Twitter Profile Scraper

T

Twitter Scraper - Apify

Twitter Scraper – Apify

FeaturesTwitter Scraper crawls specified Twitter profiles and URLs, and extracts:
User information, such as name, Twitter handle (username), location, follower/following count, profile URL/image/banner, date of creation
List of tweets, retweets, and replies from profiles
Statistics for each tweet: favorites, replies, and retweets for each tweet
Search hashtags, get top, latest, people, picture, or video tweets
Our free Twitter Scraper enables you to extract large amounts of data from Twitter. It lets you do much more than the Twitter API, because it doesn’t have rate limits and you don’t even need to have a Twitter account, a registered app, or Twitter API key.
You can crawl based on a list of Twitter handles or just by using a Twitter URL such as a search, trending topics, or hashtags.
Use casesScraping Twitter will give you access to the more than 500 million tweets posted every day. You can use that data in lots of different ways:
Track discussions about your brand, products, country, or city.
Monitor your competitors and see how popular they really are, and how you can get a competitive edge.
Keep an eye on new trends, attitudes, and fashions as they emerge.
Use the data to train AI models or for academic research.
Track sentiment to make sure your investments are protected.
Fight fake news by understanding the pattern of how misinformation spreads.
Explore discussions about travel destinations, services, amenities, and take advantage of local knowledge.
Analyze consumer habits and develop new products or target underdeveloped niches.
If you would like more inspiration on how scraping social media can help your business or organization, check out our industry pages.
TutorialYou can read our step-by-step tutorial on how to scrape Twitter if you need some guidance on how to run the scraper. Or you can always email for help.
Input ConfigurationThe Twitter Scraper actor has the following input options
Mode – Scrape only own tweets from the profile page or include replies to other users
List of Handles – Specify a list of Twitter handles (usernames) you want to scrape. If zero, the actor ignores the links and only crawls the Start URLs.
Max. Tweets – Specify the maximum number of tweets you want to scrape.
Proxy Configuration – Select a proxy to be used.
Login Cookies – Your Twitter login cookies (no username/password is submitted). Check the login section.
Supported Twitter URL types
Searches: Trending topics: Profiles: Statuses: Topics: Hashtag: Retweets with quotes: (requires login)
Events:
ResultsThe actor stores its results into the default dataset associated with the actor run. The data can be downloaded in machine-readable formats such as JSON, HTML, CSV or Excel.
Each item in the dataset will contain a separate tweet that follows this format:
{
“user”: {
“id_str”: “44196397”,
“name”: “Elon Musk”,
“screen_name”: “elonmusk”,
“location”: “”,
“description”: “”,
“followers_count”: 42583621,
“fast_followers_count”: 0,
“normal_followers_count”: 42583621,
“friends_count”: 104,
“listed_count”: 59150,
“created_at”: “2009-06-02T20:12:29. 000Z”,
“favourites_count”: 7840,
“verified”: true,
“statuses_count”: 13360,
“media_count”: 801,
“profile_image_url_”: “,
“profile_banner_url”: “,
“has_custom_timelines”: true,
“advertiser_account_type”: “promotable_user”,
“business_profile_state”: “none”,
“translator_type”: “none”},
“id”: “1338857124508684289”,
“conversation_id”: “1338390123373801472”,
“full_text”: “@CyberpunkGame The objective reality is that it is impossible to run an advanced game well on old hardware. This is a much more serious issue:,
“reply_count”: 792,
“retweet_count”: 669,
“favorite_count”: 17739,
“hashtags”: [],
“symbols”: [],
“user_mentions”: [
“screen_name”: “CyberpunkGame”,
“name”: “Cyberpunk 2077”,
“id_str”: “821102114”}],
“urls”: [
“url”: “,
“expanded_url”: “,
“display_url”: “…”}],
“created_at”: “2020-12-15T14:43:07. 000Z”}
LoginBy providing login cookies, you can access more content, such as tweets with sensitive media or related to your own account.
The login cookies look like this:
[
“name”: “auth_token”,
“domain”: “. “,
“value”: “f431d25ba571dfdb6c03b9900f28f6f2c7de3e97”}]
You can get this information using the EditThisCookie extension.
