# Integrating topic modeling and word embedding to characterize violent deaths

arXiv: https://arxiv.org/abs/2106.14365 · HTML: https://mlsanity.com/paper/2106.14365/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 62 references that are in the map.
- First posted on arXiv 28 Jun 2021.

## Builds on (most cited first, 6 of 6)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes](https://mlsanity.com/paper/1711.08412/) (2017)
- [Topic Modeling in Embedding Spaces](https://mlsanity.com/paper/1907.04907/) (2019)
- [Linear Algebraic Structure of Word Senses, with Applications to Polysemy](https://mlsanity.com/paper/1601.03764/) (2016)
- [Machine learning as a model for cultural learning: Teaching an algorithm what it means to be fat](https://mlsanity.com/paper/2003.12133/) (2020)

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

- [Theoretical foundations and limits of word embeddings: what types of meaning can they capture?](https://mlsanity.com/paper/2107.10413/) (2021)
- [Human Languages with Greater Information Density Increase Communication Speed, but Decrease Conversation Breadth](https://mlsanity.com/paper/2112.08491/) (2021)
- [A Topic Modeling Analysis of Stigma Dimensions, Social, and Related Behavioral Circumstances in Clinical Notes Among Patients with HIV](https://mlsanity.com/paper/2506.09279/) (2025)
- [The study of short texts in digital politics: Document aggregation for topic modeling](https://mlsanity.com/paper/2503.05065/) (2025)
- [Big Data and the Computational Social Science of Entrepreneurship and Innovation](https://mlsanity.com/paper/2505.08706/) (2025)
- [Identifying social isolation themes in NVDRS text narratives using topic modeling and text-classification methods](https://mlsanity.com/paper/2506.15030/) (2025)
- [Automating Historical Insight Extraction from Large-Scale Newspaper Archives via Neural Topic Modeling](https://mlsanity.com/paper/2512.11635/) (2025)
- [Method, Mind, and Morality: How People Make Sense of Artificial Intelligence](https://mlsanity.com/paper/2608.24748/) (2026)

## Abstract (arXiv)

There is an escalating need for methods to identify latent patterns in text data from many domains. We introduce a new method to identify topics in a corpus and represent documents as topic sequences. Discourse Atom Topic Modeling draws on advances in theoretical machine learning to integrate topic modeling and word embedding, capitalizing on the distinct capabilities of each. We first identify a set of vectors ("discourse atoms") that provide a sparse representation of an embedding space. Atom vectors can be interpreted as latent topics: Through a generative model, atoms map onto distributions over words; one can also infer the topic that generated a sequence of words. We illustrate our method with a prominent example of underutilized text: the U.S. National Violent Death Reporting System (NVDRS). The NVDRS summarizes violent death incidents with structured variables and unstructured narratives. We identify 225 latent topics in the narratives (e.g., preparation for death and physical aggression); many of these topics are not captured by existing structured variables. Motivated by known patterns in suicide and homicide by gender, and recent research on gender biases in semantic space, we identify the gender bias of our topics (e.g., a topic about pain medication is feminine). We then compare the gender bias of topics to their prevalence in narratives of female versus male victims. Results provide a detailed quantitative picture of reporting about lethal violence and its gendered nature. Our method offers a flexible and broadly applicable approach to model topics in text data.