This app searches Twitter for real-time snow reports and displays them on the map. Tweet the hashtag #uksnow, your location (postcode, town name or geotag your tweet), and rate the snow that is falling out of ten (0/10 for nothing – 10/10 for a blizzard). You can also include the depth of snow (cm or inches), attach a photo and add a description to your tweet.
Observe a Twitter stream and categorize the presumptive intent of each Tweet. The framing questions could be:
1. What is the tweet implicitly asking me to do in response?
2. What are the other clear contextual features I can infer from a Tweet?
3. What would be the top level categories that could be used to describe a general intent-plus-effect of a Tweet?
This last question could be visualized as sorting Tweets into different boxes based in the information in the Tweet, and, ‘safe’ inferences we would make in the light of the qualifications posed by #1 and #2. Because I’ve narrowed the categorical focus to a top level, the sense of sorting would be to derive very general categories that do not overlap.
What interests me are combinations of factors and features discoverable as a matter of knowing more fully user intent. A blunt question about this is: what is the payoff for observing and/or participating in your own Twitter stream? Implicit in this would be a behavioral economic qualification of intent. There are lots of other directions too, so I wonder about personality factors, enabling tools, affectual elements, etc.
Here is the Netdynam20 stream from 8:35 a.m. EST (USA).