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background_scrape.R
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background_scrape.R
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#-----------------------------------------------------------------------------
# load packages
#-----------------------------------------------------------------------------
cat("\n=== running setup ===\n")
library(RCurl)
library(rtweet)
library(tweetscores)
library(knitr)
library(markdown)
library(rmarkdown)
library(rAltmetric)
library(rvest)
library(rcrossref)
library(crevents)
library(tidyverse)
library(yaml)
library(anytime)
library(here)
library(jsonlite)
library(stringr)
library(text2vec)
library(tidytext)
library(tm)
library(umap)
library(httr)
#-----------------------------------------------------------------------------
# Setup options
#-----------------------------------------------------------------------------
# load history from crashed session
# loadhistory("~/.rstudio/history_database")
options(timeout = 600)
# set relative path for calling external files from within this script
# datadir <- dirname(sys.frame(1)$ofile)
datadir <- paste0(getwd(), "/audiences")
keys <- yaml.load_file(paste0(getwd(), "/_bots/battlestar_remix/_config.yaml"))
# load history from crashed session
# loadhistory("~/.rstudio/history_database")
# oauth for tweetscores functions
# cat("setting up twitter oauth...")
token <- rtweet::create_token(app="testapp",
consumer_key=keys$api_key,
consumer_secret=keys$api_secret,
access_token=keys$access_token,
access_secret=keys$access_token_secret)
my_oauth <- list(consumer_key=keys$api_key,
consumer_secret=keys$api_secret,
access_token=keys$access_token,
access_token_secret=keys$access_token_secret)
hf_path <- "/mnt/alduin/follower_data"
# group of DOIs to analyze
# doi_group <- "select"
doi_group <- "biorxiv"
# doi_group <- "nature"
outdir <- paste0(datadir, "/content/reports")
dir.create(outdir, recursive=TRUE, showWarnings = FALSE)
cat("done\n")
#-----------------------------------------------------------------------------
# get bios of up to 10k followers
#-----------------------------------------------------------------------------
# testing with timelines instead of bios
# tmls <- get_timelines(plotdat$account[1:4], n = 3200)
get_follower_bios <- function(followers, token){
f5k <- unlist(followers)
f5k <- head(f5k, 10000)
lookup_users(f5k, token=token)
}
#-----------------------------------------------------------------------------
# get list of follower files from generated reports
#-----------------------------------------------------------------------------
update_follower_files <- function(){
follower_files_details <- file.info(
paste0(datadir, "/article_data/",
list.files(path=paste0(datadir, "/article_data"),
pattern="altmetric_data_")))
follower_files_details <- rownames_to_column(follower_files_details) %>%
arrange(desc(mtime))
chk_files <- follower_files_details$rowname
# return only files that haven't been added before
return(chk_files)
}
get_follower_cache <- function(high_followers, files_to_scan, files_scanned){
# get only new files
follower_files <- files_to_scan[!files_to_scan %in% files_scanned]
if (length(follower_files) > 0) {
high_followers_new <- follower_files %>%
map_dfr(readRDS) %>%
rowwise() %>%
mutate(nfollowers = length(followers)) %>%
arrange(desc(nfollowers)) %>%
dplyr::filter(nfollowers>100)
} else {
high_followers_new <- tibble(account=character(), followers=list())
}
cat(paste0("loaded data for ", nrow(high_followers_new), " additional users\n"))
high_followers_out <- bind_rows(high_followers, high_followers_new) %>%
rowwise() %>%
mutate(nfollowers = length(followers)) %>%
group_by(account) %>%
arrange(desc(nfollowers)) %>%
slice(1L)
cat(paste0("High-follower cache updated: ", nrow(high_followers_out), " total users\n"))
return(high_followers_out)
}
#-----------------------------------------------------------------------------
# if crossref data not found, scrape from Altmetric with rvest
#-----------------------------------------------------------------------------
events_from_altmetric <- function(article_full_url){
summary_page <- read_html(article_full_url)
twitter_url <- paste0(article_full_url, "/twitter")
twitter_page <- read_html(twitter_url)
# number of pages is the ceiling of total tweets/100
totals <- twitter_page %>% html_nodes("div.text strong") %>% html_text()
npages <- ceiling(as.integer(totals[1])/100)
# loop through pages of tweets on altmetric to get handles of tweeting users
events <- data.frame()
for(page in 1:npages){
# url <- paste0(article_base_url, id, "/twitter/page:", page)
page_url <- paste0(twitter_url, "/page:", page)
page <- read_html(page_url)
names <- gsub("@", "", html_nodes(page, "div.handle") %>% html_text())
status <- gsub(".*tweet_id=", "", html_nodes(page, "a.favorite") %>%
html_attr("href"))
timestamps <- html_nodes(page, "time") %>% html_attrs() %>% unlist()
events <- bind_rows(events, data.frame(names, timestamps, status))
}
return(events)
}
#-----------------------------------------------------------------------------
