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

arXiv: https://arxiv.org/abs/1702.01807 · HTML: https://mlsanity.com/paper/1702.01807/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2017.

## Built on by (most cited first, 2 of 2)

- [Social Analysis of Young Basque Speaking Communities in Twitter](https://mlsanity.com/paper/2109.03487/) (2021)
- [The Anatomy of Conspirators: Unveiling Traits using a Comprehensive Twitter Dataset](https://mlsanity.com/paper/2308.15154/) (2023)

## Abstract (arXiv)

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.