Advanced searchYou can use a predefined search using Advanced Search as a startUrl, e. g. This returns only tweets containing “cool” before 2020-01-01.
Workaround for max tweets limitBy default, the Twitter API will return only at most 3, 200 tweets per profile or search. If you need to get more than the maximum number, you can split your start URLs with time slices, like this:
(from%3Aelonmusk)%20until%3A2020-03-01%20since%3A2020-04-01&src=typed_query&f=live
(from%3Aelonmusk)%20until%3A2020-02-01%20since%3A2020-03-01&src=typed_query&f=live
(from%3Aelonmusk)%20until%3A2020-01-01%20since%3A2020-02-01&src=typed_query&f=live
All URLs are from the same profile (elonmusk), but they are split by month (January -> February -> March 2020). This can be created using Twitter “Advanced Search” on You can use bigger intervals for profiles that don’t post very often.
Other limitations include:
Live tweets are capped by at most 1 day in the past (use the search filters above to get around this)
Most search modes are capped at around 150 tweets (Top, Videos, Pictures)
Extend output functionThis parameter allows you to change the shape of your dataset output, split arrays into separate dataset items, or filter the output:
async ({ item, request}) => {
= undefined; // removes this field from the output
delete; // this works as well
if () {
=; // add the search term to the output
archUrl = request. loadedUrl; // add the raw search URL to the output}
return item;}
Filtering items:
async ({ item}) => {
if (! cludes(‘lovely’)) {
return null; // omit the output if the tweet body doesn’t contain the text}
Splitting into multiple dataset items and change the output completely:
// dataset will be full of items like { hashtag: ‘#somehashtag’}
// returning an array here will split in multiple dataset items
return ((hashtag) => {
return { hashtag: `#${hashtag}`};});}
Extend scraper functionThis parameter allows you to extend how the scraper works and can make it easier to extend the default functionality without having to create your own custom version. For example, you can include a search of the trending topics on each page visit:
async ({ page, request, addSearch, addProfile, addThread, customData}) => {
await page. waitForSelector(‘[aria-label=”Timeline: Trending now”] [data-testid=”trend”]’);
const trending = await page. evaluate(() => {
const trendingEls = $(‘[aria-label=”Timeline: Trending now”] [data-testid=”trend”]’);
return ((_, el) => {
return {
term: $(el)(‘> div > div:nth-child(2)’)()(),
profiles: $(el)(‘> div > div:nth-child(3) [role=”link”]’)((_, el) => $(el)())()}})();});
for (const { search, profiles} of trending) {
await addSearch(search); // add a search using text
for (const profile of profiles) {
await addProfile(profile); // adds a profile using link}}
// adds a thread and get replies. can accept an id, like from conversation_id or an URL
// you can call this multiple times but will be added only once
await addThread(“1351044768030142464”);}
Additional variables are available inside extendScraperFunction:
async ({ label, response, url}) => {
if (label === ‘response’ && response) {
// inside the (‘response’) callback
if (cludes(‘live_pipeline’)) {
// deal with plain text content
const blob = await (await ())();}} else if (label === ‘before’) {
// executes before the (‘response’), can be used for intercept request/response} else if (label === ‘after’) {
// executes after the scraping process has finished, even on crash}}
Personal dataYou should be aware that the data extracted can contain personal data. Personal data is protected by GDPR in the European Union and by other regulations around the world. You should not scrape personal data unless you have a legitimate reason to do so. If you’re unsure whether your reason is legitimate, consult your lawyers. You can also read our blog post on the legality of web scraping.
Custom Twitter scraping solutionIf you want to scrape Twitter, but don’t want to run the scraper yourself, you can request a custom solution.
ChangelogTwitter Scraper is under continual development. You can always find out about the latest updates by reading the changelog. If you find a problem or would like to suggest a new feature, you can open a GitHub issue.
Web Scraping 101: 10 Myths that Everyone Should Know | Octoparse

