When I initially scraped the web for data using Twitter Archiver, I had very general search rules like #GenderEquality and #LikeAGirl, and within a few minutes I had 10,000+ tweets which was completely overwhelming. Scrolling through some of the results a lot of it covered a broad range of the issue and many were re-tweets. My next search I narrowed it to tweets containing boys, girls, gender equality and/or the words feminine, masculine, masculinity, femininity, youth or adolescence. This search produced less results but because I had only had the search up for a few minutes the results were retweets.
Once I had gotten the hang of how Twitter Archiver works, I thought it would be interesting to see how people described or tweeted about Hillary Clinton in relation to her hair, makeup, and looks as this has been highlighted especially in the most recent Rio Olympics with female athletes. Then my next search I changed it to Donald Trump keeping the same words ‘hair’, ‘makeup’, ‘look’ and ‘pretty’. The results were not what I was expecting and once looking at the data and comparing the tweets from Hillary to Trump, there was no obvious patterns I noticed in how people tweet about the two.
My findings were:
- People take to twitter to call out sexist and minsogynst remarks made by people in the media – crucial actors in the issue of Gender Equality, and look to challenge their perspective. In this case, many criticised Donald Trump for saying that Hillary Clinton doesn’t have that ‘Presidential Look’ – which many pointed out was because she is a Woman, and all others before her were male.
- Looking specifically at the American presidential candidates Hillary Clinton and Donald Trump, one of the main things that was highlighted in this scraping was the double standards that exist between the two, specifically with Hillary (which she speaks about in her latest Humans of New York interview), who the chairman of the Republican National Committee Reince Priebus told needs to ‘smile more’. The results revealed that people were not down for this comment which seems to only be used toward women.
- Many people followed up their tweets to links to reputable articles from The Huffington Post, the New York Times and Think Progress. This was actually very beneficial for me as I found some interesting articles about Gender Equality throughout the presidential campaign.
- Using conditional formatting in google sheets, I was able to sort my results by each word I added in the search rule. It produced the following results:Trump: 9pgs of results for ‘hair’, 1/4 pg of results for makeup, 2pgs of results for Presidential Look
Hillary: 5pgs of results for hair, 1/2pg of results for makeup, 24pgs of results for Presidential Look
- While looking at the tweets it was obvious who was a Trump or Hillary supporter, and in a lot of the tweets I highlighted, I was able to find influential stakeholders/accounts for each candidate. This could be very useful in finding and understanding crucial stakeholders for the demographic 18-25 for my design proposal.
Additionally, I learnt that search rules should be run over the course of a few days or at least a few hours. Because I only had the free version of Twitter Archiver I could only run one rule at a time, and as I wanted to compare the data as soon as possible, I only let the search run for 5 minutes. Therefore it generated results from the time that I started the search rule, up until two days prior to it. However, if I had left it over the course of a few hours it would search once per hour.
Rampell, C. ‘How Hillary Clinton can get that ‘presidential look’ http://wpo.st/fsww1 thank you,
@petridishes.’ Twitter post, 8 September, viewed 10 September 2016, <https://twitter.com/crampell/status/773734957890101248>.
Ruberry, E. ‘”Smile more! Be more upbeat!” Hillary Clinton is running for President, not Miss America.’ Twitter post, 8 September, viewed 10 September 2016, <https://twitter.com/erinruberry/status/773695964532080640>.
Ryan, E. ‘Imagine being an adult in 2016 and still not knowing how obnoxious women find it when you tell women they should smile’ Twitter post, 8 September, viewed 10 September 2016, <https://twitter.com/morninggloria/status/773696271131574273>.