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:
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).
7 Bullying the Supporters of the campaign
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):
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.