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Twitter analysis of Disney+'s announcement for the TV series and movies they will stream
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disneyplus_search-ids.rds

README.md

Overview

Disney recently announced its new streaming service Disney+. On Twitter, their account tweeted out a very long list of movies and TV series that they planned on releasing.

It. Is. Time. From Snow White and the Seven Dwarfs to The Mandalorian, check out basically everything coming to \#DisneyPlus in the U.S. on November 12.

Pre-order in the U.S. at https://t.co/wJig4STf4P today: https://t.co/tlWvp23gLF pic.twitter.com/0q3PTuaDWT

— Disney+ (@disneyplus) October 14, 2019
My inner child was now curious as to which movies and TV series were most popular, as "voted" by the twitterverse. And here we are.

Last updated: 2019-10-22

Setup

# Setup environment
library(tidyverse)
library(lubridate)
library(ggrepel)
library(ggthemes)
library(rtweet)

Query data

# Get recent tweets
# Note, this is the original tweet with a number of thread replies 2019-10-14
# https://twitter.com/disneyplus/status/1183715553057239040
rt <- get_timeline("disneyplus", n = 1000)
saveRDS(rt, "disneyplus_search.rds")
saveRDS(rt$status_id, "disneyplus_search-ids.rds")

But instead, here I'll just look up the status IDs.

ids_file <- "disneyplus_search-ids.rds"
disneyplus_file <- "disneyplus_search.rds"

# Read in search directly if exists
if (file.exists(disneyplus_file)) {
  rt <- readRDS(disneyplus_file)
} else {
  # Download status IDs file
  download.file(
    "https://github.com/erictleung/disneyplus-twitter-analysis/blob/master/data/disneyplus_search-ids.rds?raw=true",
    ids_file
  )
  # Read status IDs from downloaded file
  ids <- readRDS(ids_file)

  # Lookup data associated with status ids
  rt <- rtweet::lookup_tweets(ids)
}

Process and clean data

Things to do:

  • Select number of likes and number of retweets
  • Focus on just the movies after the initial announcement
  • Extract the movie name and the year of the announcement
rt_clean <-
    rt %>%
    
    # Create subset of useful columns
    select(created_at,
           text,
           favorite_count,
           retweet_count) %>%

    # Focus on tweets after the annoucement
    filter(created_at > ymd_hms("2019-10-14 12:05:54")) %>%
    
    # Extract movie name
    mutate(movie_name = str_match(text, "\\b.* \\(")) %>%
    mutate(movie_name = str_remove(movie_name, " \\($")) %>%
    
    # Fix some odd characters
    mutate(movie_name = str_replace(movie_name, "&amp;", "&")) %>%

    # Extract movie year, being four numbers between parentheses
    mutate(movie_year = str_match(text, "\\([0-9]{4}\\)")) %>%
    mutate(movie_year = str_match(movie_year, "[0-9]{4}")) %>%
    
    # Identify decade of movie
    # Modified from: https://stackoverflow.com/a/48966643/
    mutate(movie_year = as.integer(movie_year)) %>%
    mutate(movie_decade = movie_year - movie_year %% 10) %>%
    mutate(movie_decade = as.factor(movie_decade)) %>%
    
    # Ratio betwen retweet counts and favorites
    mutate(ratio_like_to_rt = favorite_count / retweet_count) %>%

    # We can remove the original text, as we've extracted the useful info
    select(-text) %>%

    # Filter for rows that have movie name and year
    filter(!is.na(movie_year) | !is.na(movie_name)) %>%

    # Add row numbers for rank analyses
    arrange(created_at) %>%
    mutate(id = row_number()) %>%
    select(id, everything())

Basic statistics

# Number of titles
rt_clean %>% nrow()
## [1] 629

# Titles per decade
rt_clean %>% select(movie_decade) %>% table()
## .
## 1930 1940 1950 1960 1970 1980 1990 2000 2010 
##    1   12   22   23   31   35  109  207  189

Plot rate over time

rt_clean %>%
    ggplot(aes(x = created_at, y = id)) +
    geom_point() +
    geom_smooth(method = "loess", color = "grey", se = FALSE) +
    ggtitle(label = "Disney+ movies and series by tweet order and time",
            subtitle = "Fit by LOESS") +
    xlab("Time tweeted") +
    ylab("Tweet order") +
    labs(caption = "\nSource: Data gathered via Twitter's standard `search/tweets` API using rtweet") +
    theme_clean()

It appears that the Twitter account tweeted out in bursts and in regular intervals.