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
Automate Getting Twitter Data in Python Using Tweepy and API Access

Automate Getting Twitter Data in Python Using Tweepy and API Access

Learning ObjectivesAfter completing this tutorial, you will be able to:Connect to the twitter RESTful API to access twitter data with nerate custom queries that download tweet data into Python using tweet metadata including users in Python using Tweepy. What You NeedYou will need a computer with internet access to complete this this lesson, you will explore analyzing social media data accessed from Twitter using Python. You will use the Twitter RESTful API to access data about both Twitter users and what they are tweeting tting StartedTo get started, you’ll need to do the following things:Set up a Twitter account if you don’t have one your Twitter account, you will need to apply for Developer Access and then create an application that will generate the API credentials that you will use to access Twitter from the tweepy you’ve done these things, you are ready to begin querying Twitter’s API to see what you can learn about tweets! Set up Twitter AppAfter you have applied for Developer Access, you can create an application in Twitter that you can use to access tweets. Make sure you already have a Twitter create your application, you can follow a useful tutorial from rtweet, which includes a section on Create an application that is not specific to R:TUTORIAL: How to setup a Twitter application using your Twitter accountNOTE: you will need to provide a phone number that can receive text messages (e. g. mobile or Google phone number) to Twitter to verify your use of the API. A heat map of the distribution of tweets across the Denver / Boulder region. Source: mAccess Twitter API in PythonOnce you have your Twitter app set-up, you are ready to access tweets in Python. Begin by importing the necessary Python os
import tweepy as tw
import pandas as pd
To access the Twitter API, you will need 4 things from the your Twitter App page. These keys are located in your Twitter app settings in the Keys and Access Tokens nsumer keyconsumer seceret keyaccess token keyaccess token secret keyDo not share these with anyone else because these values are specific to your you will need define your keysconsumer_key= ‘yourkeyhere’
consumer_secret= ‘yourkeyhere’
access_token= ‘yourkeyhere’
access_token_secret= ‘yourkeyhere’
auth = tw. OAuthHandler(consumer_key, consumer_secret)
t_access_token(access_token, access_token_secret)
api = (auth, wait_on_rate_limit=True)
Send a TweetYou can send tweets using your API access. Note that your tweet needs to be 280 characters or less. # Post a tweet from Python
api. update_status(“Look, I’m tweeting from #Python in my #earthanalytics class! @EarthLabCU”)
# Your tweet has been posted!
Search Twitter for TweetsNow you are ready to search Twitter for recent tweets! Start by finding recent tweets that use the #wildfires hashtag. You will use the method to get an object containing tweets containing the hashtag create this query, you will define the:Search term – in this case #wildfiresthe start date of your searchRemember that the Twitter API only allows you to access the past few weeks of tweets, so you cannot dig into the history too far. # Define the search term and the date_since date as variables
search_words = “#wildfires”
date_since = “2018-11-16″
Below you use () to search twitter for tweets containing the search term #wildfires. You can restrict the number of tweets returned by specifying a number in the () method. (5) will return 5 of the most recent tweets. # Collect tweets
tweets = (,
q=search_words,
lang=”en”,
since=date_since)(5)
tweets
< at 0x7fafc296e400>
() returns an object that you can iterate or loop over to access the data collected. Each item in the iterator has various attributes that you can access to get information about each tweet including:the text of the tweetwho sent the tweetthe date the tweet was sentand more. The code below loops through the object and prints the text associated with each tweet. # Collect tweets
# Iterate and print tweets
for tweet in tweets:
print()
2/2 provide forest products to local mills, provide jobs to local communities, and improve the ecological health of… 1/2 Obama’s Forest Service Chief in 2015 –>”Treating these acres through commercial thinning, hazardous fuels remo… RT @EnviroEdgeNews: US-#Volunteers care for abandoned #pets found in #California #wildfires; #Dogs, #cats, [#horses], livestock get care an…
RT @FairWarningNews: The wildfires that ravaged CA have been contained, but the health impacts from the resulting air pollution will be sev…
RT @chiarabtownley: If you know anybody who has been affected by the wildfires, please refer them to @awarenow_io It is one of the companie…
The above approach uses a standard for loop. However, this is an excellent place to use a Python list comprehension. A list comprehension provides an efficient way to collect object elements contained within an iterator as a list. # Collect tweets
