Visualising what people on Twitter like
The Twitter streaming API allows access to tweets on Twitter as they are created. I decided to experiment by writing a data-mining script that collects geo-located tweets from the streaming API that contain people saying that they 'like' something. For this post I have created two visualisations that aim to make sense of the data collected so far and display it in an easily digestible way.
The script has been running for about 5 days in total and has collected 1873 tweets; It has parsed a lot more tweets than that but a tweet is only added when it meets a certain criteria; which is that it is geo-located from the UK and contains a phrase such as 'I like', 'I love', 'I am fond of', etc...
From the data the second most 'liked' thing is Twitter itself which is un-surprising; 'My Life' is 4th and 'My vagina' is 32nd. The most liked things on Twitter are 'That song' and 'This song' which provides a very un-informative insight into the people on Twitter.
The top two images on the right are visualisations of the data. The first image is a 'Tree Map' which was quite simple to put together thanks to Google Chart Tools. The larger the square, the more likes and vice versa. Click here to view the visualisation in full. The data isn't perfect, for example, 'The' is one of the most popular things but I think overall it works quite well at extracting 'likes'.
The second visualisation is a Processing sketch that plots a users location along with what they have said they like. View a larger image of the sketch here. It doesn't really provide any insights geographically but it's a starting point for something that could have a lot more potential for spotting geographical and cultural trends. Download the code and data for the Processing sketch on the Disturb wiki here.
For now, I will leave the spider running for another month or so and then post again with some updated visualisations and hopefully some more interesting data from Twitter.
