# An Ordinal Latent Variable Model of Conflict Intensity

arXiv: https://arxiv.org/abs/2210.03971 · HTML: https://mlsanity.com/paper/2210.03971/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 68 references that are in the map.
- First posted on arXiv 8 Oct 2022.

## Builds on (most cited first, 12 of 12)

- [Pyro: Deep Universal Probabilistic Programming](https://mlsanity.com/paper/1810.09538/) (2018)
- [Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro](https://mlsanity.com/paper/1912.11554/) (2019)
- [Understanding predictive information criteria for Bayesian models](https://mlsanity.com/paper/1307.5928/) (2013)
- [Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations](https://mlsanity.com/paper/1606.01855/) (2016)
- [Bayesian Poisson Tensor Factorization for Inferring Multilateral Relations from Sparse Dyadic Event Counts](https://mlsanity.com/paper/1506.03493/) (2015)
- [Inferring Interpersonal Relations in Narrative Summaries](https://mlsanity.com/paper/1512.00112/) (2015)
- [Poisson-Randomized Gamma Dynamical Systems](https://mlsanity.com/paper/1910.12991/) (2019)
- [No Permanent Friends or Enemies: Tracking Relationships between Nations from News](https://mlsanity.com/paper/1904.08950/) (2019)
- [Classifying Dyads for Militarized Conflict Analysis](https://mlsanity.com/paper/2109.12860/) (2021)
- [Calibration of Google Trends Time Series](https://mlsanity.com/paper/2007.13861/) (2020)
- [SeismographAPI: Visualising Temporal-Spatial Crisis Data](https://mlsanity.com/paper/2107.12443/) (2021)
- [Creating a Real-Time, Reproducible Event Dataset](https://mlsanity.com/paper/1612.00866/) (2016)

## Built on by (most cited first, 6 of 6)

- [Context versus Prior Knowledge in Language Models](https://mlsanity.com/paper/2404.04633/) (2024)
- [Understanding Online Migration Decisions Following the Banning of Radical Communities](https://mlsanity.com/paper/2212.04765/) (2022)
- [Rethinking the Event Coding Pipeline with Prompt Entailment](https://mlsanity.com/paper/2210.05257/) (2022)
- [The Ordered Matrix Dirichlet for State-Space Models](https://mlsanity.com/paper/2212.04130/) (2022)
- [A Monte Carlo Language Model Pipeline for Zero-Shot Sociopolitical Event Extraction](https://mlsanity.com/paper/2305.15051/) (2023)
- [Extracting Victim Counts from Text](https://mlsanity.com/paper/2302.12367/) (2023)

## Abstract (arXiv)

Measuring the intensity of events is crucial for monitoring and tracking armed conflict. Advances in automated event extraction have yielded massive data sets of "who did what to whom" micro-records that enable data-driven approaches to monitoring conflict. The Goldstein scale is a widely-used expert-based measure that scores events on a conflictual-cooperative scale. It is based only on the action category ("what") and disregards the subject ("who") and object ("to whom") of an event, as well as contextual information, like associated casualty count, that should contribute to the perception of an event's "intensity". This paper takes a latent variable-based approach to measuring conflict intensity. We introduce a probabilistic generative model that assumes each observed event is associated with a latent intensity class. A novel aspect of this model is that it imposes an ordering on the classes, such that higher-valued classes denote higher levels of intensity. The ordinal nature of the latent variable is induced from naturally ordered aspects of the data (e.g., casualty counts) where higher values naturally indicate higher intensity. We evaluate the proposed model both intrinsically and extrinsically, showing that it obtains comparatively good held-out predictive performance.