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      17 Great About Us Pages to Inspire You


      Every business needs a website. And every website needs an About Us page.

      Actually . . . take two steps back.

      Let’s revise that.

      Every website needs a unique and exciting About Us page that compels visitors to buy your product or service.

      Stick with us, and we’ll look at what an About Us page is and why you need one. More importantly, we’ll discuss how to create compelling About Us pages that build trust, increase conversions, and boost retention rates.

      After that? We’ll dip into 17 examples of unique and exciting About Us pages and delve into what it is about them that makes them worth a special mention.

      We’ll Support Your Dream

      Whatever your online goals, we’ll be right there with you, making sure your site is fast, secure, and always up. Plans start at $2.59/mo.

      What Is an ‘About Us’ Page?

      In short, it’s a page that serves to inspire people — either to work with you or to buy your product. It can contain (but isn’t limited to containing) your brand story, your achievements, and your best testimonials.

      What Is an ‘About’ Page Not?

      An About page is not a page for pushing a hard sell or a page for boasting about your business. It should offer an up-front and honest portrayal of your company, its story, and your brand values.

      So when creating an About Us page, you should make sure to:

      • Stay away from the hype. Users can see straight through it. Leave it for social media.
      • Avoid a sales pitch. If a reader is on your About Us page, there’s a good chance they’re considering using your service or buying your product. They’re looking at why they should choose you. So don’t sell your product or service. Sell you.

      Why?

      It’s simple: People work with people, and people buy from people.

      Tips For Making Great ‘About Us’ Pages

      You should now have a decent idea of what an About Us page should and shouldn’t contain.

      We’re going to follow this with a few tips to help you stand out and create an About Us page that works for you and your business.

      • Be creative. Don’t fall into the trap of simply writing a brief summary of your business and calling it a day. The best About Us pages are creative, informative, and interesting.
      • Don’t follow the crowd. If someone’s reading your About page, there’s a good chance they’ve been reading (or will read) your competitors’ About Us pages. So, make sure your page stands out. It should make it almost impossible for a potential customer to forget you.
      • Feature faces. Consumers like to know who they’re buying from or working with, so make sure to feature at least some of your team on your About page. It can really help boost conversions.

      Oh, and never use stock photography. Ever.

      • Be transparent. Your About Us page serves to sell your story and get buy-in from your visitors. Transparency is incredibly important to win your visitors’ trust.
      • Don’t forget about CTAs. Like any other page, About Us pages need calls to action. Many sites seem to forget that this is a key page for converting visitors. Make it clear to readers what you want them to do next.

      How To Make An ‘About Us’ Page That Converts

      1. Keep your copy simple.

      Don’t litter the page with industry jargon and confusing copy. The words should leap off the page and inspire your visitors to take action. A block of text that visitors have to read six times to grasp is not going to cut the mustard.

      2. Make sure your contact details are on the page.

      This might seem obvious. However, we dug through countless About Us pages while researching this article, and you’d be surprised at how many we came across that didn’t contain contact details or even a contact form.

      If a visitor’s got as far as looking at your About Us page, there’s a good chance they’re thinking of working with you or using your service. Don’t miss that opportunity to convert them by making them search for a separate contact page.

      3. Put yourself in the readers’ shoes.

      What do you think they are looking for? What do they need to know? Many About Us pages don’t seem to have considered these things. At all.

      Does your page highlight your skills? Your knowledge? Your experience? Does it explain to readers the benefits of using your service or products? Does it reference your USPs?

      4. Don’t be afraid to use visuals.

      Consumers today are used to things being delivered fast. Whether it’s a product they’ve ordered or, in this case, information.

      Are you able to sell your service or business in visuals and words? The human brain processes images much faster than words, so if you can, use both.

      5. Include customer testimonials.

      Trust in your brand is essential. Testimonials from genuine customers are a massive selling point and can help convert prospective clients into actual, revenue-generating customers.

      Fancy going one step further?

      Alongside customer testimonials, include quotes or endorsements from influencers or industry experts (if you can get them, of course).

      6. Tell a story.

      Tell your company’s history, but in a way that compels visitors to keep reading. Who doesn’t love a good story? Stories get visitors more invested in your brand. And that, naturally, leads to more conversions.

      Bonus points if you can craft a more personal story.

      7. Make sure the page loads fast.

      This, of course, goes for every page on your site, but it’s crucial that key conversion pages load as fast as possible. Make sure to talk to your web developer and emphasize the importance of page load speed.

      You can find out exactly how long a particular page of your site takes to load and what can be done to make it load faster with Google’s PageSpeed Insights.

      8. Don’t forget the fold.

      Ideally, all important information should be positioned above the fold. You should also guide users to scroll down and read more.

      9. And mobile usability.

      While it varies from industry to industry, more than half of internet browsing now takes place on mobile devices. So make sure your About Us page, and your site as a whole, is built for mobile first.

      You can check if a page is mobile-friendly using Google’s Mobile-Friendly test.

      17 ‘About Us’ Pages That Get It Right — And Why

      Are you in need of some inspiration to help you build your ideal About Us page? Look no further. We’ve scoured the Internet to find some of the best About Us pages out there.

      No matter your niche or what kind of business you run, you’ll be able to find some inspiration in these 17 examples.

      Let’s take a look at each one and discover what makes these pages so unique and exciting (and worthy of inclusion on this list).

      1. HERoines Inc.

      The HERoines Inc. About Us page

      What makes this a good About Us page?

      • The page itself is simple and aesthetically pleasing, and it loads fast.
      • It features photos of the team that seamlessly fit into the page design.
      • The colors and tones used match the rest of the site, creating consistency across all pages.
      • It covers the brand’s visions and goals using inspirational, engaging copywriting.
      • The CTA button sits to the right of the page and remains visible at all times.

      2. Iconiq Creative

      The Iconiq Creative About Us page.

      What makes this a good About Us page?

      • It’s free of unnecessary words and gets straight to the point.
      • It features case studies, a client list, and their credentials — all in plain sight, for all to see.
      • They feature multiple testimonials (although this could be improved by slowing down the carousel or giving users the ability to scroll at their own speed).
      • They’ve linked to their founder’s website, so visitors can learn even more about the brand and its history.
      • It showcases their humanitarian work in a “Giving & Causes” section.

      3. RubyLove

      The RubyLove About Us page

      What makes this a good About Us page?

