Estimating Ideological Positions with Twitter Data

This GitHub repository contains code and materials related to the article "Birds of a Feather Tweet Together. Bayesian Ideal Point Estimation Using Twitter Data," published in Political Analysis in 2015.

The original replication code can be found in the replication folder. See also Dataverse for the full replication materials, including data and output.

For updated versions of the code, that implement a more computationally efficient approach introduced in Barberá et al (2015, Psychological Science), see the folders 2016-update/, 2018-update/, and 2020-update/.

As an application of the method, in June 2015 I wrote a blog post on The Monkey Cage / Washington Post entitled "Who is the most conservative Republican candidate for president?." The replication code for the figure in the post is available in the primary folder.

Finally, this repository also contains an R package (tweetscores) with several functions to facilitate the application of this method in future research. The rest of this README file provides a tutorial with instructions showing how to use it.

NOTE: the package currently only support v1.1 of Twitter's API, which at some point in the near future will be deprecated. This package is not currently maintained, so use at your own risk.

Authentication

In order to download data from Twitter’s API, you will first need to create a developer account in developer.twitter.com. After receiving your approval, it’s necessary to follow these steps:

1. Go to Twitter's Developer Portal and sign in
2. Click on "Overview", then “Create New App”
3. Follow the instructions.
4. Copy the API Key and Secret, as well as the Access Token and Secret, and paste them below:

   my_oauth <- list(consumer_key = "CONSUMER_KEY",
   consumer_secret = "CONSUMER_SECRET",
   access_token="ACCESS_TOKEN",
   access_token_secret = "ACCESS_TOKEN_SECRET")

5. Change current folder into a folder where you will save all your tokens

   setwd("~/Dropbox/credentials/twitter&quot")

6. Now you can save oauth token for use in future sessions with R

   save(my_oauth, file="my_oauth")

   Installing the tweetscores package

   The following code will install the tweetscores package, as well as all other R packages necessary for the functions to run.

   toInstall <- c("ggplot2", "scales", "R2WinBUGS", "devtools", "yaml", "httr", "RJSONIO")
   install.packages(toInstall, repos = "http://cran.r-project.org")
   library(devtools)
   install_github("pablobarbera/twitter_ideology/pkg/tweetscores")

   Estimating the ideological positions of a US Twitter user

   We can now go ahead and estimate ideology for any Twitter users in the US. In order to do so, the package includes pre-estimated ideology for political accounts and media outlets, so here we’re just replicating the second stage in the method – that is, estimating a user’s ideology based on the accounts they follow.

   # load package
   library(tweetscores)

   # downloading friends of a user
   user <- "p_barbera"
   friends <- getFriends(screen_name=user, oauth="~/Dropbox/credentials/twitter")

   ## /Users/pablobarbera/Dropbox/credentials/twitter/oauth_token_32 
   ## 15  API calls left
   ## 1065 friends. Next cursor:  0 
   ## 14  API calls left

   # estimate ideology with MCMC method
   results <- estimateIdeology(user, friends)

   ## p_barbera follows 11 elites: nytimes maddow caitlindewey carr2n fivethirtyeight 
   NickKristof nytgraphics nytimesbits NYTimeskrugman nytlabs thecaucus
   ## Chain 1
   |=================================================================| 100%
   ## Chain 2
   |=================================================================| 100%
   

   Once we have this set of estimates, we can analyze them with a series of built-in functions.

   # summarizing results
   summary(results)

   ##        mean   sd  2.5%   25%   50%   75% 97.5% Rhat n.eff
   ## beta  -2.30 0.57 -3.37 -2.72 -2.25 -1.92 -1.26 1.02   200
   ## theta -1.78 0.30 -2.28 -1.99 -1.82 -1.59 -1.11 1.00   200

   # assessing chain convergence using a trace plot
   tracePlot(results, "theta")

   

   # comparing with other ideology estimates
   plot(results)

   

   Faster ideology estimation

   The previous function relies on a Metropolis-Hastings sampling algorithm to estimate ideology. However, we can also use Maximum Likelihood estimation to compute the distribution of the latent parameters. This method is much faster, since it’s not sampling from the posterior distribution of the parameters, but it will tend to give smaller standard errors. However, overall the results should be almost identical. (See here for the actual estimation functions for each of these two approaches.)

   # faster estimation using maximum likelihood
   results <- estimateIdeology(user, friends, method="MLE")

   ## p_barbera follows 11 elites: nytimes maddow caitlindewey carr2n fivethirtyeight 
   NickKristof nytgraphics nytimesbits NYTimeskrugman nytlabs thecaucus

   summary(results)

   ##        mean   sd  2.5%   25%   50%   75% 97.5% Rhat n.eff
   ## beta  -2.30 0.57 -3.37 -2.72 -2.25 -1.92 -1.26 1.02   200
   ## theta -1.78 0.30 -2.28 -1.99 -1.82 -1.59 -1.11 1.00   200

   Estimation using correspondence analysis

   One limitation of the previous method is that users need to follow at least one political account. To partially overcome this problem, in a recently published article in Psychological Science, we add a third stage to the model where we add additional accounts (not necessarily political) followed predominantely by liberal or by conservative users, under the assumption that if other users also follow this same set of accounts, they are also likely to be liberal or conservative. To reduce computational costs, we rely on correspondence analysis to project all users onto the latent ideological space (see Supplementary Materials), and then we normalize all the estimates so that they follow a normal distribution with mean zero and standard deviation one. This package also includes a function that reproduces the last stage in the estimation, after all the additional accounts have been added:

   # estimation using correspondence analysis
   results <- estimateIdeology2(user, friends)

   ## p_barbera follows 22 elites: andersoncooper, billclinton, BreakingNews, 
   ## cnnbrk, davidaxelrod, Gawker, HillaryClinton, maddow, MaddowBlog, mashable, mattyglesias,
   ## NateSilver538, NickKristof, nytimes, NYTimeskrugman, repjoecrowley, RonanFarrow, 
   ## SCOTUSblog, StephenAtHome, TheDailyShow, TheEconomist, UniteBlue

   results

   ## [1] -1.06158