# scrape follower info (or load from cached data) and cache to disk
#
# due to Twitter API limits, this will take at least N minutes,
# where N is the number of unique users that have tweeted about an article
#
# users with >5,000 followers will require multiple API calls to scrape their
# full follower lists, so a user with 75,000 followers will take
# the same amount of time to process as 15 users with <5,000 followers each
# (~15 minutes)
#-----------------------------------------------------------------------------
compile_follower_data <- function(datadir, article_id, user_data){
# load cached full data (for data collected before chunk caching implemented)
follower_lists_full_fh <- paste0(datadir,
"/article_data/altmetric_data_full_", article_id, ".rds")
if(file.exists(follower_lists_full_fh)){
follower_lists_full <- readRDS(follower_lists_full_fh)
} else {
# load cached chunks
chunk_files <- file.info(
list.files(path=paste0(datadir, "/article_data"),
pattern=paste0("altmetric_data_[0-9]+_", article_id),
full.names = TRUE))
chunk_files <- rownames_to_column(chunk_files) %>%
arrange(mtime)
files <- chunk_files$rowname
if(length(files) != 0){
follower_lists_cache <- files %>%
map_dfr(readRDS)
cat(paste0("loaded data for ", length(unique(follower_lists_cache$account)), " users\n"))
} else {
follower_lists_cache <- tibble(account=character(), followers=list())
}
follower_lists_full <- tibble(account=character(), followers=list())
# scrape new data if out of date or partially complete
if(nrow(follower_lists_cache)/nrow(user_data)<0.9){
# get follower metadata from Twitter API
# sleep interval--if more than 15 API calls will be required,
# use one call per minute to minimize weird timeout issues
fc_mod <- ceiling(user_data$followers_count/5000)
sleep <- ifelse(sum(fc_mod)>15, 60, 0)
follower_list_sub <- tibble(account=character(), followers=list())
i <- 1
j <- 1
for(user in unique(user_data$screen_name)){
cat(paste0(user, " (", i , "/", nrow(user_data), ")..."))
# pull from cache if user exists in high-follower database
if(user %in% hf_handles){
cat("cached in high-follower list \n")
# i <- i+1
# follower_list_user <- tibble(account=user, followers=list())
# next
} else if (user %in% follower_lists_cache$account) {
cat("cached in previous scrape \n")
follower_list_user <- follower_lists_cache %>%
dplyr::filter(account==user)
follower_list_sub <- bind_rows(list(follower_list_sub, follower_list_user))
follower_lists_full <- bind_rows(list(follower_lists_full, follower_list_user))
} else {
cat("new\n")
if(j %% 2 == 1){
use_oauth = my_oauth
} else {
use_oauth = my_oauth
}
follower_list_user <- user_data %>%
dplyr::filter(screen_name == user) %>%
dplyr::select(account = screen_name) %>% #head
mutate(followers = getFollowers(screen_name = account,
# oauth = my_oauth,
oauth = use_oauth,
sleep = 60) %>%
data.frame %>%
as.list)
j <- j+1
follower_list_sub <- bind_rows(list(follower_list_sub, follower_list_user))
follower_lists_full <- bind_rows(list(follower_lists_full, follower_list_user))
}
# cache to disk every 10 users
if(i %% 50 == 0 | i==nrow(user_data)){
cat("caching to disk\n")
follower_list_sub_fh <- paste0(datadir,
"/article_data/altmetric_data_",
as.character(i), "_", article_id, ".rds")
saveRDS(follower_list_sub, follower_list_sub_fh)
follower_list_sub <- tibble(account=character(), followers=list())
}
i <- i+1
}
} else {
follower_lists_full <- follower_lists_cache
}
}
return(follower_lists_full)
}
#-----------------------------------------------------------------------------
# scrape and cache follower bios
#-----------------------------------------------------------------------------
compile_follower_bios <- function(datadir, article_id, follower_lists_full){
follower_bios_fh <- paste0(datadir, "/article_data/follower_bios_", article_id, ".rds")
if(file.exists(follower_bios_fh)){
out_df <- readRDS(follower_bios_fh)
} else {
# load cached chunks
chunk_files <- file.info(
list.files(path=paste0(datadir, "/article_data"),
pattern=paste0("follower_bios_[0-9]+_", article_id),
full.names = TRUE))
chunk_files <- rownames_to_column(chunk_files) %>%
arrange(mtime)
files <- chunk_files$rowname
if(length(files) != 0){
out_df <- files %>%
map_dfr(readRDS)
cat(paste0("loaded follower bios for ", length(unique(out_df$account)), " users\n"))
} else {
out_df <- tibble(account=character(), bios=character())
}
# skip individual bio checks if we have follower bios for at least 90% of users
tot_users <- length(unique(follower_lists_full$account))
if(length(unique(out_df$account))/tot_users < 0.95){
# if document term matrix has been generated & cached from a previous run
# and data for new users will be added, remove the file
bios_dtm_fh <- paste0(datadir, "/article_data/bios_dtm_", article_id, ".rds")
if(file.exists(bios_dtm_fh)){
file.remove(bios_dtm_fh)
cat("removed cached document term matrix file\n")
}
out_df_sub <- tibble(account=character(), bios=character())
i <- 1
j <- 1
for(user in unique(follower_lists_full$account)){
cat(paste0(user, " (", i , "/", tot_users, ")..."))
