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• Data
  • rtweet
  • Search
• Explore
  • Tweet frequency over time
  • Positive/negative sentiment
  • Semantic networks

This is a dedicated repository for tracking#NICAR18 tweets (the official hashtag of 2018 annual Computer-Assisted Reporting Conference).

Whether you lookup the status IDs or search/stream new tweets, you'll need to make sure to install thertweet package. The code below will install [if it's not already] and load rtweet.

## install rtweet if not alreadyif (!requireNamespace("rtweet",quietly=TRUE)) {  install.packages("rtweet")}## load rtweetlibrary(rtweet)#>#> Attaching package: 'rtweet'#> The following object is masked from 'package:tfse':#>#>     round_time

Our data collection method is described in detail below. However, if you want to get straight to the data, simply run the following code:

## download status IDs filedownload.file("https://github.com/computer-assisted-reporting/NICAR18/blob/master/data/search-ids.rds?raw=true","NICAR18_status_ids.rds")## read status IDs fromdownloaded fileids<- readRDS("NICAR18_status_ids.rds")## lookup data associated with status idsrt<-rtweet::lookup_tweets(ids$status_id)

One of the easiest ways to gather Twitter data is to search for the data (using Twitter's REST API). Unlike streaming, searching makes it possible to go back in time. Unfortunately, Twitter sets a rather restrictive cap–roughly nine days–on how far back you can go. Regardless, searching for tweets is often the preferred method. For example, the code below is setup in such a way that it can be executed once [or even several times] a day throughout the conference. See theR code here.

Here's some example code showing what essentially we're doing to collect the data:

## search termsnicar18conf<- c("NICAR18","NICAR2018","IRE_NICAR")## search for up to 10,000 tweets mentioning nicar18rt<- search_tweets(paste(nicar18conf,collapse=" OR"),n=10000)

To explore the Twitter data, we recommend using thetidyverse packages. We're also using a customizedggplot2 theme. See theR code here.

To create the image below, the data were summarized into a time series-like data frame and then plotted in order depict the frequency of tweets–aggregated in two-hour intevals–about #nicar18 over time. See theR code here.

Next, some sentiment analysis of the tweets so far. See theR code here.

The image below depicts a quick and dirty visualization of the semantic network (connections via retweet, quote, mention, or reply) as it is observed in the data. See theR code here.

Ideally, the network visualization would be an interactive, searchable graphic. Since it's not, I've printed out the node size values below.