Gender Inference using Statistical Name Characteristics in Twitter

06/17/2016
by   Juergen Mueller, et al.
0

Much attention has been given to the task of gender inference of Twitter users. Although names are strong gender indicators, the names of Twitter users are rarely used as a feature; probably due to the high number of ill-formed names, which cannot be found in any name dictionary. Instead of relying solely on a name database, we propose a novel name classifier. Our approach extracts characteristics from the user names and uses those in order to assign the names to a gender. This enables us to classify international first names as well as ill-formed names.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/22/2017

Predicting the Gender of Indonesian Names

We investigated a way to predict the gender of a name using character-le...
research
06/28/2017

Generating Appealing Brand Names

Providing appealing brand names to newly launched products, newly formed...
research
06/10/2019

The Online Resources Shared on Twitter About the #MeToo Movement: The Pareto Principle

In this paper we examine the information sharing behavior of Twitter use...
research
06/18/2021

Predicting gender of Brazilian names using deep learning

Predicting gender by the name is not a simple task. In many applications...
research
12/15/2017

Scholars on Twitter: who and how many are they?

In this paper we present a novel methodology for identifying scholars wi...
research
07/08/2020

Understanding the impact of the alphabetical ordering of names in user interfaces: a gender bias analysis

Listing people alphabetically on an electronic output device is a tradit...
research
05/06/2019

Anonymized BERT: An Augmentation Approach to the Gendered Pronoun Resolution Challenge

We present our 7th place solution to the Gendered Pronoun Resolution cha...

Please sign up or login with your details

Forgot password? Click here to reset