People voice about Lynas Malaysia through Twitter Analysis with R CloudStat: CloudStat Analysis: This is a twitter analysis report for “Lynas” from 21 till 28 February 2012, generated by CloudStat Twitter Application. Lynas was a hot topic, espec...
Since Lynas Malaysia Corporation, Australia Rare Earth Refiner Granted Malaysia License, it is always a very hot topic. This can be proven through the daily tweets across recent 7 days. This chart was built by Tweets Keyword Trending Application @...
I’ve largely avoided “time” in R to date, but following a chat with @mhawksey at #dev8d yesterday, I went down a rathole last night exploring a few ways of visualising a Twitter user timeline and as a result also had a quick initial play with some time handling features of R, such as timeseries objects,
A couple of weeks ago I saw a great example of an open learning blogpost from @katy_bird: Generating a word cloud (or not) from a Twitter hashtag. It described the trials and tribulations associated with trying to satisfy a request for the generation of a wordcloud based on tweets associated with a specific Twitter hashtag.
I recently began using the TwitteR package in R to examine my tweeting patterns. One of my first projects was to identify each of my Twitter followers, where they were located, how many tweets they had, and then plot their location on a map using a bubble which was related to their total number of
If you're a Twitter user like me, you're probably familiar with the way that conversations can easily by tracked by following the #hashtag that participants include in the tweets to label the topic. But what causes some topics to take off, and others to die on the vine? Does the use of retweets (copying another users tweet to your...
I think one of valid criticisms around a lot of the visualisations I post here and on my various #f1datajunkie blogs is that I often don’t post any explanatory context around the visualisations. This is partly a result of the way I use my blog posts in a selfish way to document the evolution of
Last year, I covered a number of the so-called “Twitter protests” in China (#cn220), Iran (#25bahman), and Algeria (#fev12). Since these protests began in January 2011, the Arab Spring has claimed many members of both ruling and revolting groups … Continue reading →
I grabbed independent samples of 1500 recent users of the #newsnight and #bbcqt hashtags within a minute or two of each other about half an hour ago. Here’s who’s followed by 25 or more of the recent hashtaggers in each case. Can you distinguish the programmes each audience interest projection map relates to? Here’s the
Following on from A Tool Chain for Plotting Twitter Archive Retweet Graphs – Py, R, Gephi, here’s a quick view summary view over #UKGC12 tweets saved in Google Spreadsheet archive as developed by Martin Hawksey, generated from an R script (R code available here; #ukgc12 tweet archive here)… (I did mean to tidy these up,