• Ana-Maria Huluba, Jason Kingdon, and Iain McLaren.
• Glass, London, United Kingdom.
• Heliyon. 2018 Dec 1; 4 (12): e01001.

AbstractGender inequality has exploded as a recent issue within mainstream media across US and UK cultural commentary. High-profile scandals of sexual harassment and gender pay differences have focused attention on the on-going disparity between sexes and political status. This paper presents a novel experiment in the application of so-called "big data" to analyse gender inequality. Using Artificial Intelligence (AI) techniques in the form of Natural Language Processing, a web crawler is used to audit the whole.uk online domain, and to measure the United Kingdom's (UK's) online economic presence for gender representation in terms of: prominence, job roles, and leadership within and across economic sectors. The procedure scans over 200 million web pages, and harvests 157,032 organisations and over 2.3 million people. The results reveal material bias (60%+) towards the representation of men over the majority of economic sectors, and across representation of power and status within job roles and professional titles. The experiment highlights not only new levels of gender bias but also the use of the Internet as a valuable source of plentiful data for social and economic analysis.

2 keywords...

hide…