I wanted to explore temporal rhythms around the conference, for example to pick out more popular or particularly tweet-able sessions. Color scheme: Is directed at someone by starting with an @ Contains a mention (@) of someone else Contains a link Disappointingly, no patterns are immediately obvious here - it seems like tweeting was reasonably consistent throughout. I removed directed points to see ...
I'm working on a paper on topical communities, and as part of that I've come back to this dataset to explore the social network that emerges through @ mentions. To start with, I looked at the social network that emerges when we look at the people on the list. This network is pretty densely connected, with the ...
For a while, I've been wanting to make something that explores color in photo sets. Reading Beautiful Visualization (Amazon), I came across the perfect way to arrange the elements. It imitates the layout of the sunflower seeds, "the most efficient and visually mesmerizing way of packing small elements into a large circle". I decided to try ...
Unfortunately I can't post the actual paper for a year, but hopefully the talk is going to be more interesting anyway! I used Google Docs to create my slides and you can find the deck here. Twitter: An Overview Who doesn't use Twitter? Who doesn't use Twitter because they think it's pointless? Before I start, I want to ...
Continued on from this post. Also see my supervisor's comments. Interestingly, it turns out there is an Eclipse product called "Eclipse RT", so perhaps there weren't as many retweets as the wordle might have suggested... Still - 413 of the 640 tweets in the data set contained an @ mention of some kind. Of these, 72 ...
My supervisor had the idea of grabbing a conference dataset by hashtag, specifically the Eclipse Conference 2010 (hashtag #ese) which took place in Ludwigsburg, Germany, November 2nd to November 4th. You can get an idea of what people were talking about in the wordle, below (applet is here): Apparently there were a lot of RT's. We'll explore that ...
I used the Classifier4J Summarizer to summarize the tweets and pick out the 5 tweet summary of the period, and the 1 tweet summary of @ replies and non-directed tweets. Only letters and spaces were kept for the summary (thus each tweet was treated as one sentence), the summarizer transforms everything to lower case and removes ...
Continued on from Part 5, exploring what they are saying using the Phrase Net visualization from Many Eyes. Each image is a link to the applet where you can explore the text and interact with it. Change the linking word on the left - I've used space, but "and" or "is" in particular could be enlightening. I ...
Continued on from Part 4, exploring what they are saying using Word Trees on Many Eyes. Each image is a link to the applet where you can explore the text and interact with it. Change the word in the top left corner to change the root of the tree. Alex Howard Alfred Hermida Andrew Keen ...
In which we answer the question - what are they saying? I've split the tweets up into two types - at replies, and not at replies, and a third which contains all tweets. I've created wordles of each one, for each of the 20 people we were following. If you haven't - check out wordle.net. It's awesome. There's ...
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