Interestingly, deletion of Sina Weibo messages tend to hit a low on Saturdays. I’m not too sure why that is except that maybe censors want to take time off on weekends as well. If you want to maximize the length of time your message will remain on Sina Weibo, probably the best time is to post the message after 11 PM Friday night.

The second analysis I did with the Weibo Scope Search was to try and figure out how long it took the censors to delete messages on Sina Weibo. Each Sina Weibo has a time stamp for when it was created. The Weibo Scope Search checks Sina Weibo‘s timeline at most four times a day (but usually less due to limits that Sina Weibo imposes). Let’s say for instance, a user posts a message on Sina Weibo at 8 AM. Weibo Scope Search checks Sina Weibo‘s timeline at 9 AM, 3 PM, 9 PM, and 3 AM. If the message was deleted by the censor at 10 AM, it would show up on Weibo Scope Search‘s “deleted time” as 3 PM.

The fastest a post was deleted on Sina Weibo was just over 4 minutes. The longest time it took for the censor to get around deleting a message on Sina Weibo was over four months. For the posts created on May 20, 2012 and deleted on the same day, it took on average 11 hours for Weibo Scope Search to detect the deletion. It took the censors about 14 hours to delete my post “chen guangcheng.” Determining the average time it takes for censors to delete “irresponsible” messages is a bit tricky since we don’t have data on exactly how long it takes for each post to be deleted. Out of curiosity, I pulled up three messages that took over four months to delete to see what they said:

I’m not too sure why it took so long to delete the posts. Cedric Sam points out that the posts may have been in the Weibo Scope Search database to begin with and they just didn’t turn up until several months later. The researchers at University of Hong Kong’s Journalism and Media Studies Centre are constantly adding new Sina Weibo to their list. Or, they could have just turned on the deletion marking system in the Weibo Scope Search so that it would have caught some censored posts that weren’t caught before.

To be sure, there is no way to tell for sure whether some of the posts were deleted by the users themselves instead of “monitoring editors.” Sina’s API returns two types of error messages: “Weibo does not exist” and “Permission denied.” We assume that when a post is deleted by the user, the “Weibo does not exist” error message comes up. When a post is censored, the “Permission denied” error message comes up. Weibo Scope Search keeps track of all the deleted posts that have the “Permission denied” error message.

If I had more time (and knew how to code), I would have liked to have analyzed more of the data that Weibo Scope Search came up with. Among the things I would have liked to explore are:

• Geographic distribution of deleted messages on Sina Weibo – The Carnegie Mellon paper also looked at geographic distribution of censored Sina Weibo and found that messages issued from Tibet, Qinghai and Ningxia are deleted at a higher rate. Weibo Scope Search also had data on the city and province that each message originated from. However, I didn’t have enough time to figure out how to convert Sina’s data in its city and province into a fungible type of data to transpose on a map.
• Relationships between the most censored Sina Weibo accounts – Using Weibo Scope Search, we’re able to rank the 3,524 users whose Sina Weibo messages are being deleted the most to last. One thing I’d be interested in exploring is how many followers these Sina Weibo accounts have and whether they follow each other. It’s not clear to me if the censors have compiled a list of influential Sina Weibo accounts and are tracking them daily or the censors are using key word searches to figure out what to censor.
• A deeper analysis into the most censored Chinese words on Sina Weibo – Several weeks ago, I did a word cloud of the most censored Chinese words on Sina Weibo to see what came up. By far, the most censored words were the Chinese words for “retweet” followed by “ha ha” or some variation. It makes sense, but it’s not very helpful. Given more time, I would have liked to dig a little deeper to see if there were any words or code words that consistently came up again and again after filtering out the “retweets,” “ha ha,” and other stop words.

How to Analyze WeiboScope Search Data

King-Wa Fu and Cedric Sam at the University of Hong Kong’s Journalism and Media Studies Centre have built a WeiboScope Search that sends all of the deleted Weibo posts to a server in Hong Kong and stores them.  However, the data is in JSON format, which looks like this:

To make sense of the data collected, we need to first clean up the data. I used Google refine to clean up the data by:

1) Download + install Google refine
2) Click on “Create Project”
3) Click on “Web Addresses (URLs)”

4) Insert link http://research.jmsc.hku.hk/social/sinaweibo/lastpermissiondenied.all.json
5) Click “Next”

6) Highlight the fields you’re interested in and left click the mouse.

Google refine should automatically put all the fields into columns:

7) Click “Create Project”
8) Click “Export”
9) Click “Excel”

Now that we have the data formatted, we want to make sense of it.

The first project I did was to graph the deleted weibos on a timeline. My classmate Eugene Wu suggested that the best software to visualize the data is Tableau.

1) Download + install Tableau
2) Click on “Open Data”
3) Under “Connect to Data: In a file”, click on “Microsoft Excel”
4) When the “Excel Workbook Connection” window pops up, click “Ok”

5) Change the format for the “created at” and “deleted” columns from “text” to “Date & time” by right clicking the mouse, selecting “Change Data Type” then “Date & time.”

6) Go to the Dimensions box and drag the “deleted” data set to “Columns”
7) Go to the Measures box and drag the “Number of Records” data set to “Rows”
8) In the “Show Me” box, select the type of graph you want. Voila!