      • The copy is upfront, accessible, and fun — despite the brand selling products that (sadly) still have some stigma attached to them.
      • It sets out their mission from the first sentence.
      • It’s not always a wise idea to sell on your About Us page; however, this site succeeds by soft-selling using videos, great imagery, and natural internal linking.
      • It features a section that boldly states the benefits of their products and how they can help you. In other words, the copy is user-centric.

      4. Band

      Band’s About Us page.

      What makes this a good About Us page?

      • As a creative studio, this About Us page demonstrates that knowing your user is crucial. The page kicks off with some fantastic imagery that reinforces their business mission.
      • The copy is minimal but covers what’s needed: who they are, why they exist, and why you should work with them.

      5. Anton & Irene 

      Anton & Irene About Us page.

      What makes this a good About Us page?

      • This is one of the best one-page websites we found. Anton and Irene have effectively turned their entire site into an About Us page. It’s edgy and daring and, well, pretty unforgettable.
      • The photography for the team (just the two of them) is incredibly creative. When you hover over their figures, snippets of what’s beneath the veneers are revealed. This one definitely isn’t following the crowd. Exactly what a design brand needs.
      • It tells you who they are and what they do using one sentence and a few bullet points. In other words, it’s minimalist and to the point, while letting the user know exactly how Anton and Irene can help them.
      • It features where they’ve appeared and what they’ve achieved, and it breaks it down into fun sections using hard facts (with a side of cheeky humor).
      • Their contact information is some of the most detailed on this list, but it doesn’t feel tedious or over-the-top. It has that human touch. Largely because they’ve injected their personality into the entire page.
      • They top it off by featuring testimonials and highlighting the awards they’ve won — and they manage this without sounding like they’re giving you a sales pitch.

      6. LessFilms

      The LessFilms About Us page.

      What makes this a good About Us page?

      • They understand their target audience. As a video production company, they (understandably) use video to tell their brand story, and they do it in less than a minute.
      • Everything else is kept to a minimum — they inject a little humor using bullet points that articulate who they are in less than 20 words. To top it off, they have a team member section that makes you feel like you know them personally. The page makes it almost impossible not to want to work with them.

      7. Mailchimp

       The Mailchimp About Us page.

      What makes this a good About Us page?

      • Despite being a big company, Mailchimp successfully manages to circumvent any corporate tropes. Instead, their About Us page makes you feel like you’re going to be working with a small team.
      • Each section is only a couple of paragraphs long. Despite having a ton to talk about, Mailchimp understands that the reader only needs top-level ideas (though you can go off and learn more, thanks to dedicated pages for the company’s culture and history).
      • At no point does the page feel like a sales pitch. It simply pulls you into what Mailchimp stands for and who they are as a company.

      8. 500px

      The 500px About Us page.

      What makes this a good About Us page?

      • As an online network for photographers, you would expect great visuals on their About page, and it doesn’t disappoint — especially if you happen to be a dog person!
      • The page goes on to explain who they are and their commitment to their network. Plus, it’s completely free of fluff.
      • It sets out the benefits of joining the network, and it does it clearly and concisely.

      9. GIPHY

      The GIPHY About Us page.

      What makes this a good About Us page?

      • If you’ve ever used a Graphics Interchange Format file (aka a GIF), then you’re probably aware of GIPHY. GIFs are fun. So GIPHY should be too. And they certainly haven’t let us down. They’ve seemingly taken their ethos, worked from the ground up, and created an About Us page that’s quirky, engaging, and completely on point when it comes to reflecting the brand and its identity.
      • And it’s pretty much done entirely using — you guessed it — GIFs.

      10. Twitter

      Twitter’s About Us page.

      What makes this a good About Us page?

      • Twitter’s About page hits the nail on the head when it comes to copy with brevity. It lays out who they are and what they stand for without going into too much detail. You know precisely what you’re going to get from the social network just by looking at their About Us page.
      • It does an excellent job of moving visitors down the funnel and getting them to sign up and start using the site. They do this in part by pulling trending content into the page. This gives readers a CTA to try the platform.

      11. Moz

      The Moz About Us page.

      What makes this a good About Us page?

      • Moz is known for its transparency, and this shines through in their About Us page. It’s honest, to the point, and tells you their entire story — but without dragging on or going into unnecessary detail.
      • The page also acknowledges their faults and when and why that had to pivot their products to survive in their niche. This is pretty unique and impressive. Not all companies are so honest.

      12. Cupcakes and Cashmere

      The Cupcakes and Cashmere About Us page.

      What makes this a good About Us page?

      • While Emily’s adopted a more traditional approach, this About Us page clearly conveys who Cupcakes and Cashmere are and what they do.
      • They do this using a set of strong visuals that show there are real people behind the brand with a legitimate mission.
      • The site also includes a comprehensive FAQ section where pretty much every question a reader could have is answered — right down to how their affiliate links work.

      13. Eight Hour Day

      The Eight Hour Day About Us page.

      What makes this a good About Us page?

      • This is another creative studio that successfully reflects the quality of their work with a simple About Us page that doesn’t beat around the bush.
      • They tell you who they are, what they do and why they do it, and who they’ve worked with.
      • And they do it all on one page, using clear, concise, and engaging copy.

      14. National Geographic 

      The National Geographic About Us page.

      What makes this a good About Us page?

      • Creating a more corporate About Us page that doesn’t feel stuffy and stagnant isn’t easy, but National Geographic manages to pull it off. The page begins with an inspiring video that’s right on-brand.
      • The page then discusses their mission, alongside information about their leadership team. It also features a job board.
      • Finally, they point you towards some of their most recent content — essentially a fairly subtle CTA.

      15. Cultivated Wit

      The Cultivated Wit About Us page.

      What makes this a good About Us page?

      • As a comedy company, you’d expect their About Us page to be on the playful side. Cultivated Wit hasn’t let their visitors down. They’ve delivered engaging copy that articulates their mission statement in just a few hundred words.
      • Cultivated Wit also uses well-chosen team imagery that makes it crystal clear who they are and how they work.

      16. Lonely Planet

      The Lonely Planet About Us page.

      What makes this a good About Us page?

      • If you’re reading the About Us page on Lonely Planet, you probably already know what they do — and Lonely Planet gets this. So, instead of preaching to the choir, the focus is on how their site can help the user.
      • This page demonstrates the importance of design when it comes to an About Us page using stunning visuals and incredible design. Every part oozes class.

      17. GummiSig

      The GummiSig About Us page.

      What makes this a good About Us page?

