Scraping data from Twitter’s Streaming API
Pablo Barbera
June 27, 2017
Scraping web data from Twitter
Authenticating
Follow these steps to create your token:
- Go to apps.twitter.com and sign in.
- Click on “Create New App”. You will need to have a phone number associated with your account in order to be able to create a token.
- Fill name, description, and website (it can be anything, even http://www.google.com). Make sure you leave ‘Callback URL’ empty.
- Agree to user conditions.
- From the “Keys and Access Tokens” tab, copy consumer key and consumer secret and paste below
#install.packages("ROAuth")
library(ROAuth)
requestURL <- "https://api.twitter.com/oauth/request_token"
accessURL <- "https://api.twitter.com/oauth/access_token"
authURL <- "https://api.twitter.com/oauth/authorize"
consumerKey <- "YOUR_CONSUMER_KEY"
consumerSecret <- "YOUR_CONSUMER_SECRET"
my_oauth <- OAuthFactory$new(consumerKey=consumerKey,
consumerSecret=consumerSecret, requestURL=requestURL,
accessURL=accessURL, authURL=authURL)Run the below line and go to the URL that appears on screen. Then, type the PIN into the console (RStudio sometimes doesn’t show what you’re typing, but it’s there!)
my_oauth$handshake(cainfo = system.file("CurlSSL", "cacert.pem", package = "RCurl"))Now you can save oauth token for use in future sessions with netdemR or streamR. Make sure you save it in a folder where this is the only file.
save(my_oauth, file="credentials/twitter-token.Rdata")Collecting data from Twitter’s Streaming API
Collecting tweets filtering by keyword:
library(streamR)## Loading required package: RCurl## Loading required package: bitops## Loading required package: rjsonload("credentials/twitter-token.Rdata")
filterStream(file.name="trump-tweets.json", track="trump",
timeout=20, oauth=my_oauth)## Capturing tweets...## Connection to Twitter stream was closed after 20 seconds with up to 493 tweets downloaded.Note the options: - file.name indicates the file in your disk where the tweets will be downloaded
- track is the keyword(s) mentioned in the tweets we want to capture. - timeout is the number of seconds that the connection will remain open
- oauth is the OAuth token we are using
Once it has finished, we can open it in R as a data frame with the parseTweets function
tweets <- parseTweets("trump-tweets.json")## Warning in vect[notnulls] <- unlist(lapply(lst[notnulls], function(x)
## x[[field[1]]][[field[2]]][[as.numeric(field[3])]][[field[4]]])): number of
## items to replace is not a multiple of replacement length## 163 tweets have been parsed.tweets[1,]## text
## 1 RT @PrincessBravato: Trump doesn't care about Russia and cyberactivity. \nHe is more in a hurry to water down sanctions\n#TrumpRussia\n#TrumpR…
## retweet_count favorited truncated id_str
## 1 65 FALSE FALSE 880364408811257856
## in_reply_to_screen_name
## 1 <NA>
## source
## 1 <a href="https://mobile.twitter.com" rel="nofollow">Twitter Lite</a>
## retweeted created_at in_reply_to_status_id_str
## 1 FALSE Thu Jun 29 09:56:46 +0000 2017 <NA>
## in_reply_to_user_id_str lang listed_count verified location
## 1 <NA> en 0 FALSE <NA>
## user_id_str
## 1 831222362779553796
## description
## 1 #AgentsofSHIELD superfan. All-around geek. Humanist, liberal, moderate. HATE bullies. #ImpeachTrump #ImpeachTrumpNow #Impeach45 #TheResistance #Resist
## geo_enabled user_created_at statuses_count
## 1 FALSE Mon Feb 13 19:23:49 +0000 2017 7641
## followers_count favourites_count protected user_url name
## 1 163 37418 FALSE <NA> Alex Forrest ❄
## time_zone user_lang utc_offset friends_count
## 1 Pacific Time (US & Canada) en -25200 183
## screen_name country_code country place_type full_name place_name
## 1 northernstar171 <NA> <NA> <NA> <NA> NA
## place_id place_lat place_lon lat lon expanded_url url
## 1 NA NaN NaN NA NA <NA> <NA>If we want, we could also export it to a csv file to be opened later with Excel
write.csv(tweets, file="trump-tweets.csv", row.names=FALSE)And this is how we would capture tweets mentioning multiple keywords:
filterStream(file.name="politics-tweets.json",
track=c("graham", "sessions", "trump", "clinton"),
tweets=20, oauth=my_oauth)Note that here I choose a different option, tweets, which indicates how many tweets (approximately) the function should capture before we close the connection to the Twitter API.
This second example shows how to collect tweets filtering by location instead. In other words, we can set a geographical box and collect only the tweets that are coming from that area.
For example, imagine we want to collect tweets from the United States. The way to do it is to find two pairs of coordinates (longitude and latitude) that indicate the southwest corner AND the northeast corner. Note the reverse order: it’s not (lat, long), but (long, lat).
