# Touch Your Heart: A Tone-aware Chatbot for Customer Care on Social Media

arXiv: https://arxiv.org/abs/1803.02952 · HTML: https://mlsanity.com/paper/1803.02952/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2018.

## Built on by (most cited first, 12 of 12)

- [Multi-Agents are Social Groups: Investigating Social Influence of Multiple Agents in Human-Agent Interactions](https://mlsanity.com/paper/2411.04578/) (2024)
- [CASS: Towards Building a Social-Support Chatbot for Online Health Community](https://mlsanity.com/paper/2101.01583/) (2021)
- [Mitigating Toxic Degeneration with Empathetic Data: Exploring the Relationship Between Toxicity and Empathy](https://mlsanity.com/paper/2205.07233/) (2022)
- [MedGPTEval: A Dataset and Benchmark to Evaluate Responses of Large Language Models in Medicine](https://mlsanity.com/paper/2305.07340/) (2023)
- [Emotion-Aware Transformer Encoder for Empathetic Dialogue Generation](https://mlsanity.com/paper/2204.11320/) (2022)
- [Enhancing Multimodal Emotion Recognition through Multi-Granularity Cross-Modal Alignment](https://mlsanity.com/paper/2412.20821/) (2024)
- [Natural Language Processing in Customer Service: A Systematic Review](https://mlsanity.com/paper/2212.09523/) (2022)
- [Linguistic Elements of Engaging Customer Service Discourse on Social Media](https://mlsanity.com/paper/2212.12801/) (2022)
- [Fine-Grained Emotion Recognition via In-Context Learning](https://mlsanity.com/paper/2510.06600/) (2025)
- [Use of a Taxonomy of Empathetic Response Intents to Control and Interpret Empathy in Neural Chatbots](https://mlsanity.com/paper/2305.10096/) (2023)
- [Improving Emotional Support Delivery in Text-Based Community Safety Reporting Using Large Language Models](https://mlsanity.com/paper/2409.15706/) (2024)
- [Persuasive or Neutral? A Field Experiment on Generative AI in Online Travel Planning](https://mlsanity.com/paper/2509.14259/) (2025)

## Abstract (arXiv)

Chatbot has become an important solution to rapidly increasing customer care demands on social media in recent years. However, current work on chatbot for customer care ignores a key to impact user experience - tones. In this work, we create a novel tone-aware chatbot that generates toned responses to user requests on social media. We first conduct a formative research, in which the effects of tones are studied. Significant and various influences of different tones on user experience are uncovered in the study. With the knowledge of effects of tones, we design a deep learning based chatbot that takes tone information into account. We train our system on over 1.5 million real customer care conversations collected from Twitter. The evaluation reveals that our tone-aware chatbot generates as appropriate responses to user requests as human agents. More importantly, our chatbot is perceived to be even more empathetic than human agents.