kevinmcdermott062 / botrnot

R package for detecting Twitter bots via machine learning

botrnot

An R package for classifying Twitter accounts as bot or not.

Features

Uses machine learning to classify Twitter accounts as bots or not bots. The default model is 93.53% accurate when classifying bots and 95.32% accurate when classifying non-bots. The fast model is 91.78% accurate when classifying bots and 92.61% accurate when classifying non-bots.

Overall, the default model is correct 93.8% of the time.

Overall, the fast model is correct 91.9% of the time.

Install

Install from Github.

if (!requireNamespace("devtools", quietly = TRUE)) {
  install.packages("devtools")
}
devtools::install_github("mkearney/botrnot")

API authorization

Users must be authorized in order to interact with Twitter's API. To setup your machine for authorized request, see these instructions provided in the rtweet package.

Usage

There's one function botornot(). Give it a vector of screen names or user IDs and let it go to work.

## load package
library(botrnot)

## select users
users <- c("realdonaldtrump", "netflix_bot",
  "kearneymw", "dataandme", "hadleywickham",
  "ma_salmon", "juliasilge", "tidyversetweets", 
  "American__Voter", "mothgenerator", "hrbrmstr")

## get botornot estimates
data <- botornot(users)

## arrange by prob ests
data[order(data$prob_bot), ]
#> # A tibble: 11 x 2
#>    user            prob_bot
#>    <chr>              <dbl>
#>  1 hadleywickham    0.00290
#>  2 realDonaldTrump  0.00753
#>  3 kearneymw        0.0171 
#>  4 ma_salmon        0.0582 
#>  5 juliasilge       0.0852 
#>  6 hrbrmstr         0.0858 
#>  7 dataandme        0.174  
#>  8 netflix_bot      0.974  
#>  9 tidyversetweets  0.997  
#> 10 mothgenerator    0.999  
#> 11 American__Voter  0.999

Integration with rtweet

The botornot() function also accepts data returned by rtweet functions.

## load rtweet
library(rtweet)
#> 
#> Attaching package: 'rtweet'
#> The following object is masked from 'package:tfse':
#> 
#>     round_time

## get most recent 100 tweets from each user
tmls <- get_timelines(users, n = 100)

## pass the returned data to botornot()
data <- botornot(tmls)

## arrange by prob ests
data[order(data$prob_bot), ]
#> # A tibble: 11 x 2
#>    user            prob_bot
#>    <chr>              <dbl>
#>  1 hadleywickham    0.00290
#>  2 realDonaldTrump  0.00753
#>  3 kearneymw        0.0171 
#>  4 ma_salmon        0.0582 
#>  5 juliasilge       0.0852 
#>  6 hrbrmstr         0.0858 
#>  7 dataandme        0.174  
#>  8 netflix_bot      0.974  
#>  9 tidyversetweets  0.997  
#> 10 mothgenerator    0.999  
#> 11 American__Voter  0.999

fast = TRUE

The default [gradient boosted] model uses both users-level (bio, location, number of followers and friends, etc.) and tweets-level (number of hashtags, mentions, capital letters, etc. in a user's most recent 100 tweets) data to estimate the probability that users are bots. For larger data sets, this method can be quite slow. Due to Twitter's REST API rate limits, users are limited to only 180 estimates per every 15 minutes.

To maximize the number of estimates per 15 minutes (at the cost of being less accurate), use the fast = TRUE argument. This method uses only users-level data, which increases the maximum number of estimates per 15 minutes to 90,000! Due to losses in accuracy, this method should be used with caution!

## get botornot estimates
data <- botornot(users, fast = TRUE)

## arrange by prob ests
data[order(data$prob_bot), ]
#> # A tibble: 11 x 2
#>    user            prob_bot
#>    <chr>              <dbl>
#>  1 hadleywickham    0.00316
#>  2 kearneymw        0.0333 
#>  3 ma_salmon        0.0385 
#>  4 hrbrmstr         0.0478 
#>  5 juliasilge       0.0640 
#>  6 dataandme        0.245  
#>  7 realDonaldTrump  0.380  
#>  8 netflix_bot      0.986  
#>  9 tidyversetweets  0.997  
#> 10 American__Voter  0.998  
#> 11 mothgenerator    0.999