Evolution of #manila823 and #manilahostage hashtags on Twitter

Evolution of %23manila823 and %23manilahostage hashtags on Twitter

Like a lot of people today here, I’ve been swept away with the tragedy in Manila involving Hong Kong tourists.

I understood that something was happening on the second time I saw people standing in front of televisions in a consumer electronics store, in the commercial district of Causeway Bay. But I only “got” the news when I went to read my Twitter feed (@cedricsam) and started following confusing reports of reports, each fighting for retweets, and thus, relevance.

Here above is just a simple graph showing the evolution of two hashtags, in number of tweets by two-minute intervals. I had a script prepared in advance, previously used on the reaction of the twittersphere to black rainstorm alerts in Hong Kong.

The story by Bloomberg indicates a “10-hour standoff”, but my data only shows a maximum of 500 tweets (a limitation of the Twitter API).

In the case of #manila823, a popular hashtag for Chinese tweeters, the maximum was attained, meaning that more tweets could potentially be found before 20:00 HKT. A Twitter user based out of Guangzhou, @Doriscafe, seemed to be leading the pack.

For #manilahostage, I retrieved about 300 tweets containing the hashtag, which went past a few times on my English-language Twitter feed.

(The peaks at about 20:45 local time occurred when the gunman was announced dead and when the last hostages were starting to be evacuated from the bus.)

If Twitter data was more searchable in the past (or if we are more systematic in sampling Twitter searches on important events), we could possibly do more in visualising what’s buzzing at any given moment.


And now the readers of @CCHK

After the graphic showing the breakdown by time zone for @cmphku readers, here’s another one showing it for @CCHK readers.

CCHK is Creative Commons Hong Kong, and we at the JMSC are hosting Creative Commons for the HKSAR. The Twitter account was registered in October 2008 and was until the advent of @cmphku, the most closely followed at JMSC.

Notice the high proportion of readers from the Alaska time zone (which does not include Hawaii). And from manual inspection of a screen names sample, most of these users may well not be from Alaska… In fact, it seems like most are based out of China, although I don’t have a precise number/proportion to offer.


China Media Project on Twitter: @cmphku followers around the globe

I uploaded compiled data to Google Fusion Tables on our followers of @cmphku (only the aggregate is shown here for privacy issues), and here’s what it gives. The location data is based on the time_zone column in the Twitter API users/show query.


Visualising HK Transport Department traffic accident data in Google Fusion Tables

Screenshot-Transport Department - Year 2008 - Google Chrome
Step One: Download the data from the Transport Department website at http://www.td.gov.hk/en/road_safety/road_traffic_accident_statistics/2008/index.html. Scroll down and you will find a link to Road Traffic Accident Database 2008.

Screenshot-Google Fusion Tables - Google Chrome
Step Two: Import to Google Fusion Tables. You have to save the XLS file as individual CSVs, since it’ll take only one table at the time, and the number of rows limit is lower for XLS files.

Screenshot-Google Fusion Tables | Vehicles involved in Road Traffic Accidents in 2008 (Hong Kong) - Google Chrome
Step Three: Visualise. Here, we see that an overwhelming proportion of casualties on the road in 2008 involved men (coded as 1 in the data), but it might just be because of demographics.

Because there is not a lot of unique information to plot (like a datetime of the accident), the suggestion with this data is to do an aggregate on your column of interest (say, driver sex), then plot it as the entity, and use the count as your value. Could be nice to mix and match two criteria (are young men more frequently involved in accidents?).

If you want to play with the data yourself, here are the links to the tables, as imported in Google Fusion Tables:

1. Road Traffic Accident Stats in 2008: http://tables.googlelabs.com/DataSource?dsrcid=224727

2. Vehicles involved in Road Traffic Accidents in 2008: http://tables.googlelabs.com/DataSource?dsrcid=225310

3. Casualties in Road Traffic Accidents in 2008: http://tables.googlelabs.com/DataSource?dsrcid=225311

Here is how it compares in terms of age, whether the casualty involved was male or female (note that the scale is different, being much lower for women).


Male driver casualties in 2008 (plotted by age on the x-axis)


Female driver casualties in 2008 (plotted by age on the x-axis)


Overall driver casualties in 2008 (plotted by age on the x-axis)

The current problem with Google Fusion Tables (which is still a Labs product) is that it won’t allow you to compare more than two criteria at the same time in a practical format. For instance, I can’t superimpose graphs for deaths per sex and per age on one single view. Sounds like a pretty basic feature, so I wouldn’t be terribly surprised if it sprung up in a couple of months, if not weeks.

Quality of the data is also questionable since maybe 60-70 people listed as “drivers” are aged 16 or less… Did they mean they were in the driver’s seat or actually driving when the accident occurred?!

***

On another note, I also imported the news agencies database from China’s General Administration of Press and Publication, which is the state agency regulating news and print publication in the PRC. This data was retrieved at around March 2010 from www.gapp.gov.cn using custom scripts systematically reading the GAPP’s webpages. After parsing into a database-friendly format, I used it to build the China Media Map, which might start to include our annotations, soon.

But frankly, there isn’t much to visualise with this data, aside from location, since it has no contextual data attached to it (it’s just an address/phone book, basically). If you can think of something to do with it, drop me a line.


My Apple Wireless Keyboard can connect to my Google Nexus One

Apple Wireless Keyboard on a Nexus One
Photos by Doug Meigs (JMSC)

Using CyanogenMod (currently in version 6, RC2), I can now connect an Apple Wireless Keyboard to my Google Nexus One phone. This works “out of the box” with the new version of CyanogenMod’s add-on (without additional software like KeyPro or BlueInput) for Bluetooth HID by Erin Yueh of 0xlabs, a bunch of engineers based in Taiwan.

I must say that when I tested writing an e-mail in the Gmail app, I encountered a few bumps, like a hung key that would completely shift the page aside. Since I am just starting to learn this new way of interacting with devices (and which should keep urges to buy an iPad at bay), I will tell you more later on how it feels to have a bluetooth keyboard on the go.

Since I work in a journalism school, my next-desk colleague, who is a freelance reporter as well, immediately sees bluetooth keyboards and a smartphone as an extremely convenient and powerful combination to do (written word) news reporting while literally chasing after the story.

While exploring solutions to connect the keyboard, I also found out that some people managed not only to connect a keyboard (by USB) to their Nexus One phones, but also to use it to act as a host for desktop applications like Firefox to run (from the phone) on an external display.

(Also, the Apple wireless keyboard is another design beauty, for only HK$518 (US$68)…)

Apple Wireless Keyboard on a Nexus One

Apple Wireless Keyboard on a Nexus One