Plot likes versus retweets

rt_clean %>%
    ggplot(aes(x = favorite_count, y = retweet_count)) +
    geom_point(alpha = 0.6) +
    geom_vline(xintercept = 10000, color = "grey") +
    geom_smooth(method = "loess", color = "grey", se = FALSE) +
    geom_label_repel(data = subset(rt_clean, favorite_count > 10000), aes(label = movie_name)) +
    ggtitle(label = "Disney+ movies and series by\ntweet favorite and retweet counts",
            subtitle = "Labels shown for titles with >10k favorites and fit by LOESS") +
    xlab("Favorite count") +
    ylab("Retweet count") +
    labs(caption = "\nSource: Data gathered via Twitter's standard `search/tweets` API using rtweet") +
    theme_clean()

Let's facet this!

rt_clean %>%
    ggplot(aes(x = favorite_count, y = retweet_count, color = movie_decade)) +
    geom_point(alpha = 0.6) +
    geom_smooth(method = "loess", color = "grey", se = FALSE) +
    facet_wrap(~ movie_decade) +
    ggtitle(label = "Disney+ movies and series by tweet favorite and retweet counts",
            subtitle = "Trends fit by LOESS and facetted by decade series/movie was released") +
    xlab("Favorite count") +
    ylab("Retweet count") +
    labs(caption = "\nSource: Data gathered via Twitter's standard `search/tweets` API using rtweet") +
    theme_clean() +
    theme(legend.position = "none")

Timeline of release date with likes

rt_clean %>%
    ggplot(aes(x = movie_year, y = favorite_count)) +
    geom_point(alpha = 0.6) +
    geom_label_repel(data = subset(rt_clean, favorite_count > 10000), aes(label = movie_name)) +
    ggtitle(label = "Disney+ movies and series by tweet favorite counts",
            subtitle = "Labels shown for titles with >10k favorites") +
    xlab("Movie or TV series release year") +
    ylab("Favorite count") +
    labs(caption = "\nSource: Data gathered via Twitter's standard `search/tweets` API using rtweet") +
    theme_clean()

Timeline of release date with retweets

rt_clean %>%
    ggplot(aes(x = movie_year, y = retweet_count)) +
    geom_point(alpha = 0.6) +
    geom_label_repel(data = subset(rt_clean, retweet_count > 1500), aes(label = movie_name)) +
    ggtitle(label = "Disney+ movies and series by tweet retweet counts",
            subtitle = "Labels shown for titles with >1.5k retweets") +
    xlab("Movie or TV series release year") +
    ylab("Retweet count") +
    labs(caption = "\nSource: Data gathered via Twitter's standard `search/tweets` API using rtweet") +
    theme_clean()

Timeline of release data with ratio between favorite and retweet count

rt_clean %>%
    ggplot(aes(x = movie_year, y = ratio_like_to_rt, color = movie_decade)) +
    geom_boxplot() +
    geom_point(alpha = 0.3) +
    ggtitle(label = "Disney+ movies and series by ratio likeness",
            subtitle = "Ratio likeness is ratio of favorite count to retweet count") +
    xlab("Movie or TV series release year") +
    ylab("Ratio likeness") +
    labs(caption = "\nSource: Data gathered via Twitter's standard `search/tweets` API using rtweet") +
    theme_clean() +
    theme(legend.position = "none")

Plot of rank tweet and likes

rt_clean %>%
    ggplot(aes(x = id, y = favorite_count)) +
    geom_point(alpha = 0.6) +
    geom_label_repel(data = subset(rt_clean, favorite_count > 10000), aes(label = movie_name)) +
    ggtitle(label = "Disney+ movies and series by tweet order and favorites",
            subtitle = "Order sorted earliest to latest tweet time, labels with >10k likes") +
    xlab("Tweet order") +
    ylab("Favorite counts") +
    labs(caption = "\nSource: Data gathered via Twitter's standard `search/tweets` API using rtweet") +
    theme_clean()

Plot of rank tweet and retweets

rt_clean %>%
    ggplot(aes(x = id, y = retweet_count)) +
    geom_point(alpha = 0.6) +
    geom_label_repel(data = subset(rt_clean, retweet_count > 2000), aes(label = movie_name)) +
    ggtitle(label = "Disney+ movies and series by tweet order and retweets",
            subtitle = "Order sorted earliest to latest tweet time, labels with >5k retweets") +
    xlab("Tweet order") +
    ylab("Retweet counts") +
    labs(caption = "\nSource: Data gathered via Twitter's standard `search/tweets` API using rtweet") +
    theme_clean()

