# Using Noisy Self-Reports to Predict Twitter User Demographics

arXiv: https://arxiv.org/abs/2005.00635 · HTML: https://mlsanity.com/paper/2005.00635/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2020.

## Built on by (most cited first, 8 of 8)

- [Gender and Racial Fairness in Depression Research using Social Media](https://mlsanity.com/paper/2103.10550/) (2021)
- [Emojis Decoded: Leveraging ChatGPT for Enhanced Understanding in Social Media Communications](https://mlsanity.com/paper/2402.01681/) (2024)
- [Towards Algorithmic Fidelity: Mental Health Representation across Demographics in Synthetic vs. Human-generated Data](https://mlsanity.com/paper/2403.16909/) (2024)
- [User Factor Adaptation for User Embedding via Multitask Learning](https://mlsanity.com/paper/2102.11103/) (2021)
- [Does Geo-co-location Matter? A Case Study of Public Health Conversations during COVID-19](https://mlsanity.com/paper/2405.17710/) (2024)
- [Improving Noise Robustness through Abstractions and its Impact on Machine Learning](https://mlsanity.com/paper/2406.08428/) (2024)
- [Analyzing the Evolution of Graphs and Texts](https://mlsanity.com/paper/2411.06295/) (2024)
- [Splits! Flexible Sociocultural Linguistic Investigation at Scale](https://mlsanity.com/paper/2504.04640/) (2025)

## Abstract (arXiv)

Computational social science studies often contextualize content analysis within standard demographics. Since demographics are unavailable on many social media platforms (e.g. Twitter) numerous studies have inferred demographics automatically. Despite many studies presenting proof of concept inference of race and ethnicity, training of practical systems remains elusive since there are few annotated datasets. Existing datasets are small, inaccurate, or fail to cover the four most common racial and ethnic groups in the United States. We present a method to identify self-reports of race and ethnicity from Twitter profile descriptions. Despite errors inherent in automated supervision, we produce models with good performance when measured on gold standard self-report survey data. The result is a reproducible method for creating large-scale training resources for race and ethnicity.