Preface

• This homework is due Wednesday September 25, 2019 at 11:59 PM.
• When you have completed the assignment, knit the R Markdown, commit your changes and push to GitHub.
• If you do not include axis labels and plot titles, then points will be deducted.
• You are welcome and encouraged to discuss homework problems with others in order to better understand it, but the work you turn in must be your own. You must write your own code, data analyses, and communicate and explain the results in your own words and with your own visualizations. All students turning in plagiarized solutions will be reported to Office of Academic Integrity, and will fail the assignment.

Motivation

Vaccines have faced criticism from the beginning of development. However, recently in the United States and around the world, vaccination has faced increasing skepticism and vaccination rates have begun to decline. Anti-vaccination rhetoric is especially prevalent on social media where platforms serve as an open space for misinformation to spread.

The goal of this homework is to download Twitter data in preparation for being able to identify tweets as “pro” or “anti” vaccination (e.g. identify Twitter users and tweets that are “anti-vaccination.” or e.g.  identify the sentiment of tweets). Tweets often have an associated location, thus this would allow researchers to locate communities where anti-vaccination sentiment is growing. This could help healthcare professionals identify communities that are at higher risk of infectious diseases.

Some R packages that you might helpful are the packages in the tidyverse and the rtweet package. The latter is needed to download the Twitter data, but you will need to sign up for a Twitter developer account, create an application, and enter your own keys & credentials (for more information on this see Problem 1).

Problem 1

Using the rtweet package, search for the 5,000 most recent tweets that contain the terms “antivax”, “provax”, “vaccineswork”, “vaccinesdontwork”, or “vaccines” using the search_tweets() function, excluding retweets, and only including tweets in English.

Helpful hints: To help you get started with the rtweet package, you will need to sign up for a Twitter developer account, create an application, authenticate using a web-browser or access token/secret method. Note that we have set the code chunk eval=FALSE so it is ignored when this file is knit.

library(rtweet)

twitter_token <- create_token(
  app = "twitter_app_name",
  consumer_key = "XXXXXXXXXXXXXXX",
  consumer_secret = "XXXXXXXXXXXXXXXXXXXXX",
  access_token = "XXXXXXXXXXXXXXXXXXXXXXXXXXX",
  access_secret = "XXXXXXXXXXXXXXXXX")

Once the twitter_token is created it is stored in the workspace and you are free to use all the functions in rtweet.

## add your code here

Problem 2

What data exists in the results from Problem 1?

## add your code here

Problem 3

After collecting the tweets with the rtweet package, save the data as a .csv file called twitter_data.csv using the readr package. Save the .csv in your GitHub repo and make sure you include when you push your changes.

We set the code chunks above to eval=FALSE so that when you knit this document, you will not access the API each time you knit. Also, this is an important step because the data you collect with the API can be very different every time. Finally, it takes a few minutes to download new tweets, so we don’t want to wait each time we knit.

## add your code here

Note: You may have to deal columns that contained e.g.  lists because they are not compatible for export.

Problem 4

Keeping in mind the original goal,

The goal of this homework is to download Twitter data in preparation for being able to identify tweets as “pro” or “anti” vaccination (e.g. identify Twitter users and tweets that are “anti-vaccination.”). Tweets often have an associated location, thus this would allow researchers to locate communities where anti-vaccination sentiment is growing. This could help healthcare professionals identify communities that are at higher risk of infectious diseases.

and using the dataset that you have now created, create a data analysis plan. Describe in great detail what you will need to do in terms of data tidying, wrangling, exploratory data analysis, data visualization and any modeling / analyses that you would like to complete to accomplish the goal above. Explore the data a bit (e.g. plots or other forms of exploratory data analyses) to help you understand what format the data are currently in and how to get it to the format you will need to complete your data analysis plan. Clearly state who your target audience is and think carefully about what audience you have in mind as you build your data analysis plan.

Note: As you formulate and write up your data analysis plan, you might realize that you need to go back to Problem 2 to add any other additional information that you need to complete your data analysis. Iterate until you are happy with both the data you gathered and your data analysis plan.

## add your code here