      • Gummisig is a freelance web designer, so we’d hope they’d know how to put a good About page together. Thankfully, this one doesn’t disappoint. From the start, you know you’re on a quality page when you see the text, “Who is this man? Dude, myth or mega designer.”
      • Gummisig also shows how important it is to inject your personality into your About page. The copy reflects this perfectly, so readers can get a real feel for who they’ll be working with.

      Ready to Create a Stellar About Us Page?

      Whether you need help writing a mission statement, creating a marketing strategy, or boosting your conversion rate, we can help! Subscribe to our monthly digest so you never miss an article.

      So, What’s Your Value Proposition?

      As you might have noticed, there’s no one set way to design an effective About Us page. Pages can be casual or corporate. Silly or serious. Image-led or copy-led. Or both.

      Throughout all these pages, the running theme is that they engage, educate, and entice readers to become customers. They’re also an accurate reflection of the brands, and leave users feeling that little bit closer to the company — as well as the people behind it.

      Create an About Us page that ticks these boxes, and you may well find you’re converting more customers (and better quality customers) with minimal extra effort.

      Good luck, and more importantly, have fun!





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      How To Create and Link To Additional Website Pages With HTML



      Part of the Series:
      How To Build a Website With HTML

      This tutorial series will guide you through creating and further customizing this website using HTML, the standard markup language used to display documents in a web browser. No prior coding experience is necessary but we recommend you start at the beginning of the series if you wish to recreate the demonstration website.

      At the end of this series, you should have a website ready to deploy to the cloud and a basic familiarity with HTML. Knowing how to write HTML will provide a strong foundation for learning additional front-end web development skills, such as CSS and JavaScript.

      When building a website, you may want to have more than one webpage. If you want to add and link to additional pages, you’ll need to first create a new html file in your website project directory. In this tutorial, we’ll learn how to create and link to an additional webpage on your website

      Our demonstration website includes an “About” webpage. In this tutorial, we’ll walk you through the process of creating and linking to an “About” webpage, but you may change the title and the content of this page to fit your needs.

      To add a new page to your website, create a new file named about.html and save it in your project directory html-practice. (If you have not been following the tutorial series, you can review instructions for setting up a new html file in our tutorial Setting Up Your HTML Project.)

      Note: If you decide to choose your own name for the file, make sure to avoid character spaces, special characters (such as !, #, %, or others), and capital letters as these can cause problems later on. You must also include the .html extension.

      Next, you’ll need to format the file by adding information that will help the browser interpret the file content. To format the file, add following code snippet at the top of the document:

      <!DOCTYPE html>
      <html>
      <head>
      <meta charset="utf-8">
      <title> About </title>
      </head>
      </html> 
      

      Make sure to change the highlighted text with a title you want for you page. For an explanation of each of the HTML tags, please visit the earlier tutorial in this series Adding an HTML <head> Element To Your Webpage. Save the file before you continue.

      Before adding any content to this page, let’s walk through the steps of adding a link to this page on your homepage.

      First, return to your index.html file and add the following snippet below the subtitle of your site and above the closing </div> tag:

      ...
      <p style="font-size: 20px; color:#1F9AFE;">
      <a href="https://www.digitalocean.com/community/tutorials/Webpage FilePath";> About this site </a>
      </p>
      ...
      

      Change the highlighted file path to the relative file path of your “about.html” file. The relative path refers to the file location relative to the current working directory (as opposed to the absolute path, which refers to the file location relative to the root directory.) If you are using the Visual Studio Code text editor, you can copy the relative file path by CTRL + Left Click (on Macs) or right-clicking' (on Windows) on the file icon and selectingCopy Relative Path.`

      Note that we have also specified a font-size and color using the style attribute. Save your index.html file and reload it in the browser.

      You should now have a link that directs to your about.html web page like this:

      Webpage with link

      If you receive an error, make sure that your file is in the same project directory as your index.html file and that there are no errors in your project path.

      You should now know how to create and link to a new webpage on your website. You can use these same steps to create and link to additional webpages on your website. You can also add content to any new webpage in the same way you are learning to add content to your homepage.



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      How To Scrape Web Pages and Post Content to Twitter with Python 3


      The author selected The Computer History Museum to receive a donation as part of the Write for DOnations program.

      Introduction

      Twitter bots are a powerful way of managing your social media as well as extracting information from the microblogging network. By leveraging Twitter’s versatile APIs, a bot can do a lot of things: tweet, retweet, “favorite-tweet”, follow people with certain interests, reply automatically, and so on. Even though people can, and do, abuse their bot’s power, leading to a negative experience for other users, research shows that people view Twitter bots as a credible source of information. For example, a bot can keep your followers engaged with content even when you’re not online. Some bots even provide critical and helpful information, like @EarthquakesSF. The applications for bots are limitless. As of 2019, it is estimated that bots account for about 24% of all tweets on Twitter.

      In this tutorial, you’ll build a Twitter bot using this Twitter API library for Python. You’ll use API keys from your Twitter account to authorize your bot and build a to capable of scraping content from two websites. Furthermore, you’ll program your bot to alternately tweet content from these two websites and at set time intervals. Note that you’ll use Python 3 in this tutorial.

      Prerequisites

      You will need the following to complete this tutorial:

      Note: You’ll be setting up a developer account with Twitter, which involves an application review by Twitter before your can access the API keys you require for this bot. Step 1 walks through the specific details for completing the application.

      Step 1 — Setting Up Your Developer Account and Accessing Your Twitter API Keys

      Before you begin coding your bot, you’ll need the API keys for Twitter to recognize the requests of your bot. In this step, you’ll set up your Twitter Developer Account and access your API keys for your Twitter bot.

      To get your API keys, head over to developer.twitter.com and register your bot application with Twitter by clicking on Apply in the top right section of the page.

      Now click on Apply for a developer account.

      Next, click on Continue to associate your Twitter username with your bot application that you’ll be building in this tutorial.

      Twitter Username Association with Bot

      On the next page, for the purposes of this tutorial, you’ll choose the I am requesting access for my own personal use option since you’ll be building a bot for your own personal education use.

      Twitter API Personal Use

      After choosing your Account Name and Country, move on to the next section. For What use case(s) are you interested in?, pick the Publish and curate Tweets and Student project / Learning to code options. These categories are the best representation of why you’re completing this tutorial.

      Twitter Bot Purpose

      Then provide a description of the bot you’re trying to build. Twitter requires this to protect against bot abuse; in 2018 they introduced such vetting. For this tutorial, you’ll be scraping tech-focused content from The New Stack and The Coursera Blog.