if(user %in% out_df$account){
cat("cached in previous scrape \n")
acct_follower_bios <- out_df %>%
dplyr::filter(account==user) #%>%
# distinct(account, .keep_all = T)
out_df_sub <- bind_rows(list(out_df_sub, acct_follower_bios))
} else {
cat("new \n")
if(j %% 2 == 1){
token <- token
} else {
token <- token
}
test_user <- follower_lists_full %>%
dplyr::filter(account==user) %>%
distinct(account, .keep_all = T)
if(length(unlist(test_user$followers)) > 5){
bios <- try(get_follower_bios(followers=test_user$followers, token=token))
# check that bios df has at least 80 columns
if(!inherits(bios, "try-error") & length(unique(names(bios))) > 80){
acct_follower_bios <- data.frame(account=test_user$account, bios)
out_df_sub <- bind_rows(list(out_df_sub, acct_follower_bios))
out_df <- bind_rows(list(out_df, acct_follower_bios))
} else {
cat(paste0("Encountered error for user—results will not be included\n"))
}
j <- j+1
Sys.sleep(5)
}
}
# out_df_sub <- bind_rows(list(out_df_sub, acct_follower_bios))
# cache to disk every 50 users
if(i %% 50 == 0 | i == tot_users){
cat("caching to disk\n")
follower_bios_sub_fh <- paste0(datadir,
"/article_data/follower_bios_",
as.character(i), "_", article_id, ".rds")
saveRDS(out_df_sub, follower_bios_sub_fh)
out_df_sub <- tibble(account=character(), bios=character())
}
i <- i+1
}
}
}
return(out_df)
}
#-----------------------------------------------------------------------------
# Read list of article DOIs and Altmetric URLs
# - in the future, this will be purely DOI-based
# - can also pull in list of popular bioRxiv papers using the Rxivist API
#-----------------------------------------------------------------------------
check_file <- FALSE
report <- TRUE
skip <- 326
# list of DOIs to skip because they lack enough tweets or are missing metadata
banned_dois <- c("10.1101/397067", "10.1101/501494", "10.1101/066803") #?)
if (doi_group=="select") {
dois <- scan(paste0(datadir, "/papers.txt"), what="", sep="\n")
for (doi in dois) {
run_report(doi, outdir, check_file)
}
} else if (doi_group=="biorxiv") {
cat(paste0("\n=== scraping top preprints from rxivist ===\n"))
# old version--scrape using rvest
rvest_scrape <- FALSE
if (rvest_scrape) {
cat("(rvest)...")
page_url <- paste0("https://rxivist.org/?q=&metric=twitter&category=", category,
"&timeframe=alltime&page_size=20&view=standard")
page <- read_html(page_url)
dois_df <- html_nodes(page, ".btn-sm.btn-altcolor") %>%
html_attr("href") %>%
data.frame() %>%
dplyr::rename(doi=".") %>%
dplyr::filter(grepl("doi", doi)) %>%
dplyr::mutate(doi=gsub("https://doi.org/", "", doi))
} else {
# new version--scrape using rxivist API
cat("(API)...")
