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Get tweets from the Academic Research product track.
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# start_time: %Y-%m-%dT%H:%M:%SZ | |
# end_time: %Y-%m-%dT%H:%M:%SZ | |
# needs jsonlite and httr | |
# next_token can be obtained from meta$next_token to paginate through results | |
get_tweets <- function(q="",n=10,start_time,end_time,token,next_token=""){ | |
if(n>500){ | |
warning("n too big. Using 500 instead") | |
n <- 500 | |
} | |
if(n<5){ | |
warning("n too small Using 10 instead") | |
n <- 500 | |
} | |
if(missing(token)){ | |
stop("bearer token must be specified.") | |
} | |
if(missing(end_time)){ | |
end_time <- gsub(" ","T",paste0(as.character(Sys.time()),"Z")) | |
} | |
if(missing(start_time)){ | |
start_time <- paste0(Sys.Date(),"T00:00:00Z") | |
} | |
if(substr(token,1,7)=="Bearer "){ | |
bearer <- token | |
} else{ | |
bearer <- paste0("Bearer ",token) | |
} | |
#endpoint | |
url <- "https://api.twitter.com/2/tweets/search/all" | |
#parameters | |
params = list( | |
"query" = q, | |
"max_results" = n, | |
"start_time" = start_time, | |
"end_time" = end_time, | |
"tweet.fields" = "attachments,author_id,context_annotations,conversation_id,created_at,entities,geo,id,in_reply_to_user_id,lang,public_metrics,possibly_sensitive,referenced_tweets,source,text,withheld", | |
"user.fields" = "created_at,description,entities,id,location,name,pinned_tweet_id,profile_image_url,protected,public_metrics,url,username,verified,withheld", | |
"expansions" = "author_id,entities.mentions.username,geo.place_id,in_reply_to_user_id,referenced_tweets.id,referenced_tweets.id.author_id", | |
"place.fields" = "contained_within,country,country_code,full_name,geo,id,name,place_type" | |
) | |
if(next_token!=""){ | |
params[["next_token"]] <- next_token | |
} | |
r <- httr::GET(url,httr::add_headers(Authorization = bearer),query=params) | |
#fix random 503 errors | |
count <- 0 | |
while(httr::status_code(r)==503 & count<4){ | |
r <- httr::GET(url,httr::add_headers(Authorization = bearer),query=params) | |
count <- count+1 | |
Sys.sleep(count*5) | |
} | |
if(httr::status_code(r)!=200){ | |
stop(paste("something went wrong. Status code:", httr::status_code(r))) | |
} | |
if(httr::headers(r)$`x-rate-limit-remaining`=="1"){ | |
warning(paste("x-rate-limit-remaining=1. Resets at",as.POSIXct(as.numeric(httr::headers(r)$`x-rate-limit-reset`), origin="1970-01-01"))) | |
} | |
dat <- jsonlite::fromJSON(httr::content(r, "text")) | |
dat | |
} |
Thank you very much for this!
Hello!
Could you provide a simple example on how to scrape some tweets? I have been trying to apply the function myself to obtain a simple sample of tweets but I can't really figure out what I am doing wrong.
Thanks a lot in advance!
@luisignaciomenendez This code has been turned into a package: https://github.com/cjbarrie/academictwitteR
This should be easier to use than the code above
Having a bit of trouble getting this off the ground (as both a standalone .R file or in the academictwitteR package. Any thoughts? Getting a 400 status code with the following:
next_token <- ""
k <- 0
while (k < 3 * 3) {
df <- get_tweets(
"beyonce",
n = 500,
start_time = "2010-01-01T00:00:00Z0",
end_time = "2010-10-01T00:00:00Z",
token = bearer_token
)
jsonlite::write_json(df$data, paste0("data/", "data_", df$data$id[nrow(df$data)], ".json"))
jsonlite::write_json(df$includes,
paste0("data/", "includes_", df$data$id[nrow(df$data)], ".json"))
next_token <-
df$meta$next_token #this is NULL if there are no pages left
Sys.sleep(3.1)
k <- k + 3
cat(k,
": ",
"(",
nrow(df$data),
") ",
df$data$created_at[nrow(df$data)],
"\n",
sep = "")
}
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simple pagination (150,000 tweets per 15 minutes without going over rate limit)