# It's All in the Name: A Character Based Approach To Infer Religion

arXiv: https://arxiv.org/abs/2010.14479 · HTML: https://mlsanity.com/paper/2010.14479/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2020.

## Built on by (most cited first, 5 of 5)

- [Generative AI in EU Law: Liability, Privacy, Intellectual Property, and Cybersecurity](https://mlsanity.com/paper/2401.07348/) (2024)
- [DADIT: A Dataset for Demographic Classification of Italian Twitter Users and a Comparison of Prediction Methods](https://mlsanity.com/paper/2403.05700/) (2024)
- [Ontology of Belief Diversity: A Community-Based Epistemological Approach](https://mlsanity.com/paper/2408.01455/) (2024)
- [Bridging or Breaking: Impact of Intergroup Interactions on Religious Polarization](https://mlsanity.com/paper/2402.11895/) (2024)
- [Differentiating Emigration from Return Migration of Scholars Using Name-Based Nationality Detection Models](https://mlsanity.com/paper/2505.06107/) (2025)

## Abstract (arXiv)

Demographic inference from text has received a surge of attention in the field of natural language processing in the last decade. In this paper, we use personal names to infer religion in South Asia - where religion is a salient social division, and yet, disaggregated data on it remains scarce. Existing work predicts religion using dictionary based method, and therefore, can not classify unseen names. We use character based models which learn character patterns and, therefore, can classify unseen names as well with high accuracy. These models are also much faster and can easily be scaled to large data sets. We improve our classifier by combining the name of an individual with that of their parent/spouse and achieve remarkably high accuracy. Finally, we trace the classification decisions of a convolutional neural network model using layer-wise relevance propagation which can explain the predictions of complex non-linear classifiers and circumvent their purported black box nature. We show how character patterns learned by the classifier are rooted in the linguistic origins of names.