What decade of movies are the most popular by likes?

top_favorite_by_decade <-
    rt_clean %>%
    group_by(movie_decade) %>%
    arrange(favorite_count) %>%
    top_n(n = 1, wt = favorite_count) %>%
    ungroup()

rt_clean %>%
    ggplot(aes(x = movie_year, y = favorite_count, color = movie_decade)) +
    geom_boxplot() +
    geom_point(alpha = 0.3) +
    geom_label_repel(data = top_favorite_by_decade, aes(label = movie_name)) +
    ggtitle(label = "Disney+ movies and series by decade and favorite count",
            subtitle = "Color represents decade, labeled titles are top favorite for each decade") +
    xlab("Movie or TV series release year") +
    ylab("Favorite count") +
    labs(caption = "\nSource: Data gathered via Twitter's standard `search/tweets` API using rtweet") +
    theme_clean() +
    theme(legend.position = "none")

bottom_favorite_by_decade <-
    rt_clean %>%
    group_by(movie_decade) %>%
    arrange(desc(favorite_count)) %>%
    top_n(n = -1, wt = favorite_count) %>%
    ungroup()

rt_clean %>%
    ggplot(aes(x = movie_year, y = favorite_count, color = movie_decade)) +
    geom_boxplot() +
    geom_point(alpha = 0.3) +
    scale_y_log10() +
    geom_label_repel(data = bottom_favorite_by_decade, aes(label = movie_name)) +
    ggtitle(label = "Disney+ movies and series by decade and favorite count",
            subtitle = "Color represents decade, labeled titles are least favorite for each decade") +
    xlab("Movie or TV series release year") +
    ylab("Favorite count") +
    labs(caption = "\nSource: Data gathered via Twitter's standard `search/tweets` API using rtweet") +
    theme_clean() +
    theme(legend.position = "none")

Both?

favorites_by_decade <- dplyr::union(top_favorite_by_decade, bottom_favorite_by_decade)

rt_clean %>%
    ggplot(aes(x = movie_year, y = favorite_count, color = movie_decade)) +
    geom_boxplot() +
    geom_point(alpha = 0.3) +
    coord_trans(y = "log10") +
    geom_label_repel(data = favorites_by_decade, aes(label = movie_name)) +
    ggtitle(label = "Disney+ movies and series by decade and favorite count",
            subtitle = "Color = decade, labeled titles are most and least favorite per decade, y-axis log10 scaled") +
    xlab("Movie or TV series release year") +
    ylab("Favorite count") +
    labs(caption = "\nSource: Data gathered via Twitter's standard `search/tweets` API using rtweet") +
    theme_clean() +
    theme(legend.position = "none")

What decade of movies are the most popular by retweets?

top_retweet_by_decade <-
    rt_clean %>%
    group_by(movie_decade) %>%
    arrange(retweet_count) %>%
    top_n(n = 1, wt = retweet_count) %>%
    ungroup()

rt_clean %>%
    ggplot(aes(x = movie_year, y = retweet_count, color = movie_decade)) +
    geom_boxplot() +
    geom_point(alpha = 0.3) +
    geom_label_repel(data = top_retweet_by_decade, aes(label = movie_name)) +
    ggtitle(label = "Disney+ movies and series by decade and retweet count",
            subtitle = "Color represents decade, labeled titles are top retweet for each decade") +
    xlab("Movie or TV series release year") +
    ylab("Retweet count") +
    labs(caption = "\nSource: Data gathered via Twitter's standard `search/tweets` API using rtweet") +
    theme_clean() +
    theme(legend.position = "none")

bottom_retweet_by_decade <-
    rt_clean %>%
    group_by(movie_decade) %>%
    arrange(retweet_count) %>%
    top_n(n = -1, wt = retweet_count) %>%
    ungroup()

rt_clean %>%
    ggplot(aes(x = movie_year, y = retweet_count, color = movie_decade)) +
    geom_boxplot() +
    geom_point(alpha = 0.3) +
    scale_y_log10() +
    geom_label_repel(data = bottom_retweet_by_decade, aes(label = movie_name)) +
    ggtitle(label = "Disney+ movies and series by decade and retweet count",
            subtitle = "Color represents decade, labeled titles are least retweeted for each decade") +
    xlab("Movie or TV series release year") +
    ylab("Retweet count") +
    labs(caption = "\nSource: Data gathered via Twitter's standard `search/tweets` API using rtweet") +
    theme_clean() +
    theme(legend.position = "none")