      When deciding what to enter into the description box, model your answer on the following lines for the purposes of this tutorial:

      I’m following a tutorial to build a Twitter bot that will scrape content from websites like thenewstack.io (The New Stack) and blog.coursera.org (Coursera’s Blog) and tweet quotes from them. The scraped content will be aggregated and will be tweeted in a round-robin fashion via Python generator functions.

      Finally, choose no for Will your product, service, or analysis make Twitter content or derived information available to a government entity?

      Twitter Bot Intent

      Next, accept Twitter’s terms and conditions, click on Submit application, and then verify your email address. Twitter will send a verification email to you after your submission of this form.

      Once you verify your email, you’ll get an Application under review page with a feedback form for the application process.

      You will also receive another email from Twitter regarding the review:

      Application Review Email

      The timeline for Twitter’s application review process can vary significantly, but often Twitter will confirm this within a few minutes. However, should your application’s review take longer than this, it is not unusual, and you should receive it within a day or two. Once you receive confirmation, Twitter has authorized you to generate your keys. You can access these under the Keys and tokens tab after clicking the details button of your app on developer.twitter.com/apps.

      Finally go to the Permissions tab on your app’s page and set the Access Permission option to Read and Write since you want to write tweet content too. Usually, you would use the read-only mode for research purposes like analyzing trends, data-mining, and so on. The final option allows users to integrate chatbots into their existing apps, since chatbots require access to direct messages.

      Twitter App Permissions Page

      You have access to Twitter’s powerful API, which will be a crucial part of your bot application. Now you’ll set up your environment and begin building your bot.

      Step 2 — Building the Essentials

      In this step, you’ll write code to authenticate your bot with Twitter using the API keys, and make the first programmatic tweet via your Twitter handle. This will serve as a good milestone in your path towards the goal of building a Twitter bot that scrapes content from The New Stack and the Coursera Blog and tweets them periodically.

      First, you’ll set up a project folder and a specific programming environment for your project.

      Create your project folder:

      Move into your project folder:

      Then create a new Python virtual environment for your project:

      Then activate your environment using the following command:

      • source bird-env/bin/activate

      This will attach a (bird-env) prefix to the prompt in your terminal window.

      Now move to your text editor and create a file called credentials.py, which will store your Twitter API keys:

      Add the following content, replacing the highlighted code with your keys from Twitter:

      bird/credentials.py

      
      ACCESS_TOKEN='your-access-token'
      ACCESS_SECRET='your-access-secret'
      CONSUMER_KEY='your-consumer-key'
      CONSUMER_SECRET='your-consumer-secret'
      

      Now, you'll install the main API library for sending requests to Twitter. For this project, you'll require the following libraries: nltk, requests, twitter, lxml, random, and time. random and time are part of Python's standard library, so you don't need to separately install these libraries. To install the remaining libraries, you'll use pip, a package manager for Python.

      Open your terminal, ensure you're in the project folder, and run the following command:

      • pip3 install lxml nltk requests twitter
      • lxml and requests: You will use them for web scraping.
      • twitter: This is the library for making API calls to Twitter's servers.
      • nltk: (natural language toolkit) You will use to split paragraphs of blogs into sentences.
      • random: You will use this to randomly select parts of an entire scraped blog post.
      • time: You will use to make your bot sleep periodically after certain actions.

      Once you have installed the libraries, you're all set to begin programming. Now, you'll import your credentials into the main script that will run the bot. Alongside credentials.py, from your text editor create a file in the bird project directory, and name it bot.py:

      In practice, you would spread the functionality of your bot across multiple files as it grows more and more sophisticated. However, in this tutorial, you'll put all of your code in a single script, bot.py, for demonstration purposes.

      First you'll test your API keys by authorizing your bot. Begin by adding the following snippet to bot.py:

      bird/bot.py

      import random
      import time
      
      from lxml.html import fromstring
      import nltk
      nltk.download('punkt')
      import requests
      from twitter import OAuth, Twitter
      
      import credentials
      

      Here, you import the required libraries; and in a couple of instances you import the necessary functions from the libraries. You will use the fromstring function later in the code to convert the string source of a scraped webpage to a tree structure that makes it easier to extract relevant information from the page. OAuth will help you in constructing an authentication object from your keys, and Twitter will build the main API object for all further communication with Twitter's servers.

      Now extend bot.py with the following lines:

      bird/bot.py

      ...
      tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
      
      oauth = OAuth(
              credentials.ACCESS_TOKEN,
              credentials.ACCESS_SECRET,
              credentials.CONSUMER_KEY,
              credentials.CONSUMER_SECRET
          )
      t = Twitter(auth=oauth)
      

      nltk.download('punkt') downloads a dataset necessary for parsing paragraphs and tokenizing (splitting) them into smaller components. tokenizer is the object you'll use later in the code for splitting paragraphs written in English.

      oauth is the authentication object constructed by feeding the imported OAuth class with your API keys. You authenticate your bot via the line t = Twitter(auth=oauth). ACCESS_TOKEN and ACCESS_SECRET help in recognizing your application. Finally, CONSUMER_KEY and CONSUMER_SECRET help in recognizing the handle via which the application interacts with Twitter. You'll use this t object to communicate your requests to Twitter.

      Now save this file and run it in your terminal using the following command:

      Your output will look similar to the following, which means your authorization was successful:

      Output

      [nltk_data] Downloading package punkt to /Users/binaryboy/nltk_data... [nltk_data] Package punkt is already up-to-date!

      If you do receive an error, verify your saved API keys with those in your Twitter developer account and try again. Also ensure that the required libraries are installed correctly. If not, use pip3 again to install them.

      Now you can try tweeting something programmatically. Type the same command on the terminal with the -i flag to open the Python interpreter after the execution of your script:

      Next, type the following to send a tweet via your account:

      • t.statuses.update(status="Just setting up my Twttr bot")

      Now open your Twitter timeline in a browser, and you'll see a tweet at the top of your timeline containing the content you posted.

      First Programmatic Tweet

      Close the interpreter by typing quit() or CTRL + D.

      Your bot now has the fundamental capability to tweet. To develop your bot to tweet useful content, you'll incorporate web scraping in the next step.

