There are two or three major things I’m thinking about at the moment – and one of them is zeitgeists. Which brings me rapidly to The World according to LiveJournal which is an awesome tracking system of LiveJournal moods over the last seven days. If you go and look at it now and try moods like ‘sympathetic’, ‘distressed’ or ‘nauseated’ you can see that the bombings in London have had a real impact on people’s moods. If you invetigate more thoroughly you can see that other moods have been inversely affected or show more complex relationships. There was a parallel drop on ‘horny’ during the coverage, an enormous drop in ‘irritated’ which then turned into a spike. Fewer people felt ‘guilty’, more felt ‘grateful’.
A couple of obvious things fall out of this for me – you could use this data to articulate relationships in moods really effectively – which things in the world cause reactions, what kinds of reactions do they cause, which moods are more closely correlated or act against one another. I keep looking for clear moods that you’d expect to see appearing twenty four hours after an event like this, but so far I’m only seeing a few (people seemed to become irate in two major spikes – I wonder why).
Another obvious thing would be to use this data to alert people to things that were going on in the world or to track trends over time. I believe that LiveJournal knows which country people are from – combining that data with the stuff from the site would be tremendously useful. Sending alerts to news gathering organisations would be interesting too. Mood expression and collation is such a fascinating area and has some real possibilities for data-mining and zeitgeist taking. Can anyone else think of good ways to get this information from people and to employ it – ideally in an open way? The best I can come up with off the top of my head is AIM status messages using controlled vocabularies and opened up in some spiderable fashion…
3 replies on “On Live Journal mood tracking and zeitgeists…”
Same kinda thing.
Very interesting. I have a few thoughts for expanding it. First, incorporate LiveJournal’s group membership data to show how different moods spread (or don’t spread) among a social network.
Second, tailor application functionality to mood. A search engine could return mood-influenced results. An email client could add more steps when sending angry emails. iTunes could have a special mood music mode. If the user could add mood in a single location and make it available to a variety of applications, the added functionality would vastly increase incentive to track the data.
Third, the input should be easier and more pervasive. LiveJournal only collects moods when entries are written, but something like a cell phone could track mood all the time, and even reject calls from certain people under certain moods, e.g. don’t ring when parents are calling and mood is “drunk.”
I wonder how accurate these moods are, how defined, how implied in oblique statements.
Much of blog writing is sarcastic, tongue in cheek, or just plain nuts.
How about quotes and reblogging? An angry blog post quote in a blog post at a different blog equals…what?
I’ve tried tracking “flames”, “disagree”, “argue”, and such on a blog tracking/mapping site. I was tracking occurence of word, not actual activity designated by word, nor surely not the mood behind it.
Men often don’t even have moods. And when we do, we have difficulty expressing them in words. Most male moods are actually physical hunger, fear of financial loss, or just plain tired. Not much emotive power in them. We cannot cry.
I’d like to know characteristics of male vs. female bloggers and blogs.
But are the diagnostic methodologies and technologies really precise as yet?