Scraping Data From Twitter Python
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:
Complete Tutorial On Twint: Twitter Scraping Without Twitter’s API
Web Scraping allows us to download data from different websites over the internet to our local system. It is data mining from different online portals using Hypertext Transfer Protocols and uses this data according to our requirements. Many companies use this for data harvesting and for creating search engine bots.
Python has a large variety of packages/modules that can help in the process of web scraping like beautiful soup, selenium. Several libraries are there which can automate the process of web scraping like Autoscraper. All these libraries use different APIs through which we can scrape data and store it into a data frame in our local gister for AWS Data & Analytics Conclave>>
Twint is an open-source python library that is used for twitter scraping i. e we can use twint in order to extract data from twitter and that too without using the twitter API. There are certain features of twint which makes it more useable and unique from other twitter scraping API, namely:
Twitter API has a limit of fetching only 3200(last) tweets while twint has no limit of downloading tweets, it can download almost all the to use and very initial Sign-in or Sign-up required for fetching data.
Twint can be used to scrape tweets using different parameters like hashtags, usernames, topics, etc. It can even extract information like phone number and email id’s from the tweets.
In this article, we will explore twint and see what different functionalities it offers for scraping data from twitter.
Implementation:
We will start by installing twint using pip install twint.
Importing required libraries
We will be scraping data from twitter using twint so we will import twint other than this we need to import net_asyncio which will handle all the notebook and runtime errors. Also, we will initiate the net_syncio in this step only.
import twint
import nest_asyncio
()
Configuring Twint
We need to scrape data from twitter using twint before that we need to configure the twint object and call it whenever required.
t = ()
Now let us start scraping different types of data from twitter.
Scraping Data
Followers on Twitter
Here, we will see how we can download the names of the followers of a particular user by using their username. Here I am using my own twitter username.
ername = “Himansh70809561”
(t)
Here you can see a list of my followers on twitter because I used my username, similarly, you can use the different usernames of different users and download the follower’s name.
Storing info to Dataframe
We can also store the information into a data frame. Let us see how to store the follower’s details in a data frame.
= 30
ername = ‘Analyticsindiam’
= True
follow_df =
Here we saw that the top 30 followers are stored in a data frame. We can set the number of followers to the desired number.
Extracting tweets with a particular word
Here we will try and extract all tweets which have a particular word in them which we define.
= “analytics”
ore_object = True
= 10
tlist = arch_tweet_list
print(tlist)
The output contains tweet from different users with their usernames and tweet along with the date when a tweet is published.
Tweets of a particular User
We can also extract tweets from different users by entering their username as the parameter.
= “from:@Analyticsindiam”
Here we can see some recent tweets from Analytics India Magazine along with their username and date on which they were published.
These are some of the ways with which we can extract data or scrape data from twitter using twint. Twint contributors are actively contributing to making it better and better day by day.
Conclusion:
In this article, we saw how we can use twint to extract data from twitter. We started with scraping the followers a person has on twitter further we saw how we can store them in a data frame. We also saw how to extract tweets with a particular string or tweets from a particular user. Twint is easy to easy and is blazingly fast with frequent updates.
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Himanshu SharmaAn aspiring Data Scientist currently Pursuing MBA in Applied Data Science, with an Interest in the financial markets. I have experience in Data Analytics, Data Visualization, Machine Learning, Creating Dashboards and Writing articles related to Data Science.
Web Scraping 101: 10 Myths that Everyone Should Know | Octoparse
1. Web Scraping is illegal
Many people have false impressions about web scraping. It is because there are people don’t respect the great work on the internet and use it by stealing the content. Web scraping isn’t illegal by itself, yet the problem comes when people use it without the site owner’s permission and disregard of the ToS (Terms of Service). According to the report, 2% of online revenues can be lost due to the misuse of content through web scraping. Even though web scraping doesn’t have a clear law and terms to address its application, it’s encompassed with legal regulations. For example:
Violation of the Computer Fraud and Abuse Act (CFAA)
Violation of the Digital Millennium Copyright Act (DMCA)
Trespass to Chattel
Misappropriation
Copy right infringement
Breach of contract
Photo by Amel Majanovic on Unsplash
2. Web scraping and web crawling are the same
Web scraping involves specific data extraction on a targeted webpage, for instance, extract data about sales leads, real estate listing and product pricing. In contrast, web crawling is what search engines do. It scans and indexes the whole website along with its internal links. “Crawler” navigates through the web pages without a specific goal.
3. You can scrape any website
It is often the case that people ask for scraping things like email addresses, Facebook posts, or LinkedIn information. According to an article titled “Is web crawling legal? ” it is important to note the rules before conduct web scraping:
Private data that requires username and passcodes can not be scrapped.
Compliance with the ToS (Terms of Service) which explicitly prohibits the action of web scraping.
Don’t copy data that is copyrighted.
