# Mixed Multi-Model Semantic Interaction for Graph-based Narrative Visualizations

arXiv: https://arxiv.org/abs/2302.06452 · HTML: https://mlsanity.com/paper/2302.06452/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 74 references that are in the map.
- First posted on arXiv 13 Feb 2023.

## Builds on (most cited first, 14 of 14)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [We Can Detect Your Bias: Predicting the Political Ideology of News Articles](https://mlsanity.com/paper/2010.05338/) (2020)
- [Evaluation of Interactive Machine Learning Systems](https://mlsanity.com/paper/1801.07964/) (2018)
- [Growing Story Forest Online from Massive Breaking News](https://mlsanity.com/paper/1803.00189/) (2018)
- [Design guidelines for narrative maps in sensemaking tasks](https://mlsanity.com/paper/2112.12205/) (2021)
- [Socially-Informed Timeline Generation for Complex Events](https://mlsanity.com/paper/1606.05699/) (2016)
- [Identifying Partisan Slant in News Articles and Twitter during Political Crises](https://mlsanity.com/paper/1703.05819/) (2017)
- [Analyzing Evolving Stories in News Articles](https://mlsanity.com/paper/1703.08593/) (2017)
- [DeepVA: Bridging Cognition and Computation through Semantic Interaction and Deep Learning](https://mlsanity.com/paper/2007.15800/) (2020)
- [Narrative Sensemaking: Strategies for Narrative Maps Construction](https://mlsanity.com/paper/2108.06035/) (2021)
- [Evaluating Semantic Interaction on Word Embeddings via Simulation](https://mlsanity.com/paper/2007.15824/) (2020)
- [Adding Semantic Information into Data Models by Learning Domain Expertise from User Interaction](https://mlsanity.com/paper/1604.02935/) (2016)

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

- [Interactive Narrative Analytics: Bridging Computational Narrative Extraction and Human Sensemaking](https://mlsanity.com/paper/2601.11459/) (2026)
- [Semantic Interaction for Narrative Map Sensemaking: An Insight-based Evaluation](https://mlsanity.com/paper/2603.29651/) (2026)
- [Explainable AI Components for Narrative Map Extraction](https://mlsanity.com/paper/2503.16554/) (2025)
- [Agenda-based Narrative Extraction: Steering Pathfinding Algorithms with Large Language Models](https://mlsanity.com/paper/2603.29661/) (2026)
- [LLM-Augmented Semantic Steering of Text Embedding Projection Spaces](https://mlsanity.com/paper/2605.01957/) (2026)
- [Evaluating the Ability of Computationally Extracted Narrative Maps to Encode Media Framing](https://mlsanity.com/paper/2405.02677/) (2024)
- [A Directed Graph Model and Experimental Framework for Design and Study of Time-Dependent Text Visualisation](https://mlsanity.com/paper/2603.02422/) (2026)
- [Information Terra: A Narrative-Anchored Semantic-First Projection of Document Embeddings](https://mlsanity.com/paper/2606.30824/) (2026)

## Abstract (arXiv)

Narrative sensemaking is an essential part of understanding sequential data. Narrative maps are a visual representation model that can assist analysts to understand narratives. In this work, we present a semantic interaction (SI) framework for narrative maps that can support analysts through their sensemaking process. In contrast to traditional SI systems which rely on dimensionality reduction and work on a projection space, our approach has an additional abstraction layer -- the structure space -- that builds upon the projection space and encodes the narrative in a discrete structure. This extra layer introduces additional challenges that must be addressed when integrating SI with the narrative extraction pipeline. We address these challenges by presenting the general concept of Mixed Multi-Model Semantic Interaction (3MSI) -- an SI pipeline, where the highest-level model corresponds to an abstract discrete structure and the lower-level models are continuous. To evaluate the performance of our 3MSI models for narrative maps, we present a quantitative simulation-based evaluation and a qualitative evaluation with case studies and expert feedback. We find that our SI system can model the analysts' intent and support incremental formalism for narrative maps.