People 'ify'-ing stuff.
It's been happening a lot, 'gamifying' being a big thing over the last few years.
This week's sandbox focuses on storyfying...well...stories.
We were telling the story of the Goldsmiths election day through live, multimedia uploads curated through Storify.
By curating media and content from across youtube, twitter, instagram, facebook, foursquare, audioboo, amongst others, we were able to build an ongoing story.
It was great to have an inspirational @Documentally, Christan Payne to show us the ropes and guide us through the best way to utilise the tools available.
Take a look at the story below.
Tuesday 7 February 2012
Friday 3 February 2012
Tweeting Terry's Armband.
I wanted to explore using Twitter to create real time analysis, reaction and to find stories as stories break for my lastest article for matchchatter.
Not many stories this year have been as big as Terry losing the England captaincy which broke around 11am this morning.
First step was using @mhawksey's twitter archiver script in google spreadsheets and twitter visualiser to follow and map conversations across twitter.
Originally the visualisation was messy with a huge volume of individual tweets, and with such a large data set, fairly unresponsive. I planned on working out where to include a for loop to include only tweets RT'd at least once or tweets which have replies - to catch real conversations, however the script seemed to be slowed down after two runs - not allowing me to download the second scrape (this is where running the scraper independently of google docs, and therefore servers would have been much better). The RT's however were an interesting field to look into itself - what are the most popular things people are saying and who are the most influential? At least the latter we could tell from the TAGSExplorer visualisation.
You can see where the conversations are spawning from - following are conversations with Gary Lineker for example. (I use the word 'conversation' loosely, really they are tweets directed at him after his original tweets on the subject.) This was interesting to see what people were saying in reply to an influential Twitter user engaged in the conversation.
The first scrape collected 4110 tweets using the search terms "terry" "captain" "#eng" and "armband". Therefore the scrape will have included in tweets complete unrelated to the subject. That's why statistical analysis of the words collected are important.
In the first scrape we can see that there are 309 mentions of "Parker", 267 of "Gerrard" and 161 of "Ferdinand" - this was worked out by "wordle's" analysis when creating the word clouds. Bear in mind a second analysis is yet to be run for terms such as "@rioferdy5" - Ferdinand's twitter account and "Stevie G" etc.
We can see Parker also features the heaviest in the google doc's word cloud feature - an independent algorithm also states Parker is the most commonly mentioned player in relation to the conversation.
The word cloud was a good way to see what the broad conversation was saying most frequently and so I used wordle to create the John Terry word clous which is the featured image in the article.
----------------------
Not many stories this year have been as big as Terry losing the England captaincy which broke around 11am this morning.
First step was using @mhawksey's twitter archiver script in google spreadsheets and twitter visualiser to follow and map conversations across twitter.
Originally the visualisation was messy with a huge volume of individual tweets, and with such a large data set, fairly unresponsive. I planned on working out where to include a for loop to include only tweets RT'd at least once or tweets which have replies - to catch real conversations, however the script seemed to be slowed down after two runs - not allowing me to download the second scrape (this is where running the scraper independently of google docs, and therefore servers would have been much better). The RT's however were an interesting field to look into itself - what are the most popular things people are saying and who are the most influential? At least the latter we could tell from the TAGSExplorer visualisation.
 |
| TAGSExplorer visualisation. |
 |
| Gary Lineker's England captaincy tweets. |
You can see where the conversations are spawning from - following are conversations with Gary Lineker for example. (I use the word 'conversation' loosely, really they are tweets directed at him after his original tweets on the subject.) This was interesting to see what people were saying in reply to an influential Twitter user engaged in the conversation.
The first scrape collected 4110 tweets using the search terms "terry" "captain" "#eng" and "armband". Therefore the scrape will have included in tweets complete unrelated to the subject. That's why statistical analysis of the words collected are important.
In the first scrape we can see that there are 309 mentions of "Parker", 267 of "Gerrard" and 161 of "Ferdinand" - this was worked out by "wordle's" analysis when creating the word clouds. Bear in mind a second analysis is yet to be run for terms such as "@rioferdy5" - Ferdinand's twitter account and "Stevie G" etc.
We can see Parker also features the heaviest in the google doc's word cloud feature - an independent algorithm also states Parker is the most commonly mentioned player in relation to the conversation.
The word cloud was a good way to see what the broad conversation was saying most frequently and so I used wordle to create the John Terry word clous which is the featured image in the article.
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Tuesday 31 January 2012
Pipes
In our sandbox this week we had Tony Hirst from OUseful.info come in and deliver a great session on both mapping data using google docs, yahoo pipes as well as Gephi to map social networks. This is how we created a map of the UK's largest towns and cities by population.
We created location based spreadsheet by scraping a Wikipedia table, then published it as a csv spreadsheet using google docs.
Piped csv file through yahoo pipes - using regex & location builder module -
http://pipes.yahoo.com/leonpuplett/uktownpop
Run pipe - creates yahoo map.
copy link from "get as kml" and pasts into maps.google.com
Can embed:
View Larger Map
We created location based spreadsheet by scraping a Wikipedia table, then published it as a csv spreadsheet using google docs.
Piped csv file through yahoo pipes - using regex & location builder module -
http://pipes.yahoo.com/leonpuplett/uktownpop
Run pipe - creates yahoo map.
copy link from "get as kml" and pasts into maps.google.com
Can embed:
View Larger Map
Friday 27 January 2012
Olympic Stadium Timeline
Olympic Stadium Timeline test for ELO.
I've been working on an interactive timeline for East London Olympics about the history of the Olympic Stadium so far.
I created two versions as the first created using Dipity is ad supported ($99p/m to get rid of them!), the second was using timetoast which is not ad supported and loads quicker, isn't as visually interesting.
I've been working on an interactive timeline for East London Olympics about the history of the Olympic Stadium so far.
I created two versions as the first created using Dipity is ad supported ($99p/m to get rid of them!), the second was using timetoast which is not ad supported and loads quicker, isn't as visually interesting.
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