Both?

retweets_by_decade <- dplyr::union(top_retweet_by_decade, bottom_retweet_by_decade)

rt_clean %>%
    ggplot(aes(x = movie_year, y = retweet_count, color = movie_decade)) +
    geom_boxplot() +
    geom_point(alpha = 0.3) +
    coord_trans(y = "log10") +
    geom_label_repel(data = retweets_by_decade, aes(label = movie_name)) +
    ggtitle(label = "Disney+ movies and series by decade and retweet count",
            subtitle = "Color = decade, labeled titles are most and least retweet per decade, y-axis log10 scaled") +
    xlab("Movie or TV series release year") +
    ylab("Retweet count") +
    labs(caption = "\nSource: Data gathered via Twitter's standard `search/tweets` API using rtweet") +
    theme_clean() +
    theme(legend.position = "none")

Pixar movie popularity

# Copied from https://en.wikipedia.org/wiki/List_of_Pixar_films
pixar <- c(
    "Toy Story",
    "A Bug's Life",
    "Toy Story 2",
    "Monsters, Inc.",
    "Finding Nemo",
    "The Incredibles",
    "Cars",
    "Ratatouille",
    "WALL•E",
    "Up",
    "Toy Story 3",
    "Cars 2",
    "Brave",
    "Monsters University",
    "Inside Out",
    "The Good Dinosaur",
    "Finding Dory",
    "Cars 3",
    "Coco",
    "Incredibles 2",
    "Toy Story 4"
)

rt_clean %>%
    filter(movie_name %in% pixar) %>%
    ggplot(aes(x = favorite_count, y = retweet_count, color = movie_decade, label = movie_name)) +
    geom_point() +
    geom_smooth(method = "loess", color = "grey", se = FALSE) +
    geom_label_repel() +
    ggtitle(label = "Pixar movies announced to stream on Disney+\nby tweet favorite and retweet counts",
            subtitle = "Points labeled by movie decade, fit by LOESS") +
    xlab("Favorite count") +
    ylab("Retweet count") +
    labs(caption = "\nSource: Data gathered via Twitter's standard `search/tweets` API using rtweet") +
    theme_clean() +
    scale_color_discrete(name = "Decade") +
    theme(legend.position = "bottom")

It appears the following movies weren't in the main list of announcements:

  • Up
  • Coco
  • Incredibles 2
  • Toy Story 4

Session information

sessionInfo()
## R version 3.6.1 (2019-07-05)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: OS X El Capitan 10.11.6
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] rtweet_0.6.9    ggthemes_4.2.0  ggrepel_0.8.1   lubridate_1.7.4
##  [5] forcats_0.4.0   stringr_1.4.0   dplyr_0.8.3     purrr_0.3.3    
##  [9] readr_1.3.1     tidyr_1.0.0     tibble_2.1.3    ggplot2_3.2.1  
## [13] tidyverse_1.2.1
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_0.2.5 xfun_0.10        haven_2.1.1      lattice_0.20-38 
##  [5] colorspace_1.4-1 vctrs_0.2.0      generics_0.0.2   htmltools_0.4.0 
##  [9] yaml_2.2.0       rlang_0.4.0      pillar_1.4.2     glue_1.3.1      
## [13] withr_2.1.2      modelr_0.1.5     readxl_1.3.1     lifecycle_0.1.0 
## [17] munsell_0.5.0    gtable_0.3.0     cellranger_1.1.0 rvest_0.3.4     
## [21] evaluate_0.14    labeling_0.3     knitr_1.25       broom_0.5.2     
## [25] Rcpp_1.0.2       scales_1.0.0     backports_1.1.5  jsonlite_1.6    
## [29] hms_0.5.1        digest_0.6.21    stringi_1.4.3    grid_3.6.1      
## [33] cli_1.1.0        tools_3.6.1      magrittr_1.5     lazyeval_0.2.2  
## [37] crayon_1.3.4     pkgconfig_2.0.3  zeallot_0.1.0    xml2_1.2.2      
## [41] assertthat_0.2.1 rmarkdown_1.16   httr_1.4.1       rstudioapi_0.10 
## [45] R6_2.4.0         nlme_3.1-141     compiler_3.6.1
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