use an automated web scraping tool – Octoparse. As Octoparse simulates human interaction with a webpage, it allows you to pull all the information you see on any website, such as Twitter. For example, you can easily extract Tweets of a handler, tweets containing certain hashtags, or posted within a specific time frame, etc. All you need to do is to grab the URL of your target webpage and paste it into Octoparse built-in browser. Within a few point-and-clicks, you will be able to create a crawler from scratch by yourself. When the extraction is completed, you can export the data into Excel sheets, CSV, HTML, SQL, or you can stream it into your database in real-time via Octoparse APIs.
Read case study: Scrape Twitter discussions for sentiment analysis
Table of contents
Step 1: Input the URL and build a pagination
Step 2: Build a loop item to extract the data
Step 3: Modify the pagination setting and execute the crawler
Before we get started, you can click here to install Octoparse on your computer. Now, let’s take a look at how to build a Twitter crawler within 3 minutes.
Read: What’s pagination?
Let’s say we are trying to scrape all the tweets of a certain handler. In this case, we are scraping the official Twitter account of Octoparse. As you can see, the website is loaded in the built-in browser. Usually, many websites have a “next page” button that allows Octoparse to click on and go to each page to grab more information. In this case, however, Twitter applies “Infinite scrolling” technique, which means that you need to first scroll down the page to let Twitter load a few more tweets, and then extract the data shown on the screen. So the final extraction process will work like this: Octoparse will scroll down the page a little bit, extract the tweets, scroll down a bit, extract, so on and so forth.
To let the bot scroll down the page repetitively, we can build a pagination loop by clicking on the blank area and click “loop click single element” on the Tips panel. As you can see here, a pagination loop is shown in the workflow area, this means that we’ve built a pagination successfully.
Read: What’s loop item?
Now, let’s extract the tweets. Let’s say we want to get the handler, publish time, text content, number of comments, retweets and likes.
First, let’s build an extraction loop to get the tweets one by one. We can hover the cursor on the corner of the first tweet and click on it. When the whole tweet is highlighted in green, it means that it is selected. Repeat this action on the second tweet. As you can see, Octoparse is an intelligent bot and it has automatically selected all the following tweets for you. Click on “extract text of the selected elements” and you will find an extraction loop is built in the workflow.
But we want to extract different data fields into separate columns instead of just one, so we need to modify the extraction settings to select our target data manually. It is very easy to do this. Make sure you go into the “action setting” of the “extract data” step. Click on the handler, and click “extract the text of the selected element”. Repeat this action to get all the data fields you want. Once you are finished, delete the first giant column which we don’t need and save the crawler. Now, our final step awaits.
We’ve built a pagination loop earlier, but we still need a little modification on the workflow setting. As we want Twitter to load the content fully before the bot extracts it, let’s set up the AJAX time out as 5 seconds, to give Twitter 5 seconds to load after each scroll. Then, let’s set up both the scroll repeats and the wait time as 2 to make sure that Twitter loads the content successfully. Now, for each scroll, Octoparse will scroll down for 2 screens, and each screen will take 2 seconds.
Head back to the loop item setting to edit the loop time to 20. This means that the bot will repeat the scrolling for 20 times. You can now run the crawler on your local device to get the data, or run it on Octoparse Cloud servers to schedule your runs and save your local resource. Notice, the blanks cells in the columns mean that there is no original data on the page, so nothing is extracted.
If you have any questions on scraping Twitter or any other websites, email us at We are so ready to help!
Author: Milly
How to Extract Data from Twitter Without Coding
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How to Scrape Tweets From Twitter | by Martin Beck - Towards ...

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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 ...

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:

Frequently Asked Questions about how to scrape twitter data

How far back can you scrape Twitter data?

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 data using Python?

If you are logged into Twitter on the web:Click More in the main navigation menu to the left of your timeline.Select Settings and privacy.Choose Privacy and safety.Select Personalization and data.Click See your Twitter data.Confirm your password, then select Request archive.

How do I pull data from Twitter?

How to scrape tweets using R for journalistsStep 1: Prep, downloads and installing R. You’ll firstly need to gather your tools. … Step 2: Open R and load your script. … Step 3: Getting your Twitter access. … Step 4: Running and merging data. … Step 5: Your finished sheet and where to go next.Jan 25, 2017

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