api_url1 <- "https://api.rxivist.org/v1/papers?metric=twitter&page_size=250&timeframe=alltime"
api_url2 <- "https://api.rxivist.org/v1/papers?metric=twitter&page_size=250&timeframe=alltime&page=1"
api_url3 <- "https://api.rxivist.org/v1/papers?metric=twitter&page_size=250&timeframe=alltime&page=2"
api_url4 <- "https://api.rxivist.org/v1/papers?metric=twitter&page_size=250&timeframe=alltime&page=3"
cat_json1 <- fromJSON(api_url1)
cat_json2 <- fromJSON(api_url2)
cat_json3 <- fromJSON(api_url3)
cat_json4 <- fromJSON(api_url4)
dois_df <- bind_rows(data.frame(cat_json1$results),
data.frame(cat_json2$results),
data.frame(cat_json3$results),
data.frame(cat_json4$results)) %>%
dplyr::filter(metric>=80 & metric <1500) %>% as_tibble() %>%
dplyr::filter(!doi %in% banned_dois) %>%
arrange(metric)
}
dois <- dois_df$doi
cat("done\n")
for (i in (1+skip):nrow(dois_df)) {
hf_handles_fh <- paste0(datadir, "/training_data/high_follower_handles.rds")
hf_handles <- readRDS(hf_handles_fh)
paper_it <- dois_df[i,]
doi_it <- paper_it$doi
doi <- doi_it
category <- paper_it$category
cat("\n=== caching high-follower accounts ===\n")
files_to_scan <- update_follower_files()
# high_followers <- get_follower_cache(high_followers, files_to_scan, files_scanned)
files_scanned <- files_to_scan
cat(paste0("done. ", length(files_scanned), " additional files scanned for high-follower users.\n"))
cat(paste0("\n=== Scraping data for '", paper_it$title, "' (", doi_it, ") ===\n"))
cat("\n=== getting altmetric metadata ===\n")
article_am <- altmetrics(doi = doi)
article_df <- altmetric_data(article_am)
article_id <- article_df$altmetric_id
cat("done\n")
cat("\n=== getting paper metadata from crossref ===\n")
cr_data <- cr_works(doi = doi)$data
title <- gsub("\"|/|:", "", cr_data$title)
nb_prefix <- paste0(gsub(" ", "_", title), "_", article_id)
nb_file <- paste0(nb_prefix, ".html")
nb_title <- paste0(title, ", ",
article_df$journal, ", ", cr_data$created)
cat("done\n")
if(file.exists(paste0(outdir, "/", nb_file)) & check_file){
cat(paste0("report for ", nb_file, " already exists\n"))
next
}
cat("\n=== querying crossref event data ===\n")
cr_event_url <- paste0("https://api.eventdata.crossref.org/v1/",
"events?source=twitter&rows=10000&from-occurred-date=2010-10-01&obj-id=", doi)
req <- httr::RETRY("GET", cr_event_url, pause_min=0.1, pause_base=0.1, pause_cap=3, times=60, httr::timeout(3))
stop_for_status(req)
warn_for_status(req)
message_for_status(req)
e2 <- fromJSON(jsonlite::prettify(rawToChar(req$content)), flatten=TRUE)
status <- e2$status
events <- e2$message$events %>%
data.frame() %>%
dplyr::select(names = subj.author.url,
timestamps = timestamp,
status = subj_id) %>%
mutate(names=gsub("http://www.twitter.com/", "", names),
status=gsub("http://twitter.com/.*statuses/", "", status)) %>%
mutate(names=gsub("twitter://user\\?screen_name=", "", names),
status=gsub("twitter://status\\?id=", "", status))
handles <- unique(events$names)
cat("done\n")
# For papers where Crossref is missing >50% of tweets, fall back to
# scraping from Altmetric
# if(length(handles)/as.numeric(article_df$cited_by_tweeters_count) < 0.5){
#
# Sys.sleep(5)
#
# cat("\n=== crossref and altmetric data do not match—scraping from altmetric ===\n")
# events <- events_from_altmetric(article_full_url)
# handles <- unique(events$names)
# cat("done\n")
# }
cat("\n=== getting user and event metadata from twitter API ===\n")
skip_langs <- c("ar", "ja", "zh-CN", "ko")
user_data <- lookup_users(handles) %>%
dplyr::filter(!(account_lang %in% skip_langs) & protected==FALSE)
cat("done\n")
cat("\n=== getting follower lists for users ===\n")
follower_lists_full <- compile_follower_data(datadir,
article_id,
user_data)
cat("done\n")
cat("\n=== getting follower bios ===\n")
follower_bios_full <- compile_follower_bios(datadir,
article_id,
follower_lists_full)
cat("done\n")
}
}