Advance Gender Prediction Tool of First Names and its Use in Analysing Gender Disparity in Computer Science in the UK, Malaysia and China

Hua Zhao; Fairouz Kamareddine

doi:10.1109/CSCI.2017.35

Advance Gender Prediction Tool of First Names and its Use in Analysing Gender Disparity in Computer Science in the UK, Malaysia and China

Hua Zhao, Fairouz Kamareddine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 Citations (Scopus)

Abstract

Global gender disparity in science is an unsolved problem. Predicting gender has an important role in analysing the gender gap through online data. We study this problem within the UK, Malaysia and China. We enhance the accuracy of an existing gender prediction tools of names that can predict the sex of Chinese characters and English characters simultaneously and with more precision. During our research, we found that there is no free gender forecasting tool to predict an arbitrary number of names. We addressed this shortcoming by providing a tool that can predict an arbitrary number of names with free requests. We demonstrate our tool through a number of experimental results. We show that this tool is better than other gender prediction tools of names for analysing social problems with big data. In our approach, lists of data can be dynamically processed and the results of the data can be displayed with a dynamic graph. We present experiments of using this tool to analyse the gender disparity in computer science in the UK, Malaysia and China.

Original language	English
Title of host publication	2017 International Conference on Computational Science and Computational Intelligence (CSCI)
Publisher	IEEE
Pages	222-227
Number of pages	6
ISBN (Electronic)	9781538626528
DOIs	https://doi.org/10.1109/CSCI.2017.35
Publication status	Published - 6 Dec 2018
Event	2017 International Conference on Computational Science and Computational Intelligence - Las Vegas, United States Duration: 14 Dec 2017 → 16 Dec 2017

Conference

Conference	2017 International Conference on Computational Science and Computational Intelligence
Abbreviated title	CSCI 2017
Country/Territory	United States
City	Las Vegas
Period	14/12/17 → 16/12/17

Keywords

Data research
Gender disparity
Gender prediction of names

ASJC Scopus subject areas

Computer Science (miscellaneous)
Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Safety, Risk, Reliability and Quality

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/CSCI.2017.35

Cite this

@inproceedings{b0383e4640284d9fa07732f099bedcf0,

title = "Advance Gender Prediction Tool of First Names and its Use in Analysing Gender Disparity in Computer Science in the UK, Malaysia and China",

abstract = "Global gender disparity in science is an unsolved problem. Predicting gender has an important role in analysing the gender gap through online data. We study this problem within the UK, Malaysia and China. We enhance the accuracy of an existing gender prediction tools of names that can predict the sex of Chinese characters and English characters simultaneously and with more precision. During our research, we found that there is no free gender forecasting tool to predict an arbitrary number of names. We addressed this shortcoming by providing a tool that can predict an arbitrary number of names with free requests. We demonstrate our tool through a number of experimental results. We show that this tool is better than other gender prediction tools of names for analysing social problems with big data. In our approach, lists of data can be dynamically processed and the results of the data can be displayed with a dynamic graph. We present experiments of using this tool to analyse the gender disparity in computer science in the UK, Malaysia and China.",

keywords = "Data research, Gender disparity, Gender prediction of names",

author = "Hua Zhao and Fairouz Kamareddine",

year = "2018",

month = dec,

day = "6",

doi = "10.1109/CSCI.2017.35",

language = "English",

pages = "222--227",

booktitle = "2017 International Conference on Computational Science and Computational Intelligence (CSCI)",

publisher = "IEEE",

address = "United States",

note = "2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017 ; Conference date: 14-12-2017 Through 16-12-2017",

}

Zhao, H & Kamareddine, F 2018, Advance Gender Prediction Tool of First Names and its Use in Analysing Gender Disparity in Computer Science in the UK, Malaysia and China. in 2017 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, pp. 222-227, 2017 International Conference on Computational Science and Computational Intelligence, Las Vegas, Nevada, United States, 14/12/17. https://doi.org/10.1109/CSCI.2017.35

Advance Gender Prediction Tool of First Names and its Use in Analysing Gender Disparity in Computer Science in the UK, Malaysia and China. / Zhao, Hua; Kamareddine, Fairouz.
2017 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2018. p. 222-227.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Advance Gender Prediction Tool of First Names and its Use in Analysing Gender Disparity in Computer Science in the UK, Malaysia and China

AU - Zhao, Hua

AU - Kamareddine, Fairouz

PY - 2018/12/6

Y1 - 2018/12/6

N2 - Global gender disparity in science is an unsolved problem. Predicting gender has an important role in analysing the gender gap through online data. We study this problem within the UK, Malaysia and China. We enhance the accuracy of an existing gender prediction tools of names that can predict the sex of Chinese characters and English characters simultaneously and with more precision. During our research, we found that there is no free gender forecasting tool to predict an arbitrary number of names. We addressed this shortcoming by providing a tool that can predict an arbitrary number of names with free requests. We demonstrate our tool through a number of experimental results. We show that this tool is better than other gender prediction tools of names for analysing social problems with big data. In our approach, lists of data can be dynamically processed and the results of the data can be displayed with a dynamic graph. We present experiments of using this tool to analyse the gender disparity in computer science in the UK, Malaysia and China.

AB - Global gender disparity in science is an unsolved problem. Predicting gender has an important role in analysing the gender gap through online data. We study this problem within the UK, Malaysia and China. We enhance the accuracy of an existing gender prediction tools of names that can predict the sex of Chinese characters and English characters simultaneously and with more precision. During our research, we found that there is no free gender forecasting tool to predict an arbitrary number of names. We addressed this shortcoming by providing a tool that can predict an arbitrary number of names with free requests. We demonstrate our tool through a number of experimental results. We show that this tool is better than other gender prediction tools of names for analysing social problems with big data. In our approach, lists of data can be dynamically processed and the results of the data can be displayed with a dynamic graph. We present experiments of using this tool to analyse the gender disparity in computer science in the UK, Malaysia and China.

KW - Data research

KW - Gender disparity

KW - Gender prediction of names

UR - http://www.scopus.com/inward/record.url?scp=85060575088&partnerID=8YFLogxK

U2 - 10.1109/CSCI.2017.35

DO - 10.1109/CSCI.2017.35

M3 - Conference contribution

AN - SCOPUS:85060575088

SP - 222

EP - 227

BT - 2017 International Conference on Computational Science and Computational Intelligence (CSCI)

PB - IEEE

T2 - 2017 International Conference on Computational Science and Computational Intelligence

Y2 - 14 December 2017 through 16 December 2017

ER -

Advance Gender Prediction Tool of First Names and its Use in Analysing Gender Disparity in Computer Science in the UK, Malaysia and China

Abstract

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this