# Comparing Native and Non-native English Speakers' Behaviors in Collaborative Writing through Visual Analytics

arXiv: https://arxiv.org/abs/2502.18681 · HTML: https://mlsanity.com/paper/2502.18681/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 116 references that are in the map.
- First posted on arXiv 25 Feb 2025.

## Builds on (most cited first, 26 of 26)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities](https://mlsanity.com/paper/2201.06796/) (2022)
- [A Design Space for Intelligent and Interactive Writing Assistants](https://mlsanity.com/paper/2403.14117/) (2024)
- [LLM-in-the-loop: Leveraging Large Language Model for Thematic Analysis](https://mlsanity.com/paper/2310.15100/) (2023)
- [Beyond Text Generation: Supporting Writers with Continuous Automatic Text Summaries](https://mlsanity.com/paper/2208.09323/) (2022)
- [VISAR: A Human-AI Argumentative Writing Assistant with Visual Programming and Rapid Draft Prototyping](https://mlsanity.com/paper/2304.07810/) (2023)
- [Lost in Translation: Loss and Decay of Linguistic Richness in Machine Translation](https://mlsanity.com/paper/1906.12068/) (2019)
- [Choice Over Control: How Users Write with Large Language Models using Diegetic and Non-Diegetic Prompting](https://mlsanity.com/paper/2303.03199/) (2023)
- [Beyond the Chat: Executable and Verifiable Text-Editing with LLMs](https://mlsanity.com/paper/2309.15337/) (2023)
- [The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations](https://mlsanity.com/paper/2212.11737/) (2022)
- [ABScribe: Rapid Exploration & Organization of Multiple Writing Variations in Human-AI Co-Writing Tasks using Large Language Models](https://mlsanity.com/paper/2310.00117/) (2023)
- [ReviewFlow: Intelligent Scaffolding to Support Academic Peer Reviewing](https://mlsanity.com/paper/2402.03530/) (2024)
- [GenNI: Human-AI Collaboration for Data-Backed Text Generation](https://mlsanity.com/paper/2110.10185/) (2021)
- [Survey on Visual Analysis of Event Sequence Data](https://mlsanity.com/paper/2006.14291/) (2020)
- [Multilinguals and Wikipedia Editing](https://mlsanity.com/paper/1312.0976/) (2013)
- [Characterizing Stage-Aware Writing Assistance in Collaborative Document Authoring](https://mlsanity.com/paper/2008.08165/) (2020)
- [Visual Analysis of High-Dimensional Event Sequence Data via Dynamic Hierarchical Aggregation](https://mlsanity.com/paper/1906.07617/) (2019)
- [Sequen-C: A Multilevel Overview of Temporal Event Sequences](https://mlsanity.com/paper/2108.03043/) (2021)
- [(Dis)placed Contributions: Uncovering Hidden Hurdles to Collaborative Writing Involving Non-Native Speakers, Native Speakers, and AI-Powered Editing Tools](https://mlsanity.com/paper/2405.05474/) (2024)
- [WordDecipher: Enhancing Digital Workspace Communication with Explainable AI for Non-native English Speakers](https://mlsanity.com/paper/2404.07005/) (2024)
- [Chronodes: Interactive Multi-focus Exploration of Event Sequences](https://mlsanity.com/paper/1609.08535/) (2016)
- [Organic Visualization of Document Evolution](https://mlsanity.com/paper/1712.06179/) (2017)
- [Towards Immersive Collaborative Sensemaking](https://mlsanity.com/paper/2210.07784/) (2022)
- [A Comparative Evaluation of Visual Summarization Techniques for Event Sequences](https://mlsanity.com/paper/2306.02489/) (2023)
- [A Multi-Level Task Framework for Event Sequence Analysis](https://mlsanity.com/paper/2408.04752/) (2024)

## Built on by (most cited first, 1 of 1)

- [Collaborative Document Editing with Multiple Users and AI Agents](https://mlsanity.com/paper/2509.11826/) (2025)

## Abstract (arXiv)

Understanding collaborative writing dynamics between native speakers (NS) and non-native speakers (NNS) is critical for enhancing collaboration quality and team inclusivity. In this paper, we partnered with communication researchers to develop visual analytics solutions for comparing NS and NNS behaviors in 162 writing sessions across 27 teams. The primary challenges in analyzing writing behaviors are data complexity and the uncertainties introduced by automated methods. In response, we present textscCOALA, a novel visual analytics tool that improves model interpretability by displaying uncertainties in author clusters, generating behavior summaries using large language models, and visualizing writing-related actions at multiple granularities. We validated the effectiveness of textscCOALA through user studies with domain experts (N=2+2) and researchers with relevant experience (N=8). We present the insights discovered by participants using textscCOALA, suggest features for future AI-assisted collaborative writing tools, and discuss the broader implications for analyzing collaborative processes beyond writing.