Dissertation Part 1

I was a very good girl, and pitched up on campus to a pre-booked study room in the library by 10.00am this morning.  I can work from home, but I can’t always access research papers as it’s impossible to log into the VLE (Virtual Learning Environment) from here.  Plus, there are less distractions.

One of the first things I did was set up a Notebook in OneNote – I use the cloud for all my document storage these days via Microsoft’s OneDrive (which is like Dropbox, Evernote or GoogleDocs).  I’m planning to keep all my notes and links there so that I can access them from various devices.  I started casting around for some background reading on feminism, and some definitions of misogyny.

Collins English Dictionary: noun.  Defined simply as ‘hatred of women’.  Word origin: C17: from Greek, from miso- + gunē woman.

OED: noun.  Dislike of, contempt for, or ingrained prejudice against women:she felt she was struggling against thinly disguised misogyny.  Mid 17th century: from Greek misos ‘hatred’ + gunē ‘woman’.

Wikipedia: Misogyny (/mɪˈsɒdʒɪni/) is the hatred or dislike of women or girls. Misogyny can be manifested in numerous ways, including sexual discrimination, belittling of women,violence against women, and sexual objectification of women.  Misogyny can be found within many mythologies of the ancient world as well as various religions. In addition, various influential Western philosophers have been described as misogynistic. (From <https://en.wikipedia.org/?title=Misogyny>)

‘Hatred’ seems a bit strong in some contexts.  Not everyone who uses misogynist language would agree that they hate women.  And yet….. The meaning is clear.  Is it hatred or is it fear?  This led me to look at some feminist theory on the same Wikipedia page, something I’ll have to go back to.  I’ve also asked one of my previous lecturers for some names of researchers to help me.

I also downloaded and installed an amazing little add-on for excel spreadsheets called NodeXL.  This amazing little app allows anyone to download thousands of tweets from twitter (or less, you can tweak the amount to suit) and will put all the tweets and corresponding data (user name, ID, date and time of tweet etc.) into a spreadsheet.  I’ve only used it to get two sets of data – my own tweets going back to January this year, and 1000 tweets containing the keyword ‘whore’.  Yes, extreme I know but I had to start somewhere!  It took a few minutes to download the tweets, which I did from home onto my laptop.  It would obviously be better if I did the downloads on campus because the download speed would be faster.  Also, I’ve got to get ethics approval before I can do anything official.  Even though all the tweets I’ve scraped are public, it’s still pretty scary being able to access so much so easily.

Having scraped the tweets, I noticed that I’ve got various ways of analysing the data further.  I wrote some notes:

  • How do the different algorithms work?
  • I used a keyword, which automatically selects the tweets I’m interested in.  The drawback is, I want to monitor ALL tweets over, say, a short time period OR download the latest 1000 and then analyse them looking for keywords.  Can I do that?
  • ‘Everything’ doesn’t actually mean ‘everything’.
  • There are problems with keywords.
  • If I were to remove, say, the twitter accounts that are doing nothing more than promoting links to porn, or retweeting themselves, does it matter that I’ve altered the raw data?  How much does it matter?

And so my next task is to find out what NodeXL can actually do, which bits are going to be the most useful, and the drawbacks and limitations.  It does turn the data into some pretty graphs, though!

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