In this course we’ll learn how to create Twitter bots. We’ll first learn how to interface with the Twitter API using Twit.js, which allows us to post statuses, search tweets, and interact with followers. We’ll create several example bots that show the diversity of possibilities, using powerful tools such as RiTa.js, Google’s Cloud Vision API, and Tracery. We’ll learn how to tweet data from Google Spreadsheets, and to compose and tweet a song for an audio bot. We’ll also learn how to host our bot logic on Heroku and Docker. 

We’ll go over the basic Twitter bot setup, including how to start a project from scratch, install necessary packages, and store our private keys in environment variables. 

Set Up a Basic Twitter Bot with Twit.js

In this lesson, our bot will tweet hello to the Twitter world! We'll do this by using Twit's POST method to post our status to the statuses/update endpoint.

Post Statuses with Twit.js

We’ll learn how your bot can get its list of followers, follow people, and look up friendships. We'll use Twit's GET method to get our followers at the followers/list endpoint, to get the users we follow at the friends/ids and friends/list endpoints, and to look up our friendships with the friendships/lookup endpoint. We'll also use Twit's POST method to follow someone at the friendships/create endpoint, and to send messages by posting to the direct_messages/new endpoint. 

Work with Users in Twit.js

We’ll learn the basics of interacting with tweets, including retweeting, deleting, favoriting, and replying to tweets. We'll get our home timeline by using Twit's GET method to access the `statuses/home_timeline` endpoint, including the count parameter, which lets us get back a certain number of tweets. We'll also pass it a callback. We'll learn how to cycle through the data (the tweets) we get back and see what information is included. We'll learn how to retweet statuses by posting to `statuses/retweet` and including the tweet id. We can unretweet by posting to `statuses/unretweet` with the same tweet id. We'll also learn how to like a tweet by posting to `favorites/create` with a tweet id, and unlike a tweet by posting to `favorites/destroy` with a tweet id. We'll also learn how to reply to a tweet by posting to `statuses/update`, with a status that includes the handle of the user we're replying to and `in_reply_to_status_id` parameter, which is the id of the tweet we're replying to. We'll learn how to delete a tweet by posting to `statuses/destroy` with the tweet's id. 

Interact with Tweets with Twit.js 

We’ll learn how to search tweets with the Twitter Search API, using Twit's GET method to get the search/tweets endpoint. We'll use the query and count parameters to get the search term(s) and number of tweets that we want. We'll learn how to get exact phrases, multiple words, one of several words, emoticons, hashtags, photos/videos, urls, and how to remove words from our results. We'll learn how to implement the safe filter and how to filter by media, website, or date. We'll learn how to get recent results, popular results, results by location, and results by language. 

The search API returns for relevance, and not completeness. If you want all the tweets from a search term, you should use the stream API (which we'll go over in the next lesson). 

Use the Twitter Search API with Twit.js

We’ll get a real-time stream of tweets, and learn how to filter a stream by keyword, location, and user.

Use the Twitter Stream API with Twit.js

In this lesson, we’ll create multiple functions to request, download, and save photos and data from NASA's API, and then have our bot upload these photos to Twitter and post them along with their descriptions. We'll also learn how to tweet videos using a video from NASA’s space archives.

Tweet Media Files with Twit.js

In this lesson, we’ll give our bot a large input of past text that we’ve written (essays, other tweets, etc.) and, using markov chains, have it create tweets that sound like ourselves! 

For more information about Markov chains, see Markov Chains explained visually:
http://setosa.io/ev/markov-chains/

The RiTa library is a powerful library for working with text and text generation. See the reference here:
http://rednoise.org/rita/reference/index.php

Make A Bot That Sounds Like You with RiTa.js

With this bot, we’ll find the number of faces in a photo that is tweeted at us, and respond back with what emotions the faces are expressing, using the Google Cloud Vision API.

The Google Cloud Vision API is worth exploring, and you'll need to create an account before this lesson:
https://cloud.google.com/vision/

Make a Bot That Analyzes Human Emotions in Photos with Google Cloud Vision API

Tracery is a brilliant tool to more easily create text grammars and structure. In this lesson, we’ll create a bot that tweets out tiny stories. 

We'll learn what a grammar is in this context, and how to create one with Tracery. We'll first create a simple story with character, action, place, and object variables, and learn how to add modifiers. Then, we'll create a more complex one, and learn how to set variables that we want to be consistent throughout the story, such as pronouns.

More information and resources on Tracery can be found at:
https://github.com/galaxykate/tracery
and 
http://www.crystalcodepalace.com/traceryTut.html

Make A Story Generation Bot with Tracery

In this lesson, we’ll learn how to retrieve and tweet data from Google Spreadsheets. We'll use Tabletop.js to make this easier. More information on Tabletop can be found at https://github.com/jsoma/tabletop.

Retrieve And Tweet Information from Google Spreadsheets

Make a Twitter Audio Bot That Composes a Song Based on a Tweet - In the final bot lesson, we'll compose a ditty based on a tweet, save it as an audio file, and post it to Twitter. Because Twitter only supports uploading audio in video form, we'll learn how to create a video from the MIDI file and post it to Twitter. This is a longer video since we are going over how to create this pipeline from scratch.

For more Natural Language Processing, see the course on Natural: https://egghead.io/courses/natural-language-processing-in-javascript-with-natural

We'll use RiTa to tokenize the text of a tweet and find the parts of speech:
https://rednoise.org/rita/

We'll use Jsmidigen to compose a tune in a MIDI format:
https://www.npmjs.com/package/jsmidgen

We'll also use FFMPEG, which will help us create a video from our audio and a picture:
https://ffmpeg.org/

And we'll use TiMidity to convert our MIDI file to a Wav file:
http://timidity.sourceforge.net/

You can use any image in place of the black image used in this video.

Make a Twitter Audio Bot That Composes a Song Based on a Tweet

In this lesson, we’ll learn how to host a simple bot with Heroku. We'll learn how to create a new Heroku application and how to deploy our code to Heroku using git. We’ll learn how to change our project from a web app to a worker app and how to create a Procfile. We'll also see how to see output from our Heroku app with the command `heroku logs`.

Host a Simple Twitter Bot Using Heroku

We’ll learn how to host a complex Twitter bot (a bot that requires external tools that need to be installed on the system) with Heroku and Docker. We'll learn how to configure our Docker file, using an image that already has node installed on it. We'll use the Heroku Container Registry to deploy an app to heroku that was created with a local container, and deploy it as a worker app.

Create your own twitter bots

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