      Step 3 — Scraping Websites for Your Tweet Content

      To introduce some more interesting content to your timeline, you'll scrape content from the New Stack and the Coursera Blog, and then post this content to Twitter in the form of tweets. Generally, to scrape the appropriate data from your target websites, you have to experiment with their HTML structure. Each tweet coming from the bot you'll build in this tutorial will have a link to a blog post from the chosen websites, along with a random quote from that blog. You'll implement this procedure within a function specific to scraping content from Coursera, so you'll name it scrape_coursera().

      First open bot.py:

      Add the scrape_coursera() function to the end of your file:

      bird/bot.py

      ...
      t = Twitter(auth=oauth)
      
      
      def scrape_coursera():
      

      To scrape information from the blog, you'll first request the relevant webpage from Coursera's servers. For that you will use the get() function from the requests library. get() takes in a URL and fetches the corresponding webpage. So, you'll pass blog.coursera.org as an argument to get(). But you also need to provide a header in your GET request, which will ensure Coursera's servers recognize you as a genuine client. Add the following highlighted lines to your scrape_coursera() function to provide a header:

      bird/bot.py

      def scrape_coursera():
          HEADERS = {
              'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5)'
                            ' AppleWebKit/537.36 (KHTML, like Gecko) Cafari/537.36'
              }
      

      This header will contain information pertaining to a defined web browser running on a specific operating system. As long as this information (usually referred to as User-Agent) corresponds to real web browsers and operating systems, it doesn't matter whether the header information aligns with the actual web browser and operating system on your computer. Therefore this header will work fine for all systems.

      Once you have defined the headers, add the following highlighted lines to make a GET request to Coursera by specifying the URL of the blog webpage:

      bird/bot.py

      ...
      def scrape_coursera():
          HEADERS = {
              'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5)'
                            ' AppleWebKit/537.36 (KHTML, like Gecko) Cafari/537.36'
              }
          r = requests.get('https://blog.coursera.org', headers=HEADERS)
          tree = fromstring(r.content)
      

      This will fetch the webpage to your machine and save the information from the entire webpage in the variable r. You can assess the HTML source code of the webpage using the content attribute of r. Therefore, the value of r.content is the same as what you see when you inspect the webpage in your browser by right clicking on the page and choosing the Inspect Element option.

      Here you've also added the fromstring function. You can pass the webpage's source code to the fromstring function imported from the lxml library to construct the tree structure of the webpage. This tree structure will allow you to conveniently access different parts of the webpage. HTML source code has a particular tree-like structure; every element is enclosed in the <html> tag and nested thereafter.

      Now, open https://blog.coursera.org in a browser and inspect its HTML source using the browser's developer tools. Right click on the page and choose the Inspect Element option. You'll see a window appear at the bottom of the browser, showing part of the page's HTML source code.

      browser-inspect

      Next, right click on the thumbnail of any visible blog post and then inspect it. The HTML source will highlight the relevant HTML lines where that blog thumbnail is defined. You'll notice that all blog posts on this page are defined within a <div> tag with a class of "recent":

      blog-div

      Thus, in your code, you'll use all such blog post div elements via their XPath, which is a convenient way of addressing elements of a web page.

      To do so, extend your function in bot.py as follows:

      bird/bot.py

      ...
      def scrape_coursera():
          HEADERS = {
              'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5)'
                            ' AppleWebKit/537.36 (KHTML, like Gecko) Cafari/537.36'
                          }
          r = requests.get('https://blog.coursera.org', headers=HEADERS)
          tree = fromstring(r.content)
          links = tree.xpath('//div[@class="recent"]//div[@class="title"]/a/@href')
          print(links)
      
      scrape_coursera()
      

      Here, the XPath (the string passed to tree.xpath()) communicates that you want div elements from the entire web page source, of class "recent". The // corresponds to searching the whole webpage, div tells the function to extract only the div elements, and [@class="recent"] asks it to only extract those div elements that have the values of their class attribute as "recent".

      However, you don't need these elements themselves, you only need the links they're pointing to, so that you can access the individual blog posts to scrape their content. Therefore, you extract all the links using the values of the href anchor tags that are within the previous div tags of the blog posts.

      To test your program so far, you call the scrape_coursera() function at the end of bot.py.

      Save and exit bot.py.

      Now run bot.py with the following command:

      In your output, you'll see a list of URLs like the following:

      Output

      ['https://blog.coursera.org/career-stories-from-inside-coursera/', 'https://blog.coursera.org/unlock-the-power-of-data-with-python-university-of-michigan-offers-new-programming-specializations-on-coursera/', ...]

      After you verify the output, you can remove the last two highlighted lines from bot.py script:

      bird/bot.py

      ...
      def scrape_coursera():
          ...
          tree = fromstring(r.content)
          links = tree.xpath('//div[@class="recent"]//div[@class="title"]/a/@href')
          ~~print(links)~~
      
      ~~scrape_coursera()~~
      

      Now extend the function in bot.py with the following highlighted line to extract the content from a blog post:

      bird/bot.py

      ...
      def scrape_coursera():
          ...
          links = tree.xpath('//div[@class="recent"]//div[@class="title"]/a/@href')
          for link in links:
              r = requests.get(link, headers=HEADERS)
              blog_tree = fromstring(r.content)
      

      You iterate over each link, fetch the corresponding blog post, extract a random sentence from the post, and then tweet this sentence as a quote, along with the corresponding URL. Extracting a random sentence involves three parts:

      1. Grabbing all the paragraphs in the blog post as a list.
      2. Selecting a paragraph at random from the list of paragraphs.
      3. Selecting a sentence at random from this paragraph.

      You'll execute these steps for each blog post. For fetching one, you make a GET request for its link.

      Now that you have access to the content of a blog, you will introduce the code that executes these three steps to extract the content you want from it. Add the following extension to your scraping function that executes the three steps:

      bird/bot.py

      ...
      def scrape_coursera():
          ...
          for link in links:
              r = requests.get(link, headers=HEADERS)
              blog_tree = fromstring(r.content)
              paras = blog_tree.xpath('//div[@class="entry-content"]/p')
              paras_text = [para.text_content() for para in paras if para.text_content()]
              para = random.choice(paras_text)
              para_tokenized = tokenizer.tokenize(para)
              for _ in range(10):
                  text = random.choice(para)
                  if text and 60 < len(text) < 210:
                      break
      

      If you inspect the blog post by opening the first link, you'll notice that all the paragraphs belong to the div tag having entry-content as its class. Therefore, you extract all paragraphs as a list with paras = blog_tree.xpath('//div[@class="entry-content"]/p').