In the case of the US, it would be approx. (-125,25) and (-66,50). How to find these coordinates? I use: http://itouchmap.com/latlong.html
filterStream(file.name="tweets_geo.json", locations=c(-125, 25, -66, 50),
timeout=30, oauth=my_oauth)## Capturing tweets...## Connection to Twitter stream was closed after 30 seconds with up to 225 tweets downloaded.We can do as before and open the tweets in R
tweets <- parseTweets("tweets_geo.json")## 124 tweets have been parsed.And use the maps library to see where most tweets are coming from. Note that there are two types of geographic information on tweets: lat/lon (from geolocated tweets) and place_lat and place_lon (from tweets with place information). We will work with whatever is available.
library(maps)
tweets$lat <- ifelse(is.na(tweets$lat), tweets$place_lat, tweets$lat)
tweets$lon <- ifelse(is.na(tweets$lon), tweets$place_lon, tweets$lon)
states <- map.where("state", tweets$lon, tweets$lat)
head(sort(table(states), decreasing=TRUE))## states
## california texas florida illinois ohio
## 18 13 9 5 5
## pennsylvania
## 5We can also prepare a map of the exact locations of the tweets.
library(ggplot2)
## First create a data frame with the map data
map.data <- map_data("state")
# And we use ggplot2 to draw the map:
# 1) map base
ggplot(map.data) + geom_map(aes(map_id = region), map = map.data, fill = "grey90",
color = "grey50", size = 0.25) + expand_limits(x = map.data$long, y = map.data$lat) +
# 2) limits for x and y axis
scale_x_continuous(limits=c(-125,-66)) + scale_y_continuous(limits=c(25,50)) +
# 3) adding the dot for each tweet
geom_point(data = tweets,
aes(x = lon, y = lat), size = 1, alpha = 1/5, color = "darkblue") +
# 4) removing unnecessary graph elements
theme(axis.line = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank(),
panel.background = element_blank(),
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.background = element_blank()) 
And here’s how to extract the edges of a network of retweets (at least one possible way of doing it):
tweets <- parseTweets("trump-tweets.json")## Warning in vect[notnulls] <- unlist(lapply(lst[notnulls], function(x)
## x[[field[1]]][[field[2]]][[as.numeric(field[3])]][[field[4]]])): number of
## items to replace is not a multiple of replacement length## 163 tweets have been parsed.# subset only RTs
rts <- tweets[grep("RT @", tweets$text),]
edges <- data.frame(
node1 = rts$screen_name,
node2 = gsub('.*RT @([a-zA-Z0-9_]+):? ?.*', rts$text, repl="\\1"),
stringsAsFactors=F
)
library(igraph)##
## Attaching package: 'igraph'## The following objects are masked from 'package:stats':
##
## decompose, spectrum## The following object is masked from 'package:base':
##
## uniong <- graph_from_data_frame(d=edges, directed=TRUE)Finally, it’s also possible to collect a random sample of tweets. That’s what the “sampleStream” function does:
sampleStream(file.name="tweets_random.json", timeout=30, oauth=my_oauth)## Capturing tweets...## Connection to Twitter stream was closed after 30 seconds with up to 3663 tweets downloaded.Here I’m collecting 30 seconds of tweets. And once again, to open the tweets in R…
tweets <- parseTweets("tweets_random.json")## Warning in vect[notnulls] <- unlist(lapply(lst[notnulls], function(x)
## x[[field[1]]][[field[2]]][[as.numeric(field[3])]][[field[4]]])): number of
## items to replace is not a multiple of replacement length## 1169 tweets have been parsed.What is the most retweeted tweet?
tweets[which.max(tweets$retweet_count),]## text
## 556 RT @GChase_: How did they get all of the reactions in high def? I'm in tears \xed\xa0\xbd\xed\xb8\x82 https://t.co/lLfN79Rwr2
## retweet_count favorited truncated id_str
## 556 137769 FALSE FALSE 880364687996710912
## in_reply_to_screen_name
## 556 <NA>
## source
## 556 <a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>
## retweeted created_at in_reply_to_status_id_str
## 556 FALSE Thu Jun 29 09:57:52 +0000 2017 <NA>
## in_reply_to_user_id_str lang listed_count verified location
## 556 <NA> en 0 FALSE <NA>
## user_id_str description geo_enabled user_created_at
## 556 1089194378 självmord. FALSE Mon Jan 14 14:33:44 +0000 2013
## statuses_count followers_count favourites_count protected user_url
## 556 287 59 352 FALSE <NA>
## name time_zone user_lang utc_offset friends_count
## 556 självmordstankar. <NA> sv NA 177
## screen_name country_code country place_type full_name place_name
## 556 johnwallin99 <NA> <NA> <NA> <NA> NA
## place_id place_lat place_lon lat lon expanded_url url
## 556 NA NaN NaN NA NA <NA> <NA>What are the most popular hashtags at the moment? We’ll use regular expressions to extract hashtags.
library(stringr)##
## Attaching package: 'stringr'## The following object is masked from 'package:igraph':
##
## %>%ht <- str_extract_all(tweets$text, "#(\\d|\\w)+")
ht <- unlist(ht)
head(sort(table(ht), decreasing = TRUE))## ht
## #임영민 #BNMBOYS #blog
## 6 5 4
## #BTS #ChampalimaudFoundation #DafBama2017_BTS
## 3 2 2How many tweets mention Justin Bieber?
length(grep("bieber", tweets$text, ignore.case=TRUE))## [1] 1Now it’s your turn to practice! Open a connection to the Streaming API and collect a sample of tweets based on keywords that you may find interest. Keep it open for about a minute. Then, read it in R and check who are the most popular users and which hashtags were used the most during this period. What do you find?