An ongoing social media presence is an important part of many crowdsourced humanities projects. This can be used to promote the project, engage a wider range of contributors and provide a channel for collaboration between academics and other interested parties.
Planning and Hosting a Twitter Chat
Leading on from my previous blog post which explored how the Letters of 1916 project uses Twitter, I planned and hosted a Twitter chat focusing on the challenges faced by teachers using digital resources in the classroom. I chose this particular topic as the letters are a fantastic resource and, as we saw from the enthusiasm surrounding the 1916 in Transition project, teachers are keen to use them once they know what is available and how to access them.
The purpose of the chat was to:
The topic of the chat was launched via a blog post on the Letters of 1916 site. As part of the preparation for the Twitter chat I identified groups, for example teachers and archivists, who may be interested in the the use of digital resources in the classroom, as well as some of the relevant hashtags (e.g. #edchatie, #ukedchat). These were used in promotional tweets in the week leading up to the date of the chat (Wednesday 25th November). Some of the tweets were sent live, others were scheduled in advance (using Tweetdeck) to promote the chat to as broad a range of people as possible (see Storify ‘Initial Announcements’ and ‘Advertising the chat’).
In addition to more general advertising, I also decided to target specific English and History teachers who use Twitter. This had a mixed reception, some, but not all, of the targeted teachers responded to the invitation – one of them had, coincidentally, been using family letters from World War 1 in lessons.
On the day of the chat a series of tweets were sent out reminding Twitter users of the time and focus of the chat, these were more frequent as the 7pm start time of the chat drew closer (see Storify ‘Final Advertising’).
The final part of the preparation was to draft and schedule a number of tweets, some of these were saved onto a Google Document – accessible by the Letters of 1916 team – while others were used to promote some of the topics the letters covered. The latter were scheduled at 15 minute intervals from 7:05 and were also used to act as markers for the analysis carried out after the chat. The chat itself was busy, although the number of contributors was relatively low (see Storify ‘The Chat: 7pm – 8pm’).
Analysing the Twitter Chat
Although the number of actual contributors seemed disappointing, something expressed by one of the contributors, this is not entirely unexpected. In his 2006 article, Nielsen states that:
Looking at the number of tweets and types of interaction is relatively straightforward, and proved to be very interesting. I downloaded the Twitter Analytics for my tweets (Twitter only allow you to access your own tweets for free) during the chat; this data can be saved as a .csv file. The analysis below was carried out using R.
This first section of code reads in the .csv file and creates a data frame containing just the data for the Twitter Chat.
# Upload the saved Twitter data .csv file into R twit <- read.csv("twitter_26.csv") # Use head() to view the top few lines and then select the relevant lines head(twit) twt <- twit[,1:22] # Use the glob2rx() function to create a regular expression selecting only the relevant dates grx <-glob2rx("2015-11-25*") # Use with(), grepl() and the regex to select the tweets from the correct date x <- with(twt, twt[grepl(grx, time), ]) # Reverse the order of the data so it runs from earliest to latest then select the desired time range x2 <- x[68:1,] y <- which(x2$time == "2015-11-25 19:00 +0000") y2 <- which(x2$time == "2015-11-25 20:00 +0000") chat <- x2[y:y2,] # Create a reduced data frame of the core numerical data chatT <-data.frame(tweet.No = factor(c(1:47)), levels = c(1:47), imp = chat$impressions, eng = chat$engagements, rt = chat$retweets, like = chat$likes, rep = chat$replies, ht = chat$hashtag.clicks, email = chat$email.tweet, mv = chat$media.views, me = chat$media.engagements)
The reduced data frame covers 47 tweets I sent during the course of the Twitter chat. The first thing I wanted to find out was how many people interacted with the tweets, Twitter Analytics calls this ‘Engagements’ and defines it as “Total number of times a user has interacted with a Tweet. This includes all clicks anywhere on the Tweet (including hashtags, links, avatar, username and Tweet expansion) , retweets, replies, follows and likes”.
# Load the ggplot2 graphics package library(ggplot2) # Create a graph showing Engagement by Tweet ggplot(data=chatT, aes(x=tweet.No, y=eng, fill=tweet.No)) + geom_bar(colour="black", stat="identity") + guides(fill=FALSE) + ggtitle("Engagement by Tweet")
The graph shows the engagement rate by tweet and we can see that tweets 1 (the welcome tweet), 15 and 26 (both tweets including images which show some of the topics the letters cover) gained the most engagements. The engagements can be subdivided into those who actively engaged with the chat, through replies and retweets (a total of 54 responses) and those who were active but hidden (129 clicks, likes etc.).
The next graph shows the number of ‘Impressions’, which Twitter defines as “Number of times users saw the Tweet on Twitter”.
# Create a graph showing Impressions by Tweet ggplot(data=chatT, aes(x=tweet.No, y=imp, fill=tweet.No)) + geom_bar(colour="black", stat="identity") + guides(fill=FALSE) + ggtitle("Impressions by Tweet")
This graph is particularly interesting as it highlights the number of ‘lurkers’ who can see the tweets is far higher than those who actively engage with them. This reinforces Nielsen’s notion of ‘participation inequality’.
Exploring the peaks on the two graphs can highlight a number of areas which could help improve interaction in a Twitter chat. A number of the peaks on both graphs are visual images, which suggest that this is a key factor for gaining an audiences attention and encouraging them to comment. Other peaks may indicate the interests of the user, for example issues of technology and bandwidth, an unusual or cryptic letter extract (tweet 27 “Postcard in Irish translated “I am here again and missing Dublin. The angel didn’t meet me. He had probably left early. 1/2 #AskLetters1916”), and references to specific uses of letters in teaching (tweet 46 “English teachers could Letters1916 be used to demonstrate language register – personal, business etc? or change over time? #AskLetters1916”).
“Analytics.” Twitter. Twitter, n.d.
Nielsen, Jakob. “Participation inequality: The 90-9-1 rule for social features.” Nielsen Norman Group. 9 Oct. 2006. Https://www.nngroup.com/articles/participation-Inequality/. 18 Dec. 2015.
Ross, Claire. “Social Media for Digital Humanities and Community Engagement.” Digital humanities in practice. Ed. Claire Warwick, Melissa Terras, and Julianne Nyhan. London: Facet Publishing, 2012. 23–45.