How well can machine learning predict demographics of social media users? release_pirwptqbsvfmbp57axlqxh3ole

by Nina Cesare, Christan Grant, Quynh Nguyen, Hedwig Lee, Elaine O. Nsoesie

Released as a article .

2017  

Abstract

The wide use of social media sites and other digital technologies have resulted in an unprecedented availability of digital data that are being used to study human behavior across research domains. Although unsolicited opinions and sentiments are available on these platforms, demographic details are usually missing. Demographic information is pertinent in fields such as demography and public health, where significant differences can exist across sex, racial and socioeconomic groups. In an attempt to address this shortcoming, a number of academic studies have proposed methods for inferring the demographics of social media users using details such as names, usernames, and network characteristics. Gender is the easiest trait to accurately infer, with measures of accuracy higher than 90 percent in some studies. Race, ethnicity and age tend to be more challenging to predict for a variety of reasons including the novelty of social media to certain age groups and a lack of significant deviations in user details across racial and ethnic groups. Although the endeavor to predict user demographics is plagued with ethical questions regarding privacy and data ownership, knowing the demographics in a data sample can aid in addressing issues of bias and population representation, so that existing societal inequalities are not exacerbated.
In text/plain format

Archived Files and Locations

application/pdf  304.9 kB
file_qigydaal5nbgdacmd2l5sy3cxq
web.archive.org (webarchive)
arxiv.org (repository)
Read Archived PDF
Archived
Type  article
Stage   submitted
Date   2017-02-06
Version   v1
Language   en ?
arXiv  1702.01807v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: a1f8bb67-878b-4c11-a927-2da2ebb312b2
API URL: JSON