# Collect a list of tweets
[ for tweet in tweets]
[‘Expert insight on how #wildfires impact our environment: ‘,
‘Lomakatsi crews join the firefight: \n\n#wildfires #smoke #firefighter\n\n’,
‘RT @rpallanuk: Current @PHE_uk #climate extremes bulletin: #Arctic #wildfires & Greenland melt, #drought in Australia/NSW; #flooding+#droug…’,
“RT @witzshared: And yet the lies continue. Can’t trust a corporation this deaf dumb and blind — PG&E tells court deferred #Maintenance did…”,
‘The #wildfires have consumed an area twice the size of Connecticut, and their smoke is reaching Seattle. Russia isn… ‘]
To Keep or Remove RetweetsA retweet is when someone shares someone else’s tweet. It is similar to sharing in Facebook. Sometimes you may want to remove retweets as they contain duplicate content that might skew your analysis if you are only looking at word frequency. Other times, you may want to keep you ignore all retweets by adding -filter:retweets to your query. The Twitter API documentation has information on other ways to customize your w_search = search_words + ” -filter:retweets”
new_search
‘#wildfires -filter:retweets’
q=new_search,
[‘@HARRISFAULKNER over 10% of a entire state (#Oregon) has been displaced due to #wildfires which is unprecedented, a… ‘,
‘I left a small window open last night and the smoke from the outside #wildfires made our smoke alarm go off at 4 am… ‘,
‘5 of the 10 biggest #wildfires in California history are burning right now. \n\nFossil fuels brought the… ‘,
‘#Wildfires are part of a vicious cycle: their #emissions fuel global heating, leading to ever-worse fires, which re… ‘,
‘This could be helpful if you need to evacuate! \n#wildfires #OregonIsBurning ‘]
Who is Tweeting About Wildfires? You can access a wealth of information associated with each tweet. Below is an example of accessing the users who are sending the tweets related to #wildfires and their locations. Note that user locations are manually entered into Twitter by the user. Thus, you will see a lot of variation in the format of this provides the user’s twitter handle associated with each provides the user’s provided can experiment with other items available within each tweet by typing tweet. and using the tab button to see all of the available attributes = (,
users_locs = [[, ] for tweet in tweets]
users_locs
[[‘J___D___B’, ‘United States’],
[‘KelliAgodon’, ‘S E A T T L E ☮ ️\u200d ‘],
[‘jpmckinnie’, ‘Los Angeles, CA’],
[‘jxnova’, ‘Harlem, USA’],
[‘momtifa’, ‘Portland, Oregon, USA’]]
Create a Pandas Dataframe From A List of Tweet DataOne you have a list of items that you wish to work with, you can create a pandas dataframe that contains that eet_text = Frame(data=users_locs,
columns=[‘user’, “location”])
tweet_text
userlocation0J___D___BUnited States1KelliAgodonS E A T T L E ☮ ️‍ 2jpmckinnieLos Angeles, CA3jxnovaHarlem, USA4momtifaPortland, Oregon, USACustomizing Twitter QueriesAs mentioned above, you can customize your Twitter search queries by following the Twitter API instance, if you search for climate+change, Twitter will return all tweets that contain both of those words (in a row) in each that the code below creates a list that can be queried using Python indexing to return the first five w_search = “climate+change -filter:retweets”
since=’2018-04-23′)(1000)
all_tweets = [ for tweet in tweets]
all_tweets[:5]
[‘They care so much for these bears, but climate change is altering their relationship with them. It’s getting so dan… ‘,
‘Prediction any celebrity/person in government that preaches about climate change probably is blackmailed… ‘,
‘@RichardBurgon Brain washed and trying to do the same to others. Capitalism is ALL that “Climate Change” is about. ‘,
“We’re in a climate crisis, but Canada’s handing out billions to fossil fuel companies. Click to change this:…,
‘Hundreds Of Starved Reindeer Found Dead In Norway, Climate Change Blamed – Forbes #nordic #norway ‘]
In the next lesson, you will explore calculating word frequencies associated with tweets using Python.

Frequently Asked Questions about twitter profile scraper

Is it legal to scrape Twitter?

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.Aug 16, 2021

How do you scrape a Twitter profile?

How to scrape data from Twitter profiles?Create a free Phantombuster account.Specify the Twitter profiles you want to scrape data from.Set the Phantom on repeat.Download this Twitter profiles data to a . CSV spreadsheet or a . JSON file.

How do I scrape Twitter data using Python?

Begin by importing the necessary Python libraries.import os import tweepy as tw import pandas as pd.auth = tw. … # Post a tweet from Python api. … # Define the search term and the date_since date as variables search_words = “#wildfires” date_since = “2018-11-16″# Collect tweets tweets = tw.More items…•Sep 11, 2020

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