      Div Enclosing Paragraphs

      The list elements aren't literal paragraphs; they are Element objects. To extract the text out of these objects, you use the text_content() method. This line follows Python's list comprehension design pattern, which defines a collection using a loop that is usually written out in a single line. In bot.py, you extract the text for each paragraph element object and store it in a list if the text is not empty. To randomly choose a paragraph from this list of paragraphs, you incorporate the random module.

      Finally, you have to select a sentence at random from this paragraph, which is stored in the variable para. For this task, you first break the paragraph into sentences. One approach to accomplish this is using the Python's split() method. However this can be difficult since a sentence can be split at multiple breakpoints. Therefore, to simplify your splitting tasks, you leverage natural language processing through the nltk library. The tokenizer object you defined earlier in the tutorial will be useful for this purpose.

      Now that you have a list of sentences, you call random.choice() to extract a random sentence. You want this sentence to be a quote for a tweet, so it can't exceed 280 characters. However, for aesthetic reasons, you'll select a sentence that is neither too big nor too small. You designate that your tweet sentence should have a length between 60 to 210 characters. The sentence random.choice() picks might not satisfy this criterion. To identify the right sentence, your script will make ten attempts, checking for the criterion each time. Once the randomly picked-up sentence satisfies your criterion, you can break out of the loop.

      Although the probability is quite low, it is possible that none of the sentences meet this size condition within ten attempts. In this case, you'll ignore the corresponding blog post and move on to the next one.

      Now that you have a sentence to quote, you can tweet it with the corresponding link. You can do this by yielding a string that contains the randomly picked-up sentence as well as the corresponding blog link. The code that calls this scrape_coursera() function will then post the yielded string to Twitter via Twitter's API.

      Extend your function as follows:

      bird/bot.py

      ...
      def scrape_coursera():
          ...
          for link in links:
              ...
              para_tokenized = tokenizer.tokenize(para)
              for _ in range(10):
                  text = random.choice(para)
                  if text and 60 < len(text) < 210:
                      break
              else:
                  yield None
              yield '"%s" %s' % (text, link)
      

      The script only executes the else statement when the preceding for loop doesn't break. Thus, it only happens when the loop is not able to find a sentence that fits your size condition. In that case, you simply yield None so that the code that calls this function is able to determine that there is nothing to tweet. It will then move on to call the function again and get the content for the next blog link. But if the loop does break it means the function has found an appropriate sentence; the script will not execute the else statement, and the function will yield a string composed of the sentence as well as the blog link, separated by a single whitespace.

      The implementation of the scrape_coursera() function is almost complete. If you want to make a similar function to scrape another website, you will have to repeat some of the code you've written for scraping Coursera's blog. To avoid rewriting and duplicating parts of the code and to ensure your bot's script follows the DRY principle (Don't Repeat Yourself), you'll identify and abstract out parts of the code that you will use again and again for any scraper function written later.

      Regardless of the website the function is scraping, you'll have to randomly pick up a paragraph and then choose a random sentence from this chosen paragraph — you can extract out these functionalities in separate functions. Then you can simply call these functions from your scraper functions and achieve the desired result. You can also define HEADERS outside the scrape_coursera() function so that all of the scraper functions can use it. Therefore, in the code that follows, the HEADERS definition should precede that of the scraper function, so that eventually you're able to use it for other scrapers:

      bird/bot.py

      ...
      HEADERS = {
          'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5)'
                        ' AppleWebKit/537.36 (KHTML, like Gecko) Cafari/537.36'
          }
      
      
      def scrape_coursera():
          r = requests.get('https://blog.coursera.org', headers=HEADERS)
          ...
      

      Now you can define the extract_paratext() function for extracting a random paragraph from a list of paragraph objects. The random paragraph will pass to the function as a paras argument, and return the chosen paragraph's tokenized form that you'll use later for sentence extraction:

      bird/bot.py

      ...
      HEADERS = {
              'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5)'
                            ' AppleWebKit/537.36 (KHTML, like Gecko) Cafari/537.36'
              }
      
      def extract_paratext(paras):
          """Extracts text from <p> elements and returns a clean, tokenized random
          paragraph."""
      
          paras = [para.text_content() for para in paras if para.text_content()]
          para = random.choice(paras)
          return tokenizer.tokenize(para)
      
      
      def scrape_coursera():
          r = requests.get('https://blog.coursera.org', headers=HEADERS)
          ...
      

      Next, you will define a function that will extract a random sentence of suitable length (between 60 and 210 characters) from the tokenized paragraph it gets as an argument, which you can name as para. If such a sentence is not discovered after ten attempts, the function returns None instead. Add the following highlighted code to define the extract_text() function:

      bird/bot.py

      ...
      
      def extract_paratext(paras):
          ...
          return tokenizer.tokenize(para)
      
      
      def extract_text(para):
          """Returns a sufficiently-large random text from a tokenized paragraph,
          if such text exists. Otherwise, returns None."""
      
          for _ in range(10):
              text = random.choice(para)
              if text and 60 < len(text) < 210:
                  return text
      
          return None
      
      
      def scrape_coursera():
          r = requests.get('https://blog.coursera.org', headers=HEADERS)
          ...
      

      Once you have defined these new helper functions, you can redefine the scrape_coursera() function to look as follows:

      bird/bot.py

      ...
      def extract_paratext():
          for _ in range(10):<^>
              text = random.choice(para)
          ...
      
      
      def scrape_coursera():
          """Scrapes content from the Coursera blog."""
      
          url = 'https://blog.coursera.org'
          r = requests.get(url, headers=HEADERS)
          tree = fromstring(r.content)
          links = tree.xpath('//div[@class="recent"]//div[@class="title"]/a/@href')
      
          for link in links:
              r = requests.get(link, headers=HEADERS)
              blog_tree = fromstring(r.content)
              paras = blog_tree.xpath('//div[@class="entry-content"]/p')
              para = extract_paratext(paras)
              text = extract_text(para)
              if not text:
                  continue
      
              yield '"%s" %s' % (text, link)
      

      Save and exit bot.py.

      Here you're using yield instead of return because, for iterating over the links, the scraper function will give you the tweet strings one-by-one in a sequential fashion. This means when you make a first call to the scraper sc defined as sc = scrape_coursera(), you will get the tweet string corresponding to the first link among the list of links that you computed within the scraper function. If you run the following code in the interpreter, you'll get string_1 and string_2 as displayed below, if the links variable within scrape_coursera() holds a list that looks like ["https://thenewstack.io/cloud-native-live-twistlocks-virtual-conference/", "https://blog.coursera.org/unlock-the-power-of-data-with-python-university-of-michigan-offers-new-programming-specializations-on-coursera/", ...].

