Women2Drive Campaign on Twitter

Back in October of last year, I had scraped about 6,000 tweets that contained the hashtag #Women2Drive. The hashtag was dedicated to the campaign to pressure the Saudi government to overturn the ban on women’s right to drive. Although, the campaign did not succeed in changing the law, it did garner international attention on the issue. There were reports on Saudi women defying the ban, but the main story that ignited on Social Media was the Youtube Video and song that was released by Saudi artist entitled “No Woman, No Drive”that was in support of overturning the ban.

https://www.youtube.com/watch?v=aZMbTFNp4wI

For this assignment I decided to take a hack at the data and see what story comes out of it. The data itself contains: the unique ID for each tweet, tweet URL, text, Language, retweet count, time stamp, media URL, screen name and more.

A number of interesting observations emerged from taking an initial look at the tweets, but what struck me is the different conversations that emerged around this issue and how dominant the youtube video was in these conversations. So I decided to visualize the different conversations and their size on twitter.

The next step I had to take was to clean the data,  I started using  Google refine, but decided to stick to basic excel and google spreadsheets to clean up the tweets. I used a basic filter to grab the unique tweets and then go through them to indicate the different conversations. There was a lot of counting, squinting at tweets and numbers but I managed to narrow the conversations down to the following topics:

1. Youtube Video

2. Defying the Driving Ban

3. Support for the Campaign

4. Prophet Mohamad

5. Ovary (A Saudi Religious figure had at some point indicated that if women drive it will damage their ovaries and their ability to have children).

6 Thelma and Louise

7 Bullying the Supporters of the campaign

8 Opposition to the Campaign

9 Campaign Launch

10 Driving ban does not equal to racism in the US

11 Random (too small to identify as a conversation)

Having counted the tweets the size of the conversation produced the following info graph (thanks to Ali’s help here):

dalia_bubble (1)The tweets and tweet recounts produced quite an interesting observation and while people would expect the event of women driving to garner the most ‘noise’, as it turns out a Saudi twist on a Bob Marley song was the most popular topic.

There are a lot more stories in these data and I’m hoping to continue exploring them with Ali and see what comes up!

Note: The data scraped follows the Twitter API rule, which means that the data is only 1% of all tweets around a specific query.

 

Posted in All

Dalia’s Media Diary

For my data collection, I decided to use the application Rescue Time. Rescue Time is ideally used to enhance productivity and manage the amount of time spent on ‘disruptive’ sites. The user can indicate a set of productivity goals with a set of sites that would be considered disruptive and calculate productivity on a daily basis based on media consumption.

 I, on the other hand decided not to set any goals and just record my consumption based on sites visited. I do have to note that I have reservations regarding the pre-defined categories in the application, especially when Twitter and Facebook are considered disruptive sites. I find this problematic, not only because I use both platforms to conduct research, but also because I find that both sites can be valuable sources of information and news. Let alone the fact that other forms of media, such as daily interactions and talks are obviously not recorded and thus not represented in this data set. My other reservation is the fact that I can pause the application and stop recording any media I consume. I have to admit I used it on multiple occasions, which gives a skewed result of how many hours I was online and the media I consumed. Regardless, I found quite interesting results and patterns in the data set which I present below.

When I first thought of doing this assignment, I thought of creating a time lapse video of the sites I consumed using the data I collected from Rescue Time. Unfortunately, I face a number of hurdles, one being that the data collected was difficult to translate into a video. More importantly my search for a tool that would be able to create this time lapse proved futile. I tested out Popcorn Maker, (I recalled someone once explaining how easy it was to use, but also the ability to add comments to the videos) However, when I signed up and started using it I realized that I can only create a video from already published media on the web. Not wanting to duplicate some steps and publish material online to then use for a video, I decided to use iMovie, which I surprisingly had never used (Final Cut Pro was my program of choice.) As I started to compile images and graphs onto the program, only to realize that I couldn’t truly translate what I wanted to do with the data using this program.

I eventually, and for the sake of time decided to stick with an info-graph which you can find HERE. I used the site infogr.am to create this graph, it is one of the many tools that are available on the web that can create info-graphics from raw data.

In addition to the info-graph I created, I decided to look into and present my daily consumption which varied from day to day, especially on the weekends.

Day 1 – Wednesday, not a complete report since I started Rescue Time in the afternoon.

Wednesday

Day 2: Thursday

Thursday

Day 3: Friday

Friday

Day 4: Saturday

Saturday

Day 5: Sunday

Sunday

Day 6: Monday

Monday

Day 7: Tuesday

Tuesday

What became a noticeable media pattern is that I was consuming more social media in the early morning and late night during the day. Whereas during the day I spent most of my time on e-mail, scheduling and ‘learning’ sites.

Over the weekend I noticed that I had more time to catch up on the news. I have to admit as someone who considers themselves a news junkie, the fact that I spent so little time reading the news was shocking. Although, my news reading behavior has changed in recent years and I’ve started to rely more on social media as new source.

Having noted all those observations, I would still place a disclaimer and indicate that the data collected by Rescue Time is not entirely accurate and does not indicate time spent at talks, watching a film or media consumed on my phone.