# Detecting Bot-Generated Text by Characterizing Linguistic Accommodation in Human-Bot Interactions

arXiv: https://arxiv.org/abs/2106.01170 · HTML: https://mlsanity.com/paper/2106.01170/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 54 references that are in the map.
- First posted on arXiv 2 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset](https://mlsanity.com/paper/1710.03957/) (2017)
- [Automatic Detection of Generated Text is Easiest when Humans are Fooled](https://mlsanity.com/paper/1911.00650/) (2019)
- [The Second Conversational Intelligence Challenge (ConvAI2)](https://mlsanity.com/paper/1902.00098/) (2019)
- [Automatic Detection of Machine Generated Text: A Critical Survey](https://mlsanity.com/paper/2011.01314/) (2020)
- [A Qualitative Evaluation of Language Models on Automatic Question-Answering for COVID-19](https://mlsanity.com/paper/2006.10964/) (2020)
- [Chatbots language design: the influence of language variation on user experience](https://mlsanity.com/paper/2101.11089/) (2021)

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

- [The Science of Detecting LLM-Generated Texts](https://mlsanity.com/paper/2303.07205/) (2023)
- [Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods](https://mlsanity.com/paper/2210.07321/) (2022)
- [Mutation-Based Adversarial Attacks on Neural Text Detectors](https://mlsanity.com/paper/2302.05794/) (2023)
- [Do language models accommodate their users? A study of linguistic convergence](https://mlsanity.com/paper/2508.03276/) (2025)
- [MUGC: Machine Generated versus User Generated Content Detection](https://mlsanity.com/paper/2403.19725/) (2024)
- [Short-PHD: Detecting Short LLM-generated Text with Topological Data Analysis After Off-topic Content Insertion](https://mlsanity.com/paper/2504.02873/) (2025)
- [Accommodation Goes Both Ways: Studying Linguistic Convergence Between Humans and Language Models](https://mlsanity.com/paper/2605.29278/) (2026)
- [Spot the bot: Coarse-Grained Partition of Semantic Paths for Bots and Humans](https://mlsanity.com/paper/2402.17392/) (2024)

## Abstract (arXiv)

Language generation models' democratization benefits many domains, from answering health-related questions to enhancing education by providing AI-driven tutoring services. However, language generation models' democratization also makes it easier to generate human-like text at-scale for nefarious activities, from spreading misinformation to targeting specific groups with hate speech. Thus, it is essential to understand how people interact with bots and develop methods to detect bot-generated text. This paper shows that bot-generated text detection methods are more robust across datasets and models if we use information about how people respond to it rather than using the bot's text directly. We also analyze linguistic alignment, providing insight into differences between human-human and human-bot conversations.