Job searches are these gender-coded? Yes.
As I write this, I am checking in with a little-known job gender-decoder designed by the brilliant Kat Matfiled @LovedayBrooke and inspired by the work of Danielle Gaucher, Justin Friesen, and Aaron C. Kay - Evidence That Gendered Wording in Job Advertisements Exists and Sustains Gender Inequality.
Gaucher and friends were interested in researching job adverts to analyse the types of words used in job descriptions and role specifications to assess the effect on potential candidates and ask how closely a candidate would feel they align with the role criteria.
Did the candidate 'belong'? And what types of classification affected the appearance and groupings of words?
If you follow the above link, you can copy and paste job advertisement text to decode by masculine and feminine coded words. You can also have fun and paste in large chunks of 'any text' - I've experimented with advertisements (predominantly masculine), email marketing (feminine), loan reports (masculine), NHS health information (feminine), the Daily Mail (masculine)... Interesting.
The mechanism for analysis here is rather crude. Still, we stereotype with and through language all the time, language is distinctly gender bias, and language endures as one of the most common mechanisms through which sexism and gender discrimination are reproduced.
Gaucher, D., Friesen, J. and Kay, A.C., 2011. Evidence that gendered wording in job advertisements exists and sustains gender inequality. Journal of personality and social psychology, 101(1), p.109.
I teach about digital surveillance and while I want my students to be able to 'write essays', I would prefer that they got to the end of teaching feeling unsettled and a bit uncomfortable about social media and with the ability to do something about this.
In the past, at the beginning of the term, it was common to receive a list of students alongside their passport photograph. To open up the first session on data mining, I'd ask if anyone was prepared to type their name into Google and see what we'd find together. As social media and digital data have proliferated, I do not feel comfortable with this exercise anymore, though an experiment with my pups names "Luna Maximuff [add extra surnames here]" threw into the ether some interesting insights, so perhaps pet name searches are the way forward.
Recent issues are about data legacy and unused accounts that still contain personal information. A high proportion of my international taught students only have Facebook while they are in the UK. For this group, it is important they understand this information does not 'magically' disappear when they stop using the platform.
Tasks I get my students to do if they feel comfortable:
- deactivate social media accounts for at least one-week;
- do an image search of their name;
- set up a Google alert of their family name;
- review all privacy settings on all devices and all apps (this one takes ages, but is effective);
- report back.
In the future (now) we will be paying digital experts to track down and modify our digital data for us.
Brilliant reading in this area:
Beer, D., 2018. Envisioning the power of data analytics. Information, Communication & Society, 21(3), pp.465-479.
Pötzsch, H., 2018. Archives and identity in the context of social media and algorithmic analytics: Towards an understanding of iArchive and predictive retention. New Media & Society, 20(9), pp.3304-3322.
Cohen, J.E., 2012. What privacy is for. Harv. L. Rev., 126, p.1904.
The Gram = Instagram
Social media are user-generated-content (UGC) and this makes them rather interesting in understanding the human condition, surfing internet images of cats and acknowledging the role of distraction in our lives.
Analysing images and text on social media is becoming increasingly tricky and a game of cat and mouse. Platforms change ownership, update APIs* (how applications talk to each other), and enable spontaneous new forms of interaction simply by being there, hello #hashtag.
Recently, I've fallen down the rabbit hole of learning (very basic) natural language processing (NLP) using Python. What is interesting is how quickly you can pull in a corpus of text (basically a sandbox of text signifiers and classifications) to understand associations from social media content. Now the system is not foolproof, so the reliability and validity of such results are at stake, however, this does allow tentative review of the kinds of content being shared. My investigations analyse some of the most popular #hashtags on 'The Gram'
#sunset, #style, #money, #healthyfood, #photography, #WTF, #brand, #recipeoftheday #bekind, #travel, #fitness, #cats (they get everywhere).
Close associations to the above follow a pattern of overt and tedious marketing content (yawn), aspirational 'stuff' designed to make you feel inadequate, the acknowledgement of the rougher edges of social media and need to #bekind, along with a range of emoticons and slang. Lovely stuff.
smsdictionary (I need this one, I did not know an aubergine meant that...)
Some bright sparks (Asghar and friends) have pulled together a partial list of slangs with their sentiment class:
Coolio - Cool - Positive
gr8 - Great - Positive
Xoxo - Hugs and kisses - Positive
Air - Alright - Positive
Happs- Happy - Positive
Smh - Shaking my head - Negative
Damn - Disbelief/condemn - Negative
Hehehe - Laughing - Negative
Notta - Not - Negative
Chale - Disagreement/disproval - Negative
Gonna - Want to go - Neutral
Haha - Laughing - Neutral
RoflRofl - rolling on floor laughing - Neutral
Wanna - Want to - Neutral
The main challenge is context. 'Hehehe' vs 'haha' are very context driven, as is 'smh' - ah the joy of sarcasm. I endevour to continue to build my own directory of social media slanguage, picking up on ways we modify our understanding and enable contextual use of algorithms to classify and unpack meaning.
*application programming interface (API)
Asghar MZ, Kundi FM, Ahmad S, Khan A, Khan F. T‐SAF: Twitter sentiment analysis framework using a hybrid classification scheme. Expert Systems. 2018 Feb;35(1):e12233.