      Instantiate the scraper and call it sc:

      >>> sc = scrape_coursera()
      

      It is now a generator; it generates or scrapes relevant content from Coursera, one at a time. You can access the scraped content one-by-one by calling next() over sc sequentially:

      >>> string_1 = next(sc)
      >>> string_2 = next(sc)
      

      Now you can print the strings you've defined to display the scraped content:

      >>> print(string_1)
      "Other speakers include Priyanka Sharma, director of cloud native alliances at GitLab and Dan Kohn, executive director of the Cloud Native Computing Foundation." https://thenewstack.io/cloud-native-live-twistlocks-virtual-conference/
      >>>
      >>> print(string_2)
      "You can learn how to use the power of Python for data analysis with a series of courses covering fundamental theory and project-based learning." https://blog.coursera.org/unlock-the-power-of-data-with-python-university-of-michigan-offers-new-programming-specializations-on-coursera/
      >>>
      

      If you use return instead, you will not be able to obtain the strings one-by-one and in a sequence. If you simply replace the yield with return in scrape_coursera(), you'll always get the string corresponding to the first blog post, instead of getting the first one in the first call, second one in the second call, and so on. You can modify the function to simply return a list of all the strings corresponding to all the links, but that is more memory intensive. Also, this kind of program could potentially make a lot of requests to Coursera's servers within a short span of time if you want the entire list quickly. This could result in your bot getting temporarily banned from accessing a website. Therefore, yield is the best fit for a wide variety of scraping jobs, where you only need information scraped one-at-a-time.

      Step 4 — Scraping Additional Content

      In this step, you'll build a scraper for thenewstack.io. The process is similar to what you've completed in the previous step, so this will be a quick overview.

      Open the website in your browser and inspect the page source. You'll find here that all blog sections are div elements of class normalstory-box.

      HTML Source Inspection of The New Stack website

      Now you'll make a new scraper function named scrape_thenewstack() and make a GET request to thenewstack.io from within it. Next, extract the links to the blogs from these elements and then iterate over each link. Add the following code to achieve this:

      bird/bot.py

      ...
      def scrape_coursera():
          ...
          yield '"%s" %s' % (text, link)
      
      
      def scrape_thenewstack():
          """Scrapes news from thenewstack.io"""
      
          r = requests.get('https://thenewstack.io', verify=False)
      
              tree = fromstring(r.content)
              links = tree.xpath('//div[@class="normalstory-box"]/header/h2/a/@href')
              for link in links:
      

      You use the verify=False flag because websites can sometimes have expired security certificates and it's OK to access them if no sensitive data is involved, as is the case here. The verify=False flag tells the requests.get method to not verify the certificates and continue fetching data as usual. Otherwise, the method throws an error about expired security certificates.

      You can now extract the paragraphs of the blog corresponding to each link, and use the extract_paratext() function you built in the previous step to pull out a random paragraph from the list of available paragraphs. Finally, extract a random sentence from this paragraph using the extract_text() function, and then yield it with the corresponding blog link. Add the following highlighted code to your file to accomplish these tasks:

      bird/bot.py

      ...
      def scrape_thenewstack():
          ...
          links = tree.xpath('//div[@class="normalstory-box"]/header/h2/a/@href')
      
          for link in links:
              r = requests.get(link, verify=False)
              tree = fromstring(r.content)
              paras = tree.xpath('//div[@class="post-content"]/p')
              para = extract_paratext(paras)
              text = extract_text(para)  
              if not text:
                  continue
      
              yield '"%s" %s' % (text, link)
      

      You now have an idea of what a scraping process generally encompasses. You can now build your own, custom scrapers that can, for example, scrape the images in blog posts instead of random quotes. For that, you can look for the relevant <img> tags. Once you have the right path for tags, which serve as their identifiers, you can access the information within tags using the names of corresponding attributes. For example, in the case of scraping images, you can access the links of images using their src attributes.

      At this point, you've built two scraper functions for scraping content from two different websites, and you've also built two helper functions to reuse functionalities that are common across the two scrapers. Now that your bot knows how to tweet and what to tweet, you'll write the code to tweet the scraped content.

      Step 5 — Tweeting the Scraped Content

      In this step, you'll extend the bot to scrape content from the two websites and tweet it via your Twitter account. More precisely, you want it to tweet content from the two websites alternately, and at regular intervals of ten minutes, for an indefinite period of time. Thus, you will use an infinite while loop to implement the desired functionality. You'll do this as part of a main() function, which will implement the core high-level process that you'll want your bot to follow:

      bird/bot.py

      ...
      def scrape_thenewstack():
          ...
          yield '"%s" %s' % (text, link)
      
      
      def main():
          """Encompasses the main loop of the bot."""
          print('---Bot started---n')
          news_funcs = ['scrape_coursera', 'scrape_thenewstack']
          news_iterators = []  
          for func in news_funcs:
              news_iterators.append(globals()[func]())
          while True:
              for i, iterator in enumerate(news_iterators):
                  try:
                      tweet = next(iterator)
                      t.statuses.update(status=tweet)
                      print(tweet, end='nn')
                      time.sleep(600)  
                  except StopIteration:
                      news_iterators[i] = globals()[newsfuncs[i]]()
      

      You first create a list of the names of the scraping functions you defined earlier, and call it as news_funcs. Then you create an empty list that will hold the actual scraper functions, and name that list as news_iterators. You then populate it by going through each name in the news_funcs list and appending the corresponding iterator in the news_iterators list. You're using Python's built-in globals() function. This returns a dictionary that maps variable names to actual variables within your script. An iterator is what you get when you call a scraper function: for example, if you write coursera_iterator = scrape_coursera(), then coursera_iterator will be an iterator on which you can invoke next() calls. Each next() call will return a string containing a quote and its corresponding link, exactly as defined in the scrape_coursera() function's yield statement. Each next() call goes through one iteration of the for loop in the scrape_coursera() function. Thus, you can only make as many next() calls as there are blog links in the scrape_coursera() function. Once that number exceeds, a StopIteration exception will be raised.

      Once both the iterators populate the news_iterators list, the main while loop starts. Within it, you have a for loop that goes through each iterator and tries to obtain the content to be tweeted. After obtaining the content, your bot tweets it and then sleeps for ten minutes. If the iterator has no more content to offer, a StopIteration exception is raised, upon which you refresh that iterator by re-instantiating it, to check for the availability of newer content on the source website. Then you move on to the next iterator, if available. Otherwise, if execution reaches the end of the iterators list, you restart from the beginning and tweet the next available content. This makes your bot tweet content alternately from the two scrapers for as long as you want.

