More or less active?

The first thing we can investigate is if my behavior in Twitter has changed in these 10 years. My feeling is that people spend less time in the platform when we get older. One reason is that, compared to 2009, it is really difficult to keep tract of what is happening in the platform. I also have less and less time. But it is true that twitter has changed their app to engage users more with the converstation, so that might counterbalance it.

To analyze it, let’s add the formated timestamp to the dataset

tweets$timestamp <- as.POSIXct(tweets$created_at,format="%a %b %d %H:%M:%S %z %Y")

and plot the number of tweets by month.

require(ggplot2)
require(zoo)
ggplot(tweets,aes(x=as.yearmon(timestamp))) + geom_bar(binwidth=.09)+scale_x_yearmon() + theme_bw() + ylab("Number of tweets per month") + xlab("Time")

As we can see, the most active years were from 2011 to 2015 (around 100 tweets per month). From then on I am tweeting less, corroborating my feeling that I spent less time in the platform (at least tweeting 🙂 )

Tweeting or retweeting more?

Have I changed the way I use Twitter? Our research in Twitter sessions and social networks using mobile phone data shows that because of our limited atention and cognitive capacities people tend to perform simpler tasks with time and age. For example we found that in long sessions in Twitter (two or more hours), users start composing less messages (which require more effort) and use more retweets or mentions (replies) within the session, that require less effort.

Let’s see what happened in ten years of data. We classify tweets as composed, mention or retweets using the fields in the dataset.

tweets$class <- "normal"
tweets$class[!is.na(tweets$in_reply_to_status_id)] <- "mention"
tweets$class[!is.na(tweets$retweeted_status_id)] <- "RT"
tweets$class[grep("RT @",tweets$full_text)] <- "RT"

and show the fraction of tweets per month in each class

require(ggplot2)
require(zoo)
ggplot(tweets,aes(x=as.yearmon(timestamp), fill=class)) + geom_bar(position="fill",binwidth=.09)+scale_x_yearmon() + theme_bw() + ylab("Fraction of tweets of each class") + xlab("Time")

Similar to our research for Twitter sessions, I can see that I compose less original tweets with time: while in 2010 almost 50% of my tweets were composed, now only 20% are original and more than 50% of the tweets in my account are retweets.

Tweeting about what?

Finally, let’s see what I tweeted about. Although we could probably do much ellaborated analysis, a simple wordcloud will do here. We clean up the text of the tweets (including mentions and retweets) and produce a wordcloud

require(tm)
require(wordcloud)
texts <- tweets$full_text
#cleanup remove mentions and url
texts <- tolower(texts)
texts <- gsub('\\b+rt', '', texts)
texts <- gsub("@\\S+", "", texts)
texts <- gsub('http\\S+\\s*', '', texts)
texts <- gsub('[[:punct:]]', '', texts) 
texts <- removeWords(texts, stopwords("english")) #get rid of stopwords in english
texts <- removeWords(texts, stopwords("spanish")) #get rid of stopwords in english
corpus.texts.all <- Corpus(VectorSource(texts))
wordcloud(corpus.texts.all, 
          max.words=100, random.order= FALSE, colors=brewer.pal(8,"Dark2"),scale = c(3,.5))

As you can see, the word I used more is thanks (in spanish), together with other related to my field of research (networks, data, social, etc.).

Conclusion

So, yes I am growing older in Twitter and the way I use the platform is different. I probably spend more time reading that engaging in new conversations, new tweets or creating hashtags for events or conferences. This exercise proves that not only Twitter is a good platform to understand timely events like elections, sports, natural disasters or unemployment, but also to understand how people change in the course of a lifetime (or at least tens of years) with and outside the platform.

And as my wordcloud shows, I am thankfull for those 10 years of sharing the platform with friends, colleagues and other familiar strangers!