One person can be prosecuted under several laws. For example, one scraped some confidential information and sold it to a third party disregarding the desist letter sent by the site owner. This person can be prosecuted under the law of Trespass to Chattel, Violation of the Digital Millennium Copyright Act (DMCA), Violation of the Computer Fraud and Abuse Act (CFAA) and Misappropriation.
It doesn’t mean that you can’t scrape social media channels like Twitter, Facebook, Instagram, and YouTube. They are friendly to scraping services that follow the provisions of the file. For Facebook, you need to get its written permission before conducting the behavior of automated data collection.
4. You need to know how to code
A web scraping tool (data extraction tool) is very useful regarding non-tech professionals like marketers, statisticians, financial consultant, bitcoin investors, researchers, journalists, etc. Octoparse launched a one of a kind feature – web scraping templates that are preformatted scrapers that cover over 14 categories on over 30 websites including Facebook, Twitter, Amazon, eBay, Instagram and more. All you have to do is to enter the keywords/URLs at the parameter without any complex task configuration. Web scraping with Python is time-consuming. On the other side, a web scraping template is efficient and convenient to capture the data you need.
5. You can use scraped data for anything
It is perfectly legal if you scrape data from websites for public consumption and use it for analysis. However, it is not legal if you scrape confidential information for profit. For example, scraping private contact information without permission, and sell them to a 3rd party for profit is illegal. Besides, repackaging scraped content as your own without citing the source is not ethical as well. You should follow the idea of no spamming, no plagiarism, or any fraudulent use of data is prohibited according to the law.
Check Below Video: 10 Myths About Web Scraping!
6. A web scraper is versatile
Maybe you’ve experienced particular websites that change their layouts or structure once in a while. Don’t get frustrated when you come across such websites that your scraper fails to read for the second time. There are many reasons. It isn’t necessarily triggered by identifying you as a suspicious bot. It also may be caused by different geo-locations or machine access. In these cases, it is normal for a web scraper to fail to parse the website before we set the adjustment.
Read this article: How to Scrape Websites Without Being Blocked in 5 Mins?
7. You can scrape at a fast speed
You may have seen scraper ads saying how speedy their crawlers are. It does sound good as they tell you they can collect data in seconds. However, you are the lawbreaker who will be prosecuted if damages are caused. It is because a scalable data request at a fast speed will overload a web server which might lead to a server crash. In this case, the person is responsible for the damage under the law of “trespass to chattels” law (Dryer and Stockton 2013). If you are not sure whether the website is scrapable or not, please ask the web scraping service provider. Octoparse is a responsible web scraping service provider who places clients’ satisfaction in the first place. It is crucial for Octoparse to help our clients get the problem solved and to be successful.
8. API and Web scraping are the same
API is like a channel to send your data request to a web server and get desired data. API will return the data in JSON format over the HTTP protocol. For example, Facebook API, Twitter API, and Instagram API. However, it doesn’t mean you can get any data you ask for. Web scraping can visualize the process as it allows you to interact with the websites. Octoparse has web scraping templates. It is even more convenient for non-tech professionals to extract data by filling out the parameters with keywords/URLs.
9. The scraped data only works for our business after being cleaned and analyzed
Many data integration platforms can help visualize and analyze the data. In comparison, it looks like data scraping doesn’t have a direct impact on business decision making. Web scraping indeed extracts raw data of the webpage that needs to be processed to gain insights like sentiment analysis. However, some raw data can be extremely valuable in the hands of gold miners.
With Octoparse Google Search web scraping template to search for an organic search result, you can extract information including the titles and meta descriptions about your competitors to determine your SEO strategies; For retail industries, web scraping can be used to monitor product pricing and distributions. For example, Amazon may crawl Flipkart and Walmart under the “Electronic” catalog to assess the performance of electronic items.
10. Web scraping can only be used in business
Web scraping is widely used in various fields besides lead generation, price monitoring, price tracking, market analysis for business. Students can also leverage a Google scholar web scraping template to conduct paper research. Realtors are able to conduct housing research and predict the housing market. You will be able to find Youtube influencers or Twitter evangelists to promote your brand or your own news aggregation that covers the only topics you want by scraping news media and RSS feeds.
Source:
Dryer, A. J., and Stockton, J. 2013. “Internet ‘Data Scraping’: A Primer for Counseling Clients, ” New York Law Journal. Retrieved from
Frequently Asked Questions about scraping data from twitter python
How do I scrape twitter data in Python?
Twint can be used to scrape tweets using different parameters like hashtags, usernames, topics, etc. It can even extract information like phone number and email id’s from the tweets. In this article, we will explore twint and see what different functionalities it offers for scraping data from twitter.Sep 23, 2020
Can I scrape data from Twitter?
It is perfectly legal if you scrape data from websites for public consumption and use it for analysis. … For example, scraping private contact information without permission, and sell them to a 3rd party for profit is illegal.Aug 16, 2021
