Touch Your Heart: A Tone-aware Chatbot for Customer Care on Social Media
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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.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Mar 2018.
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Citing papers per year: 2021: 1; 2022: 4; 2023: 2; 2024: 3; 2025: 2.
12 papers.
- 2024Multi-Agents are Social Groups: Investigating Social Influence of Multiple Agents in Human-Agent Interactionscited by 16
- 2021CASS: Towards Building a Social-Support Chatbot for Online Health Communitycited by 8
- 2022Mitigating Toxic Degeneration with Empathetic Data: Exploring the Relationship Between Toxicity and Empathycited by 8
- 2023MedGPTEval: A Dataset and Benchmark to Evaluate Responses of Large Language Models in Medicinecited by 7
- 2022Emotion-Aware Transformer Encoder for Empathetic Dialogue Generationcited by 5
- 2024Enhancing Multimodal Emotion Recognition through Multi-Granularity Cross-Modal Alignmentcited by 5
- 2022Natural Language Processing in Customer Service: A Systematic Reviewcited by 4
- 2022Linguistic Elements of Engaging Customer Service Discourse on Social Mediacited by 2
- 2025Fine-Grained Emotion Recognition via In-Context Learningcited by 2
- 2023Use of a Taxonomy of Empathetic Response Intents to Control and Interpret Empathy in Neural Chatbotscited by 0
- 2024Improving Emotional Support Delivery in Text-Based Community Safety Reporting Using Large Language Modelscited by 0
- 2025Persuasive or Neutral? A Field Experiment on Generative AI in Online Travel Planningcited by 0
- 2025Fine-Grained Emotion Recognition via In-Context Learning8 Oct 2025
- 2025Persuasive or Neutral? A Field Experiment on Generative AI in Online Travel Planning12 Sep 2025
- 2024Enhancing Multimodal Emotion Recognition through Multi-Granularity Cross-Modal Alignment30 Dec 2024
- 2024Multi-Agents are Social Groups: Investigating Social Influence of Multiple Agents in Human-Agent Interactions7 Nov 2024
- 2024Improving Emotional Support Delivery in Text-Based Community Safety Reporting Using Large Language Models24 Sep 2024
- 2023Use of a Taxonomy of Empathetic Response Intents to Control and Interpret Empathy in Neural Chatbots17 May 2023
- 2023MedGPTEval: A Dataset and Benchmark to Evaluate Responses of Large Language Models in Medicine12 May 2023
- 2022Linguistic Elements of Engaging Customer Service Discourse on Social Media24 Dec 2022
- 2022Natural Language Processing in Customer Service: A Systematic Review16 Dec 2022
- 2022Mitigating Toxic Degeneration with Empathetic Data: Exploring the Relationship Between Toxicity and Empathy15 May 2022
- 2022Emotion-Aware Transformer Encoder for Empathetic Dialogue Generation24 Apr 2022
- 2021CASS: Towards Building a Social-Support Chatbot for Online Health Community4 Jan 2021