      All that remains now is to make a call to the main() function. You do this when the script is called directly by the Python interpreter:

      bird/bot.py

      ...
      def main():
          print('---Bot started---n')<^>
          news_funcs = ['scrape_coursera', 'scrape_thenewstack']
          ...
      
      if __name__ == "__main__":  
          main()
      

      The following is a completed version of the bot.py script. You can also view the script on this GitHub repository.

      bird/bot.py

      
      """Main bot script - bot.py
      For the DigitalOcean Tutorial.
      """
      
      
      import random
      import time
      
      
      from lxml.html import fromstring
      import nltk  
      nltk.download('punkt')
      import requests  
      
      from twitter import OAuth, Twitter
      
      
      import credentials
      
      tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
      
      oauth = OAuth(
              credentials.ACCESS_TOKEN,
              credentials.ACCESS_SECRET,
              credentials.CONSUMER_KEY,
              credentials.CONSUMER_SECRET
          )
      t = Twitter(auth=oauth)
      
      HEADERS = {
              'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5)'
                            ' AppleWebKit/537.36 (KHTML, like Gecko) Cafari/537.36'
              }
      
      
      def extract_paratext(paras):
          """Extracts text from <p> elements and returns a clean, tokenized random
          paragraph."""
      
          paras = [para.text_content() for para in paras if para.text_content()]
          para = random.choice(paras)
          return tokenizer.tokenize(para)
      
      
      def extract_text(para):
          """Returns a sufficiently-large random text from a tokenized paragraph,
          if such text exists. Otherwise, returns None."""
      
          for _ in range(10):
              text = random.choice(para)
              if text and 60 < len(text) < 210:
                  return text
      
          return None
      
      
      def scrape_coursera():
          """Scrapes content from the Coursera blog."""
          url = 'https://blog.coursera.org'
          r = requests.get(url, headers=HEADERS)
          tree = fromstring(r.content)
          links = tree.xpath('//div[@class="recent"]//div[@class="title"]/a/@href')
      
          for link in links:
              r = requests.get(link, headers=HEADERS)
              blog_tree = fromstring(r.content)
              paras = blog_tree.xpath('//div[@class="entry-content"]/p')
              para = extract_paratext(paras)  
              text = extract_text(para)  
              if not text:
                  continue
      
              yield '"%s" %s' % (text, link)  
      
      
      def scrape_thenewstack():
          """Scrapes news from thenewstack.io"""
      
          r = requests.get('https://thenewstack.io', verify=False)
      
          tree = fromstring(r.content)
          links = tree.xpath('//div[@class="normalstory-box"]/header/h2/a/@href')
      
          for link in links:
              r = requests.get(link, verify=False)
              tree = fromstring(r.content)
              paras = tree.xpath('//div[@class="post-content"]/p')
              para = extract_paratext(paras)
              text = extract_text(para)  
              if not text:
                  continue
      
              yield '"%s" %s' % (text, link)
      
      
      def main():
          """Encompasses the main loop of the bot."""
          print('Bot started.')
          news_funcs = ['scrape_coursera', 'scrape_thenewstack']
          news_iterators = []  
          for func in news_funcs:
              news_iterators.append(globals()[func]())
          while True:
              for i, iterator in enumerate(news_iterators):
                  try:
                      tweet = next(iterator)
                      t.statuses.update(status=tweet)
                      print(tweet, end='n')
                      time.sleep(600)
                  except StopIteration:
                      news_iterators[i] = globals()[newsfuncs[i]]()
      
      
      if __name__ == "__main__":  
          main()
      
      

      Save and exit bot.py.

      The following is a sample execution of bot.py:

      You will receive output showing the content that your bot has scraped, in a similar format to the following:

      Output

      [nltk_data] Downloading package punkt to /Users/binaryboy/nltk_data... [nltk_data] Package punkt is already up-to-date! ---Bot started--- "Take the first step toward your career goals by building new skills." https://blog.coursera.org/career-stories-from-inside-coursera/ "Other speakers include Priyanka Sharma, director of cloud native alliances at GitLab and Dan Kohn, executive director of the Cloud Native Computing Foundation." https://thenewstack.io/cloud-native-live-twistlocks-virtual-conference/ "You can learn how to use the power of Python for data analysis with a series of courses covering fundamental theory and project-based learning." https://blog.coursera.org/unlock-the-power-of-data-with-python-university-of-michigan-offers-new-programming-specializations-on-coursera/ "“Real-user monitoring is really about trying to understand the underlying reasons, so you know, ‘who do I actually want to fly with?" https://thenewstack.io/how-raygun-co-founder-and-ceo-spun-gold-out-of-monitoring-agony/

      After a sample run of your bot, you'll see a full timeline of programmatic tweets posted by your bot on your Twitter page. It will look something like the following:

      Programmatic Tweets posted

      As you can see, the bot is tweeting the scraped blog links with random quotes from each blog as highlights. This feed is now an information feed with tweets alternating between blog quotes from Coursera and thenewstack.io. You've built a bot that aggregates content from the web and posts it on Twitter. You can now broaden the scope of this bot as per your wish by adding more scrapers for different websites, and the bot will tweet content coming from all the scrapers in a round-robin fashion, and in your desired time intervals.

      Conclusion

      In this tutorial you built a basic Twitter bot with Python and scraped some content from the web for your bot to tweet. There are many bot ideas to try; you could also implement your own ideas for a bot's utility. You can combine the versatile functionalities offered by Twitter's API and create something more complex. For a version of a more sophisticated Twitter bot, check out chirps, a Twitter bot framework that uses some advanced concepts like multithreading to make the bot do multiple things simultaneously. There are also some fun-idea bots, like misheardly. There are no limits on the creativity one can use while building Twitter bots. Finding the right API endpoints to hit for your bot's implementation is essential.

      Finally, bot etiquette or ("botiquette") is important to keep in mind when building your next bot. For example, if your bot incorporates retweeting, make all tweets' text pass through a filter to detect abusive language before retweeting them. You can implement such features using regular expressions and natural language processing. Also, while looking for sources to scrape, follow your judgment and avoid ones that spread misinformation. To read more about botiquette, you can visit this blog post by